add multi seq training
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@@ -12,8 +12,8 @@ class RenderUtil:
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def render_pts(cam_pose, scene_path,script_path, model_points_normals, voxel_threshold=0.005, filter_degree=75, nO_to_nL_pose=None, require_full_scene=False):
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nO_to_world_pose = DataLoadUtil.get_real_cam_O_from_cam_L(cam_pose, nO_to_nL_pose)
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with tempfile.TemporaryDirectory() as temp_dir:
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params = {
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"cam_pose": nO_to_world_pose.tolist(),
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@@ -30,7 +30,6 @@ class RenderUtil:
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print(result.stderr)
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return None
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path = os.path.join(temp_dir, "tmp")
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point_cloud = DataLoadUtil.get_target_point_cloud_world_from_path(path, binocular=True)
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cam_params = DataLoadUtil.load_cam_info(path, binocular=True)
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filtered_point_cloud = ReconstructionUtil.filter_points(point_cloud, model_points_normals, cam_pose=cam_params["cam_to_world"], voxel_size=voxel_threshold, theta=filter_degree)
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@@ -44,4 +43,5 @@ class RenderUtil:
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point_cloud_R = PtsUtil.random_downsample_point_cloud(point_cloud_R, 65536)
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full_scene_point_cloud = DataLoadUtil.get_overlapping_points(point_cloud_L, point_cloud_R)
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return filtered_point_cloud, full_scene_point_cloud
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