upd

app_inference.py

@@ -6,6 +6,7 @@ from runners.inference_server import InferencerServer
 from runners.evaluate_uncertainty_guide import EvaluateUncertaintyGuide
 from runners.evaluate_pbnbv import EvaluatePBNBV
 from runners.evaluate_heuristic import Heuristic
+from runners.pb_global_points_inferencer import PBGlobalPointsInferencer
 
 @PytorchBootApplication("global_points_inference")
 class GlobalPointsInferenceApp:
@@ -109,3 +110,9 @@ class EvaluateHeuristicApp:
     @staticmethod
     def start():
         Heuristic("./configs/local/heuristic_evaluation.yaml").run()
+
+@PytorchBootApplication("pb_global_points_inference")
+class PBGlobalPointsInferenceApp:
+    @staticmethod
+    def start():
+        PBGlobalPointsInferencer("./configs/local/global_only_inference_config.yaml").run()

configs/local/global_only_inference_config.yaml

@@ -15,7 +15,7 @@ runner:
       - OmniObject3d_test
 
   blender_script_path: "/media/hofee/data/project/python/nbv_reconstruction/blender/data_renderer.py"
-  output_dir: "/media/hofee/data/project/exp/old_no_cluster_ab_global_pts_and_local_pose"
+  output_dir: "/media/hofee/data/project/exp/new_ab_pb_global_pts_and_local_pose"
   pipeline: nbv_reconstruction_pipeline_global
   voxel_size: 0.003
   min_new_area: 1.0
@@ -23,8 +23,8 @@ runner:
   enable_cluster: False
 dataset:
   OmniObject3d_test:
-    root_dir: "/media/hofee/data/data/new_testset_output"
-    model_dir: "/media/hofee/data/data/scaled_object_meshes"
+    root_dir: "/media/hofee/repository/final_test_set/preprocessed_dataset"
+    model_dir: "/media/hofee/data/data/target/target_formulated_view"
     source: seq_reconstruction_dataset_preprocessed
     type: test
     filter_degree: 75

configs/server/server_heuristic_inference_server_config.yaml

@@ -0,0 +1,43 @@
+
+runner:
+  general:
+    seed: 0
+    device: cuda
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    
+  experiment:
+    name: train_ab_global_only
+    root_dir: "experiments"
+    epoch: -1 # -1 stands for last epoch
+
+  pipeline: nbv_reconstruction_pipeline
+  voxel_size: 0.003
+  enable_cluster: False
+  heuristic_method: hemisphere_random
+
+pipeline:
+  nbv_reconstruction_pipeline:
+    modules:
+      pts_encoder: pointnet_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+
+heuristic_methods:
+  hemisphere_random:
+    center: [0, 0, 0]
+    radius_fixed: True
+    fixed_radius: 0.6
+    min_radius: 0.4
+    max_radius: 0.8
+
+  hemisphere_circle_trajectory:
+    center: [0, 0, 0]
+    radius_fixed: False
+    fixed_radius: 0.6
+    min_radius: 0.4
+    max_radius: 0.8
+    phi_list: [15, 45, 75]
+    circle_times: 12

core/seq_dataset_preprocessed.py

@@ -21,7 +21,7 @@ class SeqReconstructionDatasetPreprocessed(BaseDataset):
         super(SeqReconstructionDatasetPreprocessed, self).__init__(config)
         self.config = config
         self.root_dir = config["root_dir"]
-        self.real_root_dir = r"/media/hofee/data/data/new_testset"
+        self.real_root_dir = r"/media/hofee/repository/final_test_set/view"
         self.item_list = os.listdir(self.root_dir)
 
     def __getitem__(self, index):

runners/evaluate_pbnbv.py

@@ -24,18 +24,20 @@ class VoxelStruct:
         self.empty_voxels = []
         self.unknown_voxels = []
         self.frontier_voxels = []
+        self.curr_camera_pose = None
         self.bbx_min = None
         self.bbx_max = None
         self.voxel_types: Dict[Tuple[float, float, float], VoxelType] = {}
         
     def update_voxel_map(self, points: np.ndarray, 
                         camera_pose: np.ndarray) -> Tuple[List[np.ndarray], List[np.ndarray]]:
-        points = self.transform_points(points, camera_pose)
+        #points = self.transform_points(points, camera_pose)
         new_occupied = self.voxelize_points(points)
         self.occupied_voxels.extend(new_occupied)
         self.update_bounding_box()
         self.ray_tracing(camera_pose[:3, 3], camera_pose[:3, :3])
         self.update_frontier_voxels()
+        self.curr_camera_pose = camera_pose
         return self.frontier_voxels, self.occupied_voxels
     
     def ray_tracing(self, camera_position: np.ndarray, camera_rotation: np.ndarray):
@@ -91,14 +93,10 @@ class VoxelStruct:
             hemisphere_radius = self.camera_working_distance + bbx_diagonal / 2
         else:
             hemisphere_radius = self.camera_working_distance
+        theta_step = np.pi / (12 * self.num_parallels)  
+        phi_step = np.pi / (6 * self.viewpoints_per_parallel)  
 
-        # 使用更密集的采样
-        theta_step = np.pi / (6 * self.num_parallels)  # 减小theta的步长
-        phi_step = np.pi / (6 * self.viewpoints_per_parallel)  # 减小phi的步长
-
-        # 从顶部到底部采样
         for theta in np.arange(0, np.pi/6 + theta_step, theta_step):
-            # 在每个纬度上采样
             for phi in np.arange(0, 2*np.pi, phi_step):
                 x = hemisphere_radius * np.sin(theta) * np.cos(phi)
                 y = hemisphere_radius * np.sin(theta) * np.sin(phi)
@@ -273,7 +271,6 @@ class PBNBV:
         
     def capture(self, point_cloud: np.ndarray, camera_pose: np.ndarray):
         frontier_voxels, occupied_voxels = self.voxel_struct.update_voxel_map(point_cloud, camera_pose)
-        # self.voxel_struct.visualize_voxel_struct(camera_pose)
         self.fit_ellipsoids(frontier_voxels, occupied_voxels)
     
     def reset(self):
@@ -382,8 +379,8 @@ class PBNBV:
             return []
             
         center = (self.voxel_struct.bbx_min + self.voxel_struct.bbx_max) / 2
-        radius = np.linalg.norm(self.voxel_struct.bbx_max - self.voxel_struct.bbx_min) / 2 + self.focal_length
-        
+        #import ipdb; ipdb.set_trace()
+        radius = np.linalg.norm(self.voxel_struct.bbx_max - self.voxel_struct.bbx_min) / 2 + 0.3
         candidate_views = []
         
         latitudes = np.linspace(np.deg2rad(40), np.deg2rad(90), longitude_num)
@@ -432,15 +429,15 @@ class PBNBV:
         scores = [self.evaluate_viewpoint(view) for view in candidate_views]
         best_idx = np.argmax(scores)
         
-        return candidate_views[best_idx]
+        return candidate_views[best_idx],candidate_views
     
     def execute(self) -> Tuple[np.ndarray, bool]:
-        best_view = self.select_best_view()
+        best_view,candidate_views = self.select_best_view()
         
         has_frontier = any(e["type"] == "frontier" for e in self.ellipsoids)
         done = not has_frontier
         
-        return best_view, done
+        return best_view, candidate_views, done
     
 import os
 import json
@@ -478,8 +475,8 @@ class EvaluatePBNBV(Runner):
         CM = 0.01
         self.min_new_pts_num = self.min_new_area * (CM / self.voxel_size) ** 2
         self.overlap_limit = ConfigManager.get(namespace.Stereotype.RUNNER, "overlap_limit")
+        self.pbnbv = PBNBV()
 
-        self.pbnbv = PBNBV(self.voxel_size)
         ''' Experiment '''
         self.load_experiment("nbv_evaluator")
         self.stat_result_path = os.path.join(self.output_dir, "stat.json")
@@ -541,11 +538,49 @@ class EvaluatePBNBV(Runner):
                 
         status_manager.set_progress("inference", "inferencer", f"dataset", len(self.test_set_list), len(self.test_set_list))
 
-    def get_output_data(self):
-        pose_matrix, done = self.pbnbv.execute()
+    def visualize(self, best_view, candidate_views):
+        import plotly.graph_objects as go
+        fig = go.Figure()
+        colors = ['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance',
+             'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg']
+        
+        color = colors[0]
+        candidate_pose = best_view
+        origin_candidate = candidate_pose[:3, 3]
+        z_axis_candidate = candidate_pose[:3, 2]
+        fig.add_trace(go.Cone(
+            x=[origin_candidate[0]], y=[origin_candidate[1]], z=[origin_candidate[2]],
+            u=[z_axis_candidate[0]], v=[z_axis_candidate[1]], w=[z_axis_candidate[2]],
+            colorscale=color,
+            sizemode="absolute", sizeref=0.1, anchor="tail", showscale=False
+        ))
+        for candidate_pose in candidate_views:
+            origin_candidate = candidate_pose[:3, 3]
+            z_axis_candidate = candidate_pose[:3, 2]
+            color = colors[1]
+            fig.add_trace(go.Cone(
+                x=[origin_candidate[0]], y=[origin_candidate[1]], z=[origin_candidate[2]],
+                u=[z_axis_candidate[0]], v=[z_axis_candidate[1]], w=[z_axis_candidate[2]],
+                colorscale=color,
+                sizemode="absolute", sizeref=0.05, anchor="tail", showscale=False
+            ))
+
+        fig.update_layout(
+            title="Clustered Poses and Input Points",
+            scene=dict(
+                xaxis_title='X',
+                yaxis_title='Y',
+                zaxis_title='Z'
+            ),
+            margin=dict(l=0, r=0, b=0, t=40),
+            scene_camera=dict(eye=dict(x=1.25, y=1.25, z=1.25))
+        )
+        
+        fig.show()
+
+    def get_output_data(self):
+        pose_matrix,candidate_views, done = self.pbnbv.execute()
         
-        offset = np.asarray([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])
-        pose_matrix = pose_matrix @ offset
         rot = pose_matrix[:3,:3]
         
         pose_6d = PoseUtil.matrix_to_rotation_6d_numpy(rot)
@@ -554,7 +589,9 @@ class EvaluatePBNBV(Runner):
         pose_9d = np.concatenate([pose_6d, translation], axis=0).reshape(1,9)
         pose_9d = pose_9d.repeat(50, axis=0)
         #import ipdb; ipdb.set_trace()
-        return {"pred_pose_9d": pose_9d}
+        #self.visualize(pose_matrix,candidate_views)
+        
+        return {"pred_pose_9d": pose_9d, "candidate_views": candidate_views}
         
     def predict_sequence(self, data, cr_increase_threshold=0, overlap_area_threshold=25, scan_points_threshold=10, max_iter=50, max_retry = 10, max_success=3):
         scene_name = data["scene_name"]
@@ -611,8 +648,8 @@ class EvaluatePBNBV(Runner):
             
             predict_result = PredictResult(pred_pose_9d, input_pts=input_data["combined_scanned_pts"], cluster_params=dict(eps=0.25, min_samples=3))
             # -----------------------
-            import ipdb; ipdb.set_trace()
-            predict_result.visualize()
+            #import ipdb; ipdb.set_trace()
+            #predict_result.visualize()
             # -----------------------
             pred_pose_9d_candidates = predict_result.candidate_9d_poses
             #import ipdb; ipdb.set_trace()

runners/inference_heuristic_server.py

@@ -0,0 +1,216 @@
+import os
+import json
+import torch
+import numpy as np
+from flask import Flask, request, jsonify
+
+from PytorchBoot.config import ConfigManager
+import PytorchBoot.namespace as namespace
+import PytorchBoot.stereotype as stereotype
+from PytorchBoot.factory import ComponentFactory
+
+from PytorchBoot.runners.runner import Runner
+from PytorchBoot.utils import Log
+
+from utils.pts import PtsUtil
+from beans.predict_result import PredictResult
+
+@stereotype.runner("heuristic_inferencer_server")
+class HeuristicInferencerServer(Runner):
+    def __init__(self, config_path):
+        super().__init__(config_path)
+        self.heuristic_method = ConfigManager.get(namespace.Stereotype.RUNNER, "heuristic_method")
+        self.heuristic_method_config = ConfigManager.get("heuristic_methods", self.heuristic_method)
+        ''' Web Server '''
+        self.app = Flask(__name__)
+        ''' Pipeline '''
+        self.pipeline_name = self.config[namespace.Stereotype.PIPELINE]
+        self.pipeline:torch.nn.Module = ComponentFactory.create(namespace.Stereotype.PIPELINE, self.pipeline_name)
+        self.pipeline = self.pipeline.to(self.device)
+        self.pts_num = 8192
+        self.voxel_size = 0.002
+            
+        ''' Experiment '''
+        self.load_experiment("inferencer_server")
+
+    def generate_hemisphere_random_sequence(self, max_iter, config):
+        """Generate a random hemisphere sampling sequence"""
+        radius_fixed = config["radius_fixed"]
+        fixed_radius = config["fixed_radius"]
+        min_radius = config["min_radius"]
+        max_radius = config["max_radius"]
+        poses = []
+        center = np.array(config["center"])
+        
+        for _ in range(max_iter):
+            # 随机采样方向
+            direction = np.random.randn(3)
+            direction[2] = abs(direction[2])  # 确保在上半球
+            direction = direction / np.linalg.norm(direction)
+            
+            # 确定半径
+            if radius_fixed:
+                radius = fixed_radius
+            else:
+                radius = np.random.uniform(min_radius, max_radius)
+                
+            # 计算位置和朝向
+            position = center + direction * radius
+            z_axis = -direction
+            y_axis = np.array([0, 0, 1])
+            x_axis = np.cross(y_axis, z_axis)
+            x_axis = x_axis / np.linalg.norm(x_axis)
+            y_axis = np.cross(z_axis, x_axis)
+            
+            pose = np.eye(4)
+            pose[:3,:3] = np.stack([x_axis, y_axis, z_axis], axis=1)
+            pose[:3,3] = position
+            poses.append(pose)
+            
+        return poses
+
+    def generate_hemisphere_circle_sequence(self, config):
+        """Generate a circular trajectory sampling sequence"""
+        radius_fixed = config["radius_fixed"]
+        fixed_radius = config["fixed_radius"]
+        min_radius = config["min_radius"]
+        max_radius = config["max_radius"]
+        phi_list = config["phi_list"]
+        circle_times = config["circle_times"]
+        
+        poses = []
+        center = np.array(config["center"])
+        
+        for phi in phi_list:  # 仰角
+            phi_rad = np.deg2rad(phi)
+            for i in range(circle_times):  # 方位角
+                theta = i * (2 * np.pi / circle_times)
+                
+                # 确定半径
+                if radius_fixed:
+                    radius = fixed_radius
+                else:
+                    radius = np.random.uniform(min_radius, max_radius)
+                    
+                # 球坐标转笛卡尔坐标
+                x = radius * np.cos(theta) * np.sin(phi_rad)
+                y = radius * np.sin(theta) * np.sin(phi_rad)
+                z = radius * np.cos(phi_rad)
+                position = center + np.array([x, y, z])
+                
+                # 计算朝向
+                direction = (center - position) / np.linalg.norm(center - position)
+                z_axis = direction
+                y_axis = np.array([0, 0, 1])
+                x_axis = np.cross(y_axis, z_axis)
+                x_axis = x_axis / np.linalg.norm(x_axis)
+                y_axis = np.cross(z_axis, x_axis)
+                
+                pose = np.eye(4)
+                pose[:3,:3] = np.stack([x_axis, y_axis, z_axis], axis=1)
+                pose[:3,3] = position
+                poses.append(pose)
+                
+        return poses
+
+
+    def generate_seq(self, max_iter=50):
+        if self.heuristic_method == "hemisphere_random":
+            pose_sequence = self.generate_hemisphere_random_sequence(
+                max_iter,
+                self.heuristic_method_config
+            )
+        elif self.heuristic_method == "hemisphere_circle_trajectory":
+            pose_sequence = self.generate_hemisphere_circle_sequence(
+                self.heuristic_method_config
+            )
+        else:
+            raise ValueError(f"Unknown heuristic method: {self.heuristic_method}")
+        return pose_sequence
+    
+    def get_input_data(self, data):
+        input_data = {}
+        scanned_pts = data["scanned_pts"]
+        scanned_n_to_world_pose_9d = data["scanned_n_to_world_pose_9d"]
+        combined_scanned_views_pts = np.concatenate(scanned_pts, axis=0)
+        voxel_downsampled_combined_scanned_pts = PtsUtil.voxel_downsample_point_cloud(
+            combined_scanned_views_pts, self.voxel_size
+        )
+        fps_downsampled_combined_scanned_pts, fps_idx = PtsUtil.fps_downsample_point_cloud(
+            voxel_downsampled_combined_scanned_pts, self.pts_num, require_idx=True
+        )
+
+        input_data["scanned_pts"] = scanned_pts
+        input_data["scanned_n_to_world_pose_9d"] = np.asarray(scanned_n_to_world_pose_9d, dtype=np.float32)
+        input_data["combined_scanned_pts"] = np.asarray(fps_downsampled_combined_scanned_pts, dtype=np.float32)
+        return input_data
+    
+    def get_result(self, output_data):
+
+        pred_pose_9d = output_data["pred_pose_9d"]
+        pred_pose_9d = np.asarray(PredictResult(pred_pose_9d.cpu().numpy(), None, cluster_params=dict(eps=0.25, min_samples=3)).candidate_9d_poses, dtype=np.float32)
+        result = {
+            "pred_pose_9d": pred_pose_9d.tolist()
+        }
+        return result
+    
+    def collate_input(self, input_data):
+        collated_input_data = {}
+        collated_input_data["scanned_pts"] = [torch.tensor(input_data["scanned_pts"], dtype=torch.float32, device=self.device)]
+        collated_input_data["scanned_n_to_world_pose_9d"] = [torch.tensor(input_data["scanned_n_to_world_pose_9d"], dtype=torch.float32, device=self.device)]
+        collated_input_data["combined_scanned_pts"] = torch.tensor(input_data["combined_scanned_pts"], dtype=torch.float32, device=self.device).unsqueeze(0)
+        return collated_input_data
+    
+    def do_inference(self, input_data):
+        scanned_pts = input_data["scanned_pts"]
+    
+    def run(self):
+        Log.info("Loading from epoch {}.".format(self.current_epoch))
+    
+        @self.app.route("/inference", methods=["POST"])
+        def inference():
+            data = request.json
+            input_data = self.get_input_data(data)
+            collated_input_data = self.collate_input(input_data)
+            output_data = self.do_inference(collated_input_data)
+            result = self.get_result(output_data)
+            return jsonify(result)
+        
+
+        self.app.run(host="0.0.0.0", port=5000)
+
+    def get_checkpoint_path(self, is_last=False):
+        return os.path.join(self.experiment_path, namespace.Direcotry.CHECKPOINT_DIR_NAME,
+                            "Epoch_{}.pth".format(
+                                self.current_epoch if self.current_epoch != -1 and not is_last else "last"))
+
+    def load_checkpoint(self, is_last=False):
+        self.load(self.get_checkpoint_path(is_last))
+        Log.success(f"Loaded checkpoint from {self.get_checkpoint_path(is_last)}")
+        if is_last:
+            checkpoint_root = os.path.join(self.experiment_path, namespace.Direcotry.CHECKPOINT_DIR_NAME)
+            meta_path = os.path.join(checkpoint_root, "meta.json")
+            if not os.path.exists(meta_path):
+                raise FileNotFoundError(
+                    "No checkpoint meta.json file in the experiment {}".format(self.experiments_config["name"]))
+            file_path = os.path.join(checkpoint_root, "meta.json")
+            with open(file_path, "r") as f:
+                meta = json.load(f)
+            self.current_epoch = meta["last_epoch"]
+            self.current_iter = meta["last_iter"]
+
+    def load_experiment(self, backup_name=None):
+        super().load_experiment(backup_name)
+        self.current_epoch = self.experiments_config["epoch"]
+        self.load_checkpoint(is_last=(self.current_epoch == -1))
+
+    def create_experiment(self, backup_name=None):
+        super().create_experiment(backup_name)
+       
+        
+    def load(self, path):
+        state_dict = torch.load(path)
+        self.pipeline.load_state_dict(state_dict)
+
+
+

runners/inference_server.py

@@ -64,6 +64,9 @@ class InferencerServer(Runner):
         collated_input_data["combined_scanned_pts"] = torch.tensor(input_data["combined_scanned_pts"], dtype=torch.float32, device=self.device).unsqueeze(0)
         return collated_input_data
     
+    def do_inference(self, input_data):
+        return self.pipeline.forward_test(input_data)
+    
     def run(self):
         Log.info("Loading from epoch {}.".format(self.current_epoch))
     
@@ -72,7 +75,7 @@ class InferencerServer(Runner):
             data = request.json
             input_data = self.get_input_data(data)
             collated_input_data = self.collate_input(input_data)
-            output_data = self.pipeline.forward_test(collated_input_data)
+            output_data = self.do_inference(collated_input_data)
             result = self.get_result(output_data)
             return jsonify(result)
         

