hofee/nbv_reconstruction
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Merge branch 'master' of https://git.hofee.top/hofee/nbv_reconstruction

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This commit is contained in:
hofee 2024-10-19 19:06:26 +08:00
commit ed569254dc
3 changed files with 22 additions and 21 deletions
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2
runners/strategy_generator.py
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@ -73,7 +73,7 @@ class StrategyGenerator(Runner):
def generate_sequence(self, root, scene_name, voxel_threshold, soft_overlap_threshold, hard_overlap_threshold):
status_manager.set_status("generate_strategy", "strategy_generator", "scene", scene_name)
frame_num = DataLoadUtil.get_scene_seq_length(root, scene_name)
import ipdb; ipdb.set_trace()
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)

15
utils/pts.py
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@ -1,16 +1,14 @@
import numpy as np
import open3d as o3d
import torch
from scipy.spatial import cKDTree
class PtsUtil:
@staticmethod
def voxel_downsample_point_cloud(point_cloud, voxel_size=0.005):
o3d_pc = o3d.geometry.PointCloud()
o3d_pc.points = o3d.utility.Vector3dVector(point_cloud)
downsampled_pc = o3d_pc.voxel_down_sample(voxel_size)
return np.asarray(downsampled_pc.points)
voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32)
unique_voxels = np.unique(voxel_indices, axis=0, return_inverse=True)
return unique_voxels[0]*voxel_size
@staticmethod
def random_downsample_point_cloud(point_cloud, num_points, require_idx=False):
@ -94,5 +92,8 @@ class PtsUtil:
z_filtered_points = filtered_sampled_points[idx]
return z_filtered_points[:, :3]
@staticmethod
def point_to_hash(point, voxel_size):
return tuple(np.floor(point / voxel_size).astype(int))

26
utils/reconstruction.py
View File

@ -3,7 +3,7 @@ from scipy.spatial import cKDTree
from utils.pts import PtsUtil
class ReconstructionUtil:
@staticmethod
def compute_coverage_rate(target_point_cloud, combined_point_cloud, threshold=0.01):
kdtree = cKDTree(combined_point_cloud)
@ -12,6 +12,18 @@ class ReconstructionUtil:
coverage_rate = covered_points_num / target_point_cloud.shape[0]
return coverage_rate, covered_points_num
def compute_coverage_rate_with_normal(target_point_cloud, combined_point_cloud, target_normal, combined_normal, threshold=0.01, normal_threshold=0.1):
kdtree = cKDTree(combined_point_cloud)
distances, indices = kdtree.query(target_point_cloud)
is_covered_by_distance = distances < threshold
normal_dots = np.einsum('ij,ij->i', target_normal, combined_normal[indices])
is_covered_by_normal = normal_dots > normal_threshold
covered_points_num = np.sum(is_covered_by_distance & is_covered_by_normal)
coverage_rate = covered_points_num / target_point_cloud.shape[0]
return coverage_rate, covered_points_num
@staticmethod
def compute_overlap_rate(new_point_cloud, combined_point_cloud, threshold=0.01):
kdtree = cKDTree(combined_point_cloud)
@ -145,18 +157,6 @@ class ReconstructionUtil:
attempts += 1
return points
@staticmethod
def compute_covered_scan_points(scan_points, point_cloud, threshold=0.01):
tree = cKDTree(point_cloud)
covered_points = []
indices = []
for i, scan_point in enumerate(scan_points):
if tree.query_ball_point(scan_point, threshold):
covered_points.append(scan_point)
indices.append(i)
return covered_points, indices
@staticmethod
def check_scan_points_overlap(history_indices, indices2, threshold=5):
for indices1 in history_indices: