hofee/nbv_reconstruction
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

update normal strategy

Browse Source
This commit is contained in:
hofee
2024-10-23 02:58:58 -05:00
parent 7e68259f6d
commit 64891ef189
8 changed files with 185 additions and 57 deletions
Download Patch File Download Diff File Expand all files Collapse all files

38
runners/strategy_generator.py
View File

@@ -24,12 +24,15 @@ class StrategyGenerator(Runner):
}
self.overwrite = ConfigManager.get("runner", "generate", "overwrite")
self.seq_num = ConfigManager.get("runner","generate","seq_num")
self.overlap_area_threshold = ConfigManager.get("runner","generate","overlap_area_threshold")
self.compute_with_normal = ConfigManager.get("runner","generate","compute_with_normal")
self.scan_points_threshold = ConfigManager.get("runner","generate","scan_points_threshold")
def run(self):
dataset_name_list = ConfigManager.get("runner", "generate", "dataset_list")
voxel_threshold, soft_overlap_threshold, hard_overlap_threshold = ConfigManager.get("runner","generate","voxel_threshold"), ConfigManager.get("runner","generate","soft_overlap_threshold"), ConfigManager.get("runner","generate","hard_overlap_threshold")
voxel_threshold = ConfigManager.get("runner","generate","voxel_threshold")
for dataset_idx in range(len(dataset_name_list)):
dataset_name = dataset_name_list[dataset_idx]
status_manager.set_progress("generate_strategy", "strategy_generator", "dataset", dataset_idx, len(dataset_name_list))
@@ -51,7 +54,7 @@ class StrategyGenerator(Runner):
cnt += 1
continue
self.generate_sequence(root_dir, scene_name,voxel_threshold, soft_overlap_threshold, hard_overlap_threshold)
self.generate_sequence(root_dir, scene_name,voxel_threshold)
cnt += 1
status_manager.set_progress("generate_strategy", "strategy_generator", "scene", total, total)
status_manager.set_progress("generate_strategy", "strategy_generator", "dataset", len(dataset_name_list), len(dataset_name_list))
@@ -64,28 +67,34 @@ class StrategyGenerator(Runner):
def load_experiment(self, backup_name=None):
super().load_experiment(backup_name)
def generate_sequence(self, root, scene_name, voxel_threshold, soft_overlap_threshold, hard_overlap_threshold):
def generate_sequence(self, root, scene_name, voxel_threshold):
status_manager.set_status("generate_strategy", "strategy_generator", "scene", scene_name)
frame_num = DataLoadUtil.get_scene_seq_length(root, scene_name)
model_points_normals = DataLoadUtil.load_points_normals(root, scene_name)
model_pts = model_points_normals[:,:3]
down_sampled_model_pts = PtsUtil.voxel_downsample_point_cloud(model_pts, voxel_threshold)
down_sampled_model_pts, idx = PtsUtil.voxel_downsample_point_cloud(model_pts, voxel_threshold, require_idx=True)
down_sampled_model_nrm = model_points_normals[idx, 3:]
pts_list = []
nrm_list = []
scan_points_indices_list = []
non_zero_cnt = 0
for frame_idx in range(frame_num):
status_manager.set_progress("generate_strategy", "strategy_generator", "loading frame", frame_idx, frame_num)
pts_path = os.path.join(root,scene_name, "pts", f"{frame_idx}.npy")
nrm_path = os.path.join(root,scene_name, "nrm", f"{frame_idx}.npy")
idx_path = os.path.join(root,scene_name, "scan_points_indices", f"{frame_idx}.npy")
point_cloud = np.load(pts_path)
sampled_point_cloud = PtsUtil.voxel_downsample_point_cloud(point_cloud, voxel_threshold)
pts = np.load(pts_path)
if pts.shape[0] == 0:
nrm = np.zeros((0,3))
else:
nrm = np.load(nrm_path)
indices = np.load(idx_path)
pts_list.append(sampled_point_cloud)
pts_list.append(pts)
nrm_list.append(nrm)
scan_points_indices_list.append(indices)
if sampled_point_cloud.shape[0] > 0:
if pts.shape[0] > 0:
non_zero_cnt += 1
status_manager.set_progress("generate_strategy", "strategy_generator", "loading frame", frame_num, frame_num)
@@ -93,7 +102,7 @@ class StrategyGenerator(Runner):
init_view_list = []
idx = 0
while len(init_view_list) < seq_num and idx < len(pts_list):
if pts_list[idx].shape[0] > 100:
if pts_list[idx].shape[0] > 50:
init_view_list.append(idx)
idx += 1
@@ -102,8 +111,13 @@ class StrategyGenerator(Runner):
for init_view in init_view_list:
status_manager.set_progress("generate_strategy", "strategy_generator", "computing sequence", seq_idx, len(init_view_list))
start = time.time()
limited_useful_view, _, _ = ReconstructionUtil.compute_next_best_view_sequence_with_overlap(down_sampled_model_pts, pts_list, scan_points_indices_list = scan_points_indices_list,init_view=init_view,
threshold=voxel_threshold, soft_overlap_threshold=soft_overlap_threshold, hard_overlap_threshold= hard_overlap_threshold, scan_points_threshold=10, status_info=self.status_info)
if not self.compute_with_normal:
limited_useful_view, _, _ = ReconstructionUtil.compute_next_best_view_sequence(down_sampled_model_pts, pts_list, scan_points_indices_list = scan_points_indices_list,init_view=init_view,
threshold=voxel_threshold, scan_points_threshold=self.scan_points_threshold, overlap_area_threshold=self.overlap_area_threshold, status_info=self.status_info)
else:
limited_useful_view, _, _ = ReconstructionUtil.compute_next_best_view_sequence_with_normal(down_sampled_model_pts, down_sampled_model_nrm, pts_list, nrm_list, scan_points_indices_list = scan_points_indices_list,init_view=init_view,
threshold=voxel_threshold, scan_points_threshold=self.scan_points_threshold, overlap_area_threshold=self.overlap_area_threshold, status_info=self.status_info)
end = time.time()
print(f"Time: {end-start}")
data_pairs = self.generate_data_pairs(limited_useful_view)