runners/pb_global_points_inferencer.py

@@ -0,0 +1,829 @@
+import os
+import json
+from utils.render import RenderUtil
+from utils.pose import PoseUtil
+from utils.pts import PtsUtil
+from utils.reconstruction import ReconstructionUtil
+from beans.predict_result import PredictResult
+
+import torch
+from tqdm import tqdm
+import numpy as np
+import pickle
+
+from PytorchBoot.config import ConfigManager
+import PytorchBoot.namespace as namespace
+import PytorchBoot.stereotype as stereotype
+from PytorchBoot.factory import ComponentFactory
+
+from PytorchBoot.dataset import BaseDataset
+from PytorchBoot.runners.runner import Runner
+from PytorchBoot.utils import Log
+from PytorchBoot.status import status_manager
+from utils.data_load import DataLoadUtil
+
+from sklearn.mixture import GaussianMixture
+from typing import List, Tuple, Dict
+from enum import Enum
+
+class VoxelType(Enum):
+    NONE = 0
+    OCCUPIED = 1
+    EMPTY = 2
+    UNKNOWN = 3
+    FRONTIER = 4
+
+class VoxelStruct:
+    def __init__(self, voxel_resolution=0.01, ray_trace_step=0.01, surrounding_radius=1, 
+                 num_parallels=10, viewpoints_per_parallel=10, camera_working_distance=0.5):
+        self.voxel_resolution = voxel_resolution 
+        self.ray_trace_step = ray_trace_step
+        self.surrounding_radius = surrounding_radius
+        self.num_parallels = num_parallels
+        self.viewpoints_per_parallel = viewpoints_per_parallel
+        self.camera_working_distance = camera_working_distance
+        self.occupied_voxels = []
+        self.empty_voxels = []
+        self.unknown_voxels = []
+        self.frontier_voxels = []
+        self.curr_camera_pose = None
+        self.bbx_min = None
+        self.bbx_max = None
+        self.voxel_types: Dict[Tuple[float, float, float], VoxelType] = {}
+        
+    def update_voxel_map(self, points: np.ndarray, 
+                        camera_pose: np.ndarray) -> Tuple[List[np.ndarray], List[np.ndarray]]:
+        #points = self.transform_points(points, camera_pose)
+        new_occupied = self.voxelize_points(points)
+        self.occupied_voxels.extend(new_occupied)
+        self.update_bounding_box()
+        self.ray_tracing(camera_pose[:3, 3], camera_pose[:3, :3])
+        self.update_frontier_voxels()
+        self.curr_camera_pose = camera_pose
+        return self.frontier_voxels, self.occupied_voxels
+    
+    def ray_tracing(self, camera_position: np.ndarray, camera_rotation: np.ndarray):
+        if self.bbx_min is None or self.bbx_max is None:
+            return
+            
+        directions = self.generate_ray_directions()   
+        for direction in directions:
+            direction_cam = camera_rotation @ direction
+            current_pos = camera_position.copy()
+
+            cnt = 0
+            while not self.is_in_bounding_box(current_pos):
+                current_pos -= direction_cam * self.ray_trace_step*2
+                cnt += 1
+                if cnt > 200:
+                    break
+
+            occupied_flag = False
+            maybe_unknown_voxels = []
+            while self.is_in_bounding_box(current_pos):
+                voxel = self.get_voxel_coordinate(current_pos)
+                voxel_key = tuple(voxel)
+                
+                if self.is_occupied(voxel):
+                    current_pos -= direction_cam * self.ray_trace_step
+                    occupied_flag = True
+                    continue
+                if not occupied_flag:
+                    if voxel_key not in self.voxel_types or self.voxel_types[voxel_key] == VoxelType.NONE or self.voxel_types[voxel_key] == VoxelType.UNKNOWN:
+                        maybe_unknown_voxels.append(voxel)
+                else:
+                    if voxel_key not in self.voxel_types or self.voxel_types[voxel_key] == VoxelType.NONE:
+                        self.voxel_types[voxel_key] = VoxelType.UNKNOWN
+                        self.unknown_voxels.append(voxel)
+                current_pos -= direction_cam * self.ray_trace_step
+            if not occupied_flag:
+                for voxel in maybe_unknown_voxels:
+                    self.voxel_types[tuple(voxel)] = VoxelType.UNKNOWN
+                    self.unknown_voxels.append(voxel)
+            else:
+                for voxel in maybe_unknown_voxels:
+                    voxel_key = tuple(voxel)
+                    if voxel_key in self.voxel_types and self.voxel_types[voxel_key] == VoxelType.UNKNOWN:
+                        self.unknown_voxels = [v for v in self.unknown_voxels if not np.array_equal(v, voxel)]
+                    self.voxel_types[voxel_key] = VoxelType.EMPTY
+                    self.empty_voxels.append(voxel)
+    
+    def generate_ray_directions(self):
+        directions = []
+        if self.bbx_min is not None and self.bbx_max is not None:
+            bbx_diagonal = np.linalg.norm(self.bbx_max - self.bbx_min)
+            hemisphere_radius = self.camera_working_distance + bbx_diagonal / 2
+        else:
+            hemisphere_radius = self.camera_working_distance
+        theta_step = np.pi / (6 * self.num_parallels)  
+        phi_step = np.pi / (6 * self.viewpoints_per_parallel)  
+
+        for theta in np.arange(0, np.pi/6 + theta_step, theta_step):
+            for phi in np.arange(0, 2*np.pi, phi_step):
+                x = hemisphere_radius * np.sin(theta) * np.cos(phi)
+                y = hemisphere_radius * np.sin(theta) * np.sin(phi)
+                z = hemisphere_radius * np.cos(theta)
+                direction = np.array([-x, -y, -z])
+                direction = direction / np.linalg.norm(direction)
+                directions.append(direction)
+
+        return directions
+    
+    def update_frontier_voxels(self):
+        self.frontier_voxels = []
+        remaining_unknown = []
+        
+        for voxel in self.unknown_voxels:
+            neighbors = self.find_neighbors(voxel)
+            has_empty = any(self.voxel_types.get(tuple(n), VoxelType.NONE) == VoxelType.EMPTY for n in neighbors)
+            has_occupied = any(self.voxel_types.get(tuple(n), VoxelType.NONE) == VoxelType.OCCUPIED for n in neighbors)
+            
+            if has_empty and has_occupied:
+                self.voxel_types[tuple(voxel)] = VoxelType.FRONTIER
+                self.frontier_voxels.append(voxel)
+            else:
+                remaining_unknown.append(voxel)
+        self.unknown_voxels = remaining_unknown
+    
+    def is_in_bounding_box(self, point: np.ndarray) -> bool:
+        if self.bbx_min is None or self.bbx_max is None:
+            return False
+        return np.all(point >= self.bbx_min) and np.all(point <= self.bbx_max)
+    
+    def get_voxel_coordinate(self, point: np.ndarray) -> np.ndarray:
+        return (point / self.voxel_resolution).astype(int) * self.voxel_resolution
+    
+    def voxelize_points(self, points: np.ndarray) -> List[np.ndarray]:
+        voxel_coords = (points / self.voxel_resolution).astype(int)
+        unique_voxels = np.unique(voxel_coords, axis=0)
+        voxels = [voxel * self.voxel_resolution for voxel in unique_voxels]
+        for voxel in voxels:
+            self.voxel_types[tuple(voxel)] = VoxelType.OCCUPIED
+        return voxels
+    
+    def is_occupied(self, voxel: np.ndarray) -> bool:
+        return self.voxel_types.get(tuple(voxel), VoxelType.NONE) == VoxelType.OCCUPIED
+    
+    def find_neighbors(self, voxel: np.ndarray) -> List[np.ndarray]:
+        neighbors = []
+        for dx in [-1, 0, 1]:
+            for dy in [-1, 0, 1]:
+                for dz in [-1, 0, 1]:
+                    if dx == 0 and dy == 0 and dz == 0:
+                        continue
+                    neighbor = voxel + np.array([dx, dy, dz]) * self.voxel_resolution
+                    neighbors.append(neighbor)
+        return neighbors
+    
+    def update_bounding_box(self):
+        if not self.occupied_voxels:
+            return
+            
+        occupied_array = np.array(self.occupied_voxels)
+        self.bbx_min = occupied_array.min(axis=0) - 2 * self.voxel_resolution
+        self.bbx_max = occupied_array.max(axis=0) + 2 * self.voxel_resolution
+    
+    def transform_points(self, points: np.ndarray, transform: np.ndarray) -> np.ndarray:
+        ones = np.ones((points.shape[0], 1))
+        points_homo = np.hstack((points, ones))
+        transformed = (transform @ points_homo.T).T
+        return transformed[:, :3]
+    
+    def create_voxel_geometry(self,voxels, color, voxel_size):
+        import open3d as o3d
+        points = np.array(voxels)
+        if len(points) == 0:
+            return None
+        
+        pcd = o3d.geometry.PointCloud()
+        pcd.points = o3d.utility.Vector3dVector(points)
+        pcd.colors = o3d.utility.Vector3dVector(np.tile(color, (len(points), 1)))
+        return pcd
+
+    def create_ray_geometry(self,camera_pos, directions, camera_rot, length=1.0):
+        import open3d as o3d
+        lines = []
+        colors = []
+        for direction in directions:
+            # 将方向向量转换到相机坐标系
+            direction_cam = camera_rot @ direction
+            end_point = camera_pos - direction_cam * length
+            lines.append([camera_pos, end_point])
+            colors.append([0.5, 0.5, 0.5])  # 灰色光线
+        
+        line_set = o3d.geometry.LineSet()
+        line_set.points = o3d.utility.Vector3dVector(np.array(lines).reshape(-1, 3))
+        line_set.lines = o3d.utility.Vector2iVector(np.array([[i*2, i*2+1] for i in range(len(lines))]))
+        line_set.colors = o3d.utility.Vector3dVector(colors)
+        return line_set
+
+    def visualize_voxel_struct(self, camera_pose: np.ndarray = None):
+        if camera_pose is None:
+            camera_pose = self.curr_camera_pose
+        import open3d as o3d
+        vis = o3d.visualization.Visualizer()
+        vis.create_window()
+        
+        coordinate_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=0.1, origin=[0, 0, 0])
+        vis.add_geometry(coordinate_frame)
+        
+        # 显示已占据的体素（蓝色）
+        occupied_voxels = self.create_voxel_geometry(
+            self.occupied_voxels,
+            [0, 0, 1],
+            self.voxel_resolution
+        )
+        if occupied_voxels:
+            vis.add_geometry(occupied_voxels)
+        
+        # 显示空体素（绿色）
+        empty_voxels = self.create_voxel_geometry(
+            self.empty_voxels,
+            [0, 1, 0],
+            self.voxel_resolution
+        )
+        if empty_voxels:
+            vis.add_geometry(empty_voxels)
+        
+        # 显示未知体素（灰色）
+        unknown_voxels = self.create_voxel_geometry(
+            self.unknown_voxels,
+            [0.5, 0.5, 0.5],
+            self.voxel_resolution
+        )
+        if unknown_voxels:
+            vis.add_geometry(unknown_voxels)
+        
+        # 显示frontier体素（红色）
+        frontier_voxels = self.create_voxel_geometry(
+            self.frontier_voxels,
+            [1, 0, 0],
+            self.voxel_resolution
+        )
+        if frontier_voxels:
+            vis.add_geometry(frontier_voxels)
+        
+        # 显示光线
+        if camera_pose is not None:
+            directions = self.generate_ray_directions()
+            rays = self.create_ray_geometry(
+                camera_pose[:3, 3],
+                directions,
+                camera_pose[:3, :3],
+                length=0.5  # 光线长度
+            )
+            vis.add_geometry(rays)
+        
+        opt = vis.get_render_option()
+        opt.background_color = np.asarray([0.8, 0.8, 0.8])
+        opt.point_size = 5.0
+        
+        vis.run()
+        vis.destroy_window()
+
+class PBNBV:
+    def __init__(self, voxel_resolution=0.01, camera_intrinsic=None):
+        self.voxel_resolution = voxel_resolution
+        self.voxel_struct = VoxelStruct(voxel_resolution)
+        self.camera_intrinsic = camera_intrinsic or np.array([
+            [902.14, 0, 320],
+            [0, 902.14, 200],
+            [0, 0, 1]
+        ])
+        self.focal_length = (self.camera_intrinsic[0,0] + self.camera_intrinsic[1,1]) / 2 / 1000
+        self.ellipsoids = []
+        
+    def capture(self, point_cloud: np.ndarray, camera_pose: np.ndarray):
+        # ------  Debug Start ------
+        #import ipdb;ipdb.set_trace()
+        # ------  Debug End ------
+        frontier_voxels, occupied_voxels = self.voxel_struct.update_voxel_map(point_cloud, camera_pose)
+        self.fit_ellipsoids(frontier_voxels, occupied_voxels)
+    
+    def reset(self):
+        self.ellipsoids = []
+        self.voxel_struct = VoxelStruct(self.voxel_resolution)
+    
+    def fit_ellipsoids(self, frontier_voxels: List[np.ndarray], occupied_voxels: List[np.ndarray], 
+                      max_ellipsoids=10):
+        self.ellipsoids = []
+        
+        if not frontier_voxels and not occupied_voxels:
+            return
+            
+        if frontier_voxels:
+            frontier_gmm = self.fit_gmm(np.array(frontier_voxels), max_ellipsoids)
+            self.ellipsoids.extend(self.gmm_to_ellipsoids(frontier_gmm, "frontier"))
+        
+        if occupied_voxels:
+            occupied_gmm = self.fit_gmm(np.array(occupied_voxels), max_ellipsoids)
+            self.ellipsoids.extend(self.gmm_to_ellipsoids(occupied_gmm, "occupied"))
+    
+    def fit_gmm(self, data: np.ndarray, max_components: int) -> GaussianMixture:
+        best_gmm = None
+        best_bic = np.inf
+        
+        for n in range(1, min(max_components, len(data)) + 1):
+            gmm = GaussianMixture(n_components=n, covariance_type='full')
+            gmm.fit(data)
+            bic = gmm.bic(data)
+            
+            if bic < best_bic:
+                best_bic = bic
+                best_gmm = gmm
+                
+        return best_gmm
+    
+    def gmm_to_ellipsoids(self, gmm: GaussianMixture, ellipsoid_type: str) -> List[Dict]:
+        ellipsoids = []
+        
+        for i in range(gmm.n_components):
+            mean = gmm.means_[i]
+            cov = gmm.covariances_[i]
+            
+            eigvals, eigvecs = np.linalg.eigh(cov)
+            radii = np.sqrt(eigvals) * 3
+            
+            rotation = eigvecs
+            pose = np.eye(4)
+            pose[:3, :3] = rotation
+            pose[:3, 3] = mean
+            
+            ellipsoids.append({
+                "type": ellipsoid_type,
+                "pose": pose,
+                "radii": radii
+            })
+            
+        return ellipsoids
+    
+    def evaluate_viewpoint(self, viewpoint_pose: np.ndarray) -> float:
+        if not self.ellipsoids:
+            return 0.0
+            
+        ellipsoid_weights = self.compute_ellipsoid_weights(viewpoint_pose)
+        
+        projection_scores = []
+        for ellipsoid, weight in zip(self.ellipsoids, ellipsoid_weights):
+            score = self.project_ellipsoid(ellipsoid, viewpoint_pose) * weight
+            projection_scores.append((ellipsoid["type"], score))
+        
+        frontier_score = sum(s for t, s in projection_scores if t == "frontier")
+        occupied_score = sum(s for t, s in projection_scores if t == "occupied")
+        
+        return frontier_score - occupied_score
+    
+    def compute_ellipsoid_weights(self, viewpoint_pose: np.ndarray) -> List[float]:
+        centers_world = np.array([e["pose"][:3, 3] for e in self.ellipsoids])
+        centers_homo = np.hstack((centers_world, np.ones((len(centers_world), 1))))
+        centers_cam = (np.linalg.inv(viewpoint_pose) @ centers_homo.T).T[:, :3]
+        
+        z_coords = centers_cam[:, 2]
+        sorted_indices = np.argsort(z_coords)
+        
+        weights = np.zeros(len(self.ellipsoids))
+        for rank, idx in enumerate(sorted_indices):
+            weights[idx] = 0.5 ** rank
+            
+        return weights.tolist()
+    
+    def project_ellipsoid(self, ellipsoid: Dict, viewpoint_pose: np.ndarray) -> float:
+        ellipsoid_pose_cam = np.linalg.inv(viewpoint_pose) @ ellipsoid["pose"]
+        
+        radii = ellipsoid["radii"]
+        rotation = ellipsoid_pose_cam[:3, :3]
+        scales = np.diag(radii)
+        transform = rotation @ scales
+        
+        major_axis = np.linalg.norm(transform[:, 0])
+        minor_axis = np.linalg.norm(transform[:, 1])
+        area = np.pi * major_axis * minor_axis
+        
+        return area
+    
+    def generate_candidate_views(self, num_views=100, longitude_num=5) -> List[np.ndarray]:
+        if self.voxel_struct.bbx_min is None:
+            return []
+            
+        center = (self.voxel_struct.bbx_min + self.voxel_struct.bbx_max) / 2
+        #import ipdb; ipdb.set_trace()
+        radius = np.linalg.norm(self.voxel_struct.bbx_max - self.voxel_struct.bbx_min) / 2 + 0.3
+        candidate_views = []
+        
+        latitudes = np.linspace(np.deg2rad(40), np.deg2rad(90), longitude_num)
+        
+        lengths = [2 * np.pi * np.sin(lat) * radius for lat in latitudes]
+        total_length = sum(lengths)
+        points_per_lat = [int(round(num_views * l / total_length)) for l in lengths]
+        
+        for lat, n in zip(latitudes, points_per_lat):
+            if n == 0:
+                continue
+                
+            longitudes = np.linspace(0, 2*np.pi, n, endpoint=False)
+            for lon in longitudes:
+                x = radius * np.sin(lat) * np.cos(lon)
+                y = radius * np.sin(lat) * np.sin(lon)
+                z = radius * np.cos(lat)
+                position = np.array([x, y, z]) + center
+                
+                z_axis = center - position
+                z_axis /= np.linalg.norm(z_axis)
+                
+                x_axis = np.cross(z_axis, np.array([0, 0, 1]))
+                if np.linalg.norm(x_axis) < 1e-6:
+                    x_axis = np.array([1, 0, 0])
+                x_axis /= np.linalg.norm(x_axis)
+                
+                y_axis = np.cross(z_axis, x_axis)
+                y_axis /= np.linalg.norm(y_axis)
+                
+                rotation = np.column_stack((x_axis, y_axis, z_axis))
+                
+                view_pose = np.eye(4)
+                view_pose[:3, :3] = rotation
+                view_pose[:3, 3] = position
+                
+                candidate_views.append(view_pose)
+                
+        return candidate_views
+    
+    def select_best_view(self, pred_pose_mat: np.ndarray) -> np.ndarray:
+        # ------  Debug Start ------
+        # import ipdb;ipdb.set_trace()
+        # ------  Debug End ------
+        candidate_views = pred_pose_mat
+            
+        scores = [self.evaluate_viewpoint(view) for view in candidate_views]
+        best_idx = np.argmax(scores)
+        
+        return candidate_views[best_idx],candidate_views
+    
+    def execute(self, pred_pose_mat: np.ndarray) -> Tuple[np.ndarray, bool]:
+        best_view,candidate_views = self.select_best_view(pred_pose_mat)
+        
+        has_frontier = any(e["type"] == "frontier" for e in self.ellipsoids)
+        done = not has_frontier
+        
+        return best_view, candidate_views, done
+    
+    def visualize(self):
+        self.voxel_struct.visualize_voxel_struct(self.voxel_struct.curr_camera_pose)
+
+    
+@stereotype.runner("pb_global_points_inferencer")
+class PBGlobalPointsInferencer(Runner):
+    def __init__(self, config_path):
+        
+        super().__init__(config_path)
+    
+        self.script_path = ConfigManager.get(namespace.Stereotype.RUNNER, "blender_script_path")
+        self.output_dir = ConfigManager.get(namespace.Stereotype.RUNNER, "output_dir")
+        self.voxel_size = ConfigManager.get(namespace.Stereotype.RUNNER, "voxel_size")
+        self.min_new_area = ConfigManager.get(namespace.Stereotype.RUNNER, "min_new_area")
+        CM = 0.01
+        self.min_new_pts_num = self.min_new_area * (CM / self.voxel_size) **2
+        self.overlap_limit = ConfigManager.get(namespace.Stereotype.RUNNER, "overlap_limit")
+        self.enable_cluster = ConfigManager.get(namespace.Stereotype.RUNNER, "enable_cluster")
+        self.pbnbv = PBNBV()
+        ''' Pipeline '''
+        self.pipeline_name = self.config[namespace.Stereotype.PIPELINE]
+        self.pipeline:torch.nn.Module = ComponentFactory.create(namespace.Stereotype.PIPELINE, self.pipeline_name)
+        self.pipeline = self.pipeline.to(self.device)
+            
+        ''' Experiment '''
+        self.load_experiment("nbv_evaluator")
+        self.stat_result_path = os.path.join(self.output_dir, "stat.json")
+        if os.path.exists(self.stat_result_path):
+            with open(self.stat_result_path, "r") as f:
+                self.stat_result = json.load(f)
+        else:
+            self.stat_result = {}
+
+        ''' Test '''
+        self.test_config = ConfigManager.get(namespace.Stereotype.RUNNER, namespace.Mode.TEST)
+        self.test_dataset_name_list = self.test_config["dataset_list"]
+        self.test_set_list = []
+        self.test_writer_list = []
+        seen_name = set()
+        for test_dataset_name in self.test_dataset_name_list:
+            if test_dataset_name not in seen_name:
+                seen_name.add(test_dataset_name)
+            else:
+                raise ValueError("Duplicate test dataset name: {}".format(test_dataset_name))
+            test_set: BaseDataset = ComponentFactory.create(namespace.Stereotype.DATASET, test_dataset_name)
+            self.test_set_list.append(test_set)
+        self.print_info()
+        
+
+    def run(self):
+        Log.info("Loading from epoch {}.".format(self.current_epoch))
+        self.inference()
+        Log.success("Inference finished.")
+
+    def get_output_data(self, inference_output):
+        pred_pose_9d = inference_output["pred_pose_9d"]
+        pred_pose_9d = pred_pose_9d.cpu().numpy()
+        pred_pose_mat = np.zeros((pred_pose_9d.shape[0], 4, 4))
+        
+        for i in range(pred_pose_9d.shape[0]):
+            pred_pose_mat[i, :3, :3] = PoseUtil.rotation_6d_to_matrix_numpy(pred_pose_9d[i, :6])
+            pred_pose_mat[i, :3, 3] = pred_pose_9d[i, 6:]
+            pred_pose_mat[i, 3, 3] = 1
+        #import ipdb; ipdb.set_trace()
+        pose_matrix,candidate_views, done = self.pbnbv.execute(pred_pose_mat)
+        
+        rot = pose_matrix[:3,:3]
+        
+        pose_6d = PoseUtil.matrix_to_rotation_6d_numpy(rot)
+        translation = pose_matrix[:3, 3]
+
+        pose_9d = np.concatenate([pose_6d, translation], axis=0).reshape(1,9)
+        pose_9d = pose_9d.repeat(50, axis=0)
+        
+        #import ipdb; ipdb.set_trace()
+        #self.visualize(pose_matrix,candidate_views)
+        
+        return {"pred_pose_9d": pose_9d, "candidate_views": candidate_views}
+        
+
+    def inference(self):
+        self.pipeline.eval()
+        with torch.no_grad():
+            test_set: BaseDataset
+            for dataset_idx, test_set in enumerate(self.test_set_list):
+                status_manager.set_progress("inference", "inferencer", f"dataset", dataset_idx, len(self.test_set_list))
+                test_set_name = test_set.get_name()
+
+                total=int(len(test_set))
+                for i in tqdm(range(total), desc=f"Processing {test_set_name}", ncols=100):
+                    try:
+                        self.pbnbv.reset()
+                        data = test_set.__getitem__(i)
+                        scene_name = data["scene_name"]
+                        inference_result_path = os.path.join(self.output_dir, test_set_name, f"{scene_name}.pkl")
+                        
+                        if os.path.exists(inference_result_path):
+                            Log.info(f"Inference result already exists for scene: {scene_name}")
+                            continue
+                        
+                        status_manager.set_progress("inference", "inferencer", f"Batch[{test_set_name}]", i+1, total)
+                        output = self.predict_sequence(data)
+                        self.save_inference_result(test_set_name, data["scene_name"], output)
+                    except Exception as e:
+                        print(e)
+                        Log.error(f"Error, {e}")
+                        import traceback
+                        traceback.print_exc()
+                        continue
+                    
+            status_manager.set_progress("inference", "inferencer", f"dataset", len(self.test_set_list), len(self.test_set_list))
+        
+    def predict_sequence(self, data, cr_increase_threshold=0, overlap_area_threshold=25, scan_points_threshold=10, max_iter=50, max_retry = 10, max_success=3):
+        scene_name = data["scene_name"]
+        Log.info(f"Processing scene: {scene_name}")
+        status_manager.set_status("inference", "inferencer", "scene", scene_name)
+
+        ''' data for rendering '''
+        scene_path = data["scene_path"]
+        O_to_L_pose = data["O_to_L_pose"]
+        voxel_threshold = self.voxel_size
+        filter_degree = 75
+        down_sampled_model_pts = data["gt_pts"]
+        
+        first_frame_to_world_9d = data["first_scanned_n_to_world_pose_9d"][0]
+        first_frame_to_world = np.eye(4)
+        first_frame_to_world[:3,:3] = PoseUtil.rotation_6d_to_matrix_numpy(first_frame_to_world_9d[:6])
+        first_frame_to_world[:3,3] = first_frame_to_world_9d[6:]
+        self.pbnbv.capture(data["first_scanned_pts"][0], first_frame_to_world)
+        ''' data for inference '''
+        input_data = {}
+        
+        input_data["combined_scanned_pts"] = torch.tensor(data["first_scanned_pts"][0], dtype=torch.float32).to(self.device).unsqueeze(0)
+        input_data["scanned_pts_mask"] = [torch.zeros(input_data["combined_scanned_pts"].shape[1], dtype=torch.bool).to(self.device).unsqueeze(0)]
+        input_data["scanned_n_to_world_pose_9d"] = [torch.tensor(data["first_scanned_n_to_world_pose_9d"], dtype=torch.float32).to(self.device)]
+        input_data["mode"] = namespace.Mode.TEST
+        input_pts_N = input_data["combined_scanned_pts"].shape[1]
+        
+        root = os.path.dirname(scene_path)
+        display_table_info = DataLoadUtil.get_display_table_info(root, scene_name)
+        radius = display_table_info["radius"]
+        scan_points = np.asarray(ReconstructionUtil.generate_scan_points(display_table_top=0,display_table_radius=radius))
+
+        first_frame_target_pts, first_frame_target_normals, first_frame_scan_points_indices = RenderUtil.render_pts(first_frame_to_world, scene_path, self.script_path, scan_points, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=O_to_L_pose)
+        scanned_view_pts = [first_frame_target_pts]
+        history_indices = [first_frame_scan_points_indices]
+        last_pred_cr, added_pts_num = self.compute_coverage_rate(scanned_view_pts, None, down_sampled_model_pts, threshold=voxel_threshold)
+        retry_duplication_pose = []
+        retry_no_pts_pose = []
+        retry_overlap_pose = []
+        retry = 0
+        pred_cr_seq = [last_pred_cr]
+        success = 0
+        last_pts_num = PtsUtil.voxel_downsample_point_cloud(data["first_scanned_pts"][0], voxel_threshold).shape[0]
+        import time
+        while len(pred_cr_seq) < max_iter and retry < max_retry and success < max_success:
+            #import ipdb; ipdb.set_trace()
+            
+            Log.green(f"iter: {len(pred_cr_seq)}, retry: {retry}/{max_retry}, success: {success}/{max_success}")
+            combined_scanned_pts = np.vstack(scanned_view_pts)
+            voxel_downsampled_combined_scanned_pts_np, inverse =  self.voxel_downsample_with_mapping(combined_scanned_pts, voxel_threshold)
+            
+            inference_output = self.pipeline(input_data)
+            inference_pred_pose_9d = inference_output["pred_pose_9d"]
+            output = self.get_output_data(inference_output)
+            
+            pred_pose_9d = output["pred_pose_9d"]
+            
+            if not self.enable_cluster:
+                pred_pose_9d_candidates = [pred_pose_9d[0]]
+            else:
+                predict_result = PredictResult(pred_pose_9d.cpu().numpy(), input_pts=input_data["combined_scanned_pts"][0].cpu().numpy(), cluster_params=dict(eps=0.25, min_samples=3))
+                pred_pose_9d_candidates = predict_result.candidate_9d_poses
+            pred_pose = np.eye(4)
+            for pred_pose_9d in pred_pose_9d_candidates:
+                #import ipdb; ipdb.set_trace()
+                t1_s = time.time()
+                pred_pose_9d = np.array(pred_pose_9d, dtype=np.float32)
+                pred_pose[:3,:3] = PoseUtil.rotation_6d_to_matrix_numpy(pred_pose_9d[:6])
+                pred_pose[:3,3] = pred_pose_9d[6:]
+                try:
+                    new_target_pts, new_target_normals, new_scan_points_indices = RenderUtil.render_pts(pred_pose, scene_path, self.script_path, scan_points, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=O_to_L_pose)
+                    #import ipdb; ipdb.set_trace()
+                    if not ReconstructionUtil.check_scan_points_overlap(history_indices, new_scan_points_indices, scan_points_threshold):
+                        curr_overlap_area_threshold = overlap_area_threshold
+                    else:
+                        curr_overlap_area_threshold = overlap_area_threshold * 0.5  
+
+                    downsampled_new_target_pts = PtsUtil.voxel_downsample_point_cloud(new_target_pts, voxel_threshold)
+                    if self.overlap_limit:
+                        overlap, _ = ReconstructionUtil.check_overlap(downsampled_new_target_pts, voxel_downsampled_combined_scanned_pts_np, overlap_area_threshold = curr_overlap_area_threshold, voxel_size=voxel_threshold, require_new_added_pts_num = True)
+                        if not overlap:
+                            Log.yellow("no overlap!")
+                            retry += 1
+                            retry_overlap_pose.append(pred_pose.tolist())
+                            continue
+                    
+                    history_indices.append(new_scan_points_indices)
+                except Exception as e:
+                    Log.error(f"Error in scene {scene_path}, {e}")
+                    print("current pose: ", pred_pose)
+                    print("curr_pred_cr: ", last_pred_cr)
+                    retry_no_pts_pose.append(pred_pose.tolist())
+                    retry += 1
+                    continue
+                
+                if new_target_pts.shape[0] == 0:
+                    Log.red("no pts in new target")
+                    retry_no_pts_pose.append(pred_pose.tolist())
+                    retry += 1
+                    continue
+                
+                pred_cr, _ = self.compute_coverage_rate(scanned_view_pts, new_target_pts, down_sampled_model_pts, threshold=voxel_threshold)
+                Log.yellow(f"{pred_cr}, {last_pred_cr}, max: , {data['seq_max_coverage_rate']}")
+                if pred_cr >= data["seq_max_coverage_rate"] - 1e-3:
+                    print("max coverage rate reached!: ", pred_cr)
+                    
+            
+
+                pred_cr_seq.append(pred_cr)
+                scanned_view_pts.append(new_target_pts)
+                
+                input_data["scanned_n_to_world_pose_9d"] = [torch.cat([input_data["scanned_n_to_world_pose_9d"][0], inference_pred_pose_9d], dim=0)]
+                
+                combined_scanned_pts = np.vstack(scanned_view_pts)
+                voxel_downsampled_combined_scanned_pts_np = PtsUtil.voxel_downsample_point_cloud(combined_scanned_pts, voxel_threshold)
+                random_downsampled_combined_scanned_pts_np = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, input_pts_N)
+                self.pbnbv.capture(np.array(random_downsampled_combined_scanned_pts_np, dtype=np.float32), pred_pose)
+                t1_e = time.time()
+                t2_s = time.time()
+                input_data["combined_scanned_pts"] = torch.tensor(random_downsampled_combined_scanned_pts_np, dtype=torch.float32).unsqueeze(0).to(self.device)
+
+                
+                last_pred_cr = pred_cr
+                pts_num = voxel_downsampled_combined_scanned_pts_np.shape[0]
+                Log.info(f"delta pts num:,{pts_num - last_pts_num },{pts_num}, {last_pts_num}")
+
+                if pts_num - last_pts_num < self.min_new_pts_num and pred_cr <= data["seq_max_coverage_rate"] - 1e-2:
+                    retry += 1
+                    retry_duplication_pose.append(pred_pose.tolist())
+                    Log.red(f"delta pts num < {self.min_new_pts_num}:, {pts_num}, {last_pts_num}")
+                elif pts_num - last_pts_num < self.min_new_pts_num and pred_cr > data["seq_max_coverage_rate"] - 1e-2:
+                    success += 1
+                    Log.success(f"delta pts num < {self.min_new_pts_num}:, {pts_num}, {last_pts_num}")
+
+                last_pts_num = pts_num
+                t2_e = time.time()
+                print("t1:", t1_e - t1_s)
+                print("t2:", t2_e - t2_s)
+
+        input_data["scanned_n_to_world_pose_9d"] = input_data["scanned_n_to_world_pose_9d"][0].tolist()
+        result = {
+            "pred_pose_9d_seq": input_data["scanned_n_to_world_pose_9d"],
+            "combined_scanned_pts": input_data["combined_scanned_pts"],
+            "target_pts_seq": scanned_view_pts,
+            "coverage_rate_seq": pred_cr_seq,
+            "max_coverage_rate": data["seq_max_coverage_rate"],
+            "pred_max_coverage_rate": max(pred_cr_seq),
+            "scene_name": scene_name,
+            "retry_no_pts_pose": retry_no_pts_pose,
+            "retry_duplication_pose": retry_duplication_pose,
+            "retry_overlap_pose": retry_overlap_pose,
+            "best_seq_len": data["best_seq_len"],
+        }
+        self.stat_result[scene_name] = {
+            "coverage_rate_seq": pred_cr_seq,
+            "pred_max_coverage_rate": max(pred_cr_seq),
+            "pred_seq_len": len(pred_cr_seq),
+        }
+        print('success rate: ', max(pred_cr_seq))
+
+        return result
+
+    def voxel_downsample_with_mapping(self, point_cloud, voxel_size=0.003):
+        voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32)
+        unique_voxels, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True)
+        idx_sort = np.argsort(inverse)
+        idx_unique = idx_sort[np.cumsum(counts)-counts]
+        downsampled_points = point_cloud[idx_unique]
+        return downsampled_points, inverse
+    
+    def compute_coverage_rate(self, scanned_view_pts, new_pts, model_pts, threshold=0.005):
+        if new_pts is not None:
+            new_scanned_view_pts = scanned_view_pts + [new_pts]
+        else:
+            new_scanned_view_pts = scanned_view_pts
+        combined_point_cloud = np.vstack(new_scanned_view_pts)
+        down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_point_cloud,threshold)
+        return ReconstructionUtil.compute_coverage_rate(model_pts, down_sampled_combined_point_cloud, threshold)
+    
+    def voxel_downsample_with_mapping(self, point_cloud, voxel_size=0.003):
+        voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32)
+        unique_voxels, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True)
+        idx_sort = np.argsort(inverse)
+        idx_unique = idx_sort[np.cumsum(counts)-counts]
+        downsampled_points = point_cloud[idx_unique]
+        return downsampled_points, inverse
+
+    def save_inference_result(self, dataset_name, scene_name, output):
+        dataset_dir = os.path.join(self.output_dir, dataset_name)
+        if not os.path.exists(dataset_dir):
+            os.makedirs(dataset_dir)
+        output_path = os.path.join(dataset_dir, f"{scene_name}.pkl")
+        pickle.dump(output, open(output_path, "wb"))
+        with open(self.stat_result_path, "w") as f:
+            json.dump(self.stat_result, f)
+    
+
+    def get_checkpoint_path(self, is_last=False):
+        return os.path.join(self.experiment_path, namespace.Direcotry.CHECKPOINT_DIR_NAME,
+                            "Epoch_{}.pth".format(
+                                self.current_epoch if self.current_epoch != -1 and not is_last else "last"))
+
+    def load_checkpoint(self, is_last=False):
+        self.load(self.get_checkpoint_path(is_last))
+        Log.success(f"Loaded checkpoint from {self.get_checkpoint_path(is_last)}")
+        if is_last:
+            checkpoint_root = os.path.join(self.experiment_path, namespace.Direcotry.CHECKPOINT_DIR_NAME)
+            meta_path = os.path.join(checkpoint_root, "meta.json")
+            if not os.path.exists(meta_path):
+                raise FileNotFoundError(
+                    "No checkpoint meta.json file in the experiment {}".format(self.experiments_config["name"]))
+            file_path = os.path.join(checkpoint_root, "meta.json")
+            with open(file_path, "r") as f:
+                meta = json.load(f)
+            self.current_epoch = meta["last_epoch"]
+            self.current_iter = meta["last_iter"]
+
+    def load_experiment(self, backup_name=None):
+        super().load_experiment(backup_name)
+        self.current_epoch = self.experiments_config["epoch"]
+        self.load_checkpoint(is_last=(self.current_epoch == -1))
+
+    def create_experiment(self, backup_name=None):
+        super().create_experiment(backup_name)
+       
+        
+    def load(self, path):
+        state_dict = torch.load(path)
+        self.pipeline.load_state_dict(state_dict)
+
+    def print_info(self):
+        def print_dataset(dataset: BaseDataset):
+            config = dataset.get_config()
+            name = dataset.get_name()
+            Log.blue(f"Dataset: {name}")
+            for k,v in config.items():
+                Log.blue(f"\t{k}: {v}")
+
+        super().print_info()
+        table_size = 70
+        Log.blue(f"{'+' + '-' * (table_size // 2)} Pipeline {'-' * (table_size // 2)}" + '+')
+        Log.blue(self.pipeline)
+        Log.blue(f"{'+' + '-' * (table_size // 2)} Datasets {'-' * (table_size // 2)}" + '+')
+        for i, test_set in enumerate(self.test_set_list):
+            Log.blue(f"test dataset {i}: ")
+            print_dataset(test_set)
+
+        Log.blue(f"{'+' + '-' * (table_size // 2)}----------{'-' * (table_size // 2)}" + '+')
+