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37ee115 cac424e 37ee115 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 | #!/usr/bin/env python3
"""
Benchmark 3DMatch pairs using Open3D
"""
import json
import sys
import csv
import time
import argparse
from pathlib import Path
from tqdm import tqdm
try:
import open3d as o3d
import numpy as np
except ImportError as e:
print(f"Error: Required packages not installed: {e}")
print("Install with: pip install open3d numpy")
sys.exit(1)
def load_point_cloud(path):
"""Load PLY point cloud"""
pcd = o3d.io.read_point_cloud(str(path))
return pcd
def preprocess_point_cloud(pcd, voxel_size):
"""Preprocess point cloud"""
# Remove non-finite points
pcd_clean = o3d.geometry.PointCloud()
pcd_clean.points = o3d.utility.Vector3dVector(
np.asarray(pcd.points)[~np.any(~np.isfinite(np.asarray(pcd.points)), axis=1)]
)
if pcd.has_normals():
pcd_clean.normals = o3d.utility.Vector3dVector(
np.asarray(pcd.normals)[~np.any(~np.isfinite(np.asarray(pcd.points)), axis=1)]
)
# Remove duplicates
pcd_clean.remove_duplicated_points()
# Downsample
pcd_down = pcd_clean.voxel_down_sample(voxel_size)
return pcd_down, len(pcd_clean.points), len(pcd_down.points)
def compute_fpfh(pcd, voxel_size):
"""Compute FPFH features"""
if len(pcd.points) < 10:
return None
pcd.estimate_normals(
o3d.geometry.KDTreeSearchParamRadius(radius=voxel_size * 2.0)
)
fpfh = o3d.pipelines.registration.compute_fpfh_feature(
pcd,
o3d.geometry.KDTreeSearchParamRadius(radius=voxel_size * 5.0)
)
return fpfh
def global_registration(source, target, source_fpfh, target_fpfh, voxel_size):
"""RANSAC global registration"""
distance_threshold = voxel_size * 1.5
result = o3d.pipelines.registration.registration_ransac_based_on_feature_matching(
source, target, source_fpfh, target_fpfh,
mutual_filter=False,
max_correspondence_distance=distance_threshold,
estimation_method=o3d.pipelines.registration.TransformationEstimationPointToPoint(False),
ransac_n=3,
checkers=[
o3d.pipelines.registration.CorrespondenceCheckerBasedOnEdgeLength(0.9),
o3d.pipelines.registration.CorrespondenceCheckerBasedOnDistance(distance_threshold)
],
criteria=o3d.pipelines.registration.RANSACConvergenceCriteria(50000, 0.999)
)
return result
def local_registration(source, target, init_transform, voxel_size):
"""ICP local registration"""
distance_threshold = voxel_size * 0.4
result = o3d.pipelines.registration.registration_icp(
source, target,
max_correspondence_distance=distance_threshold,
init=init_transform,
criteria=o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=50),
estimation_method=o3d.pipelines.registration.TransformationEstimationPointToPlane()
)
return result
def benchmark_pair(pair, voxel_size):
"""Benchmark a single pair"""
result = {
'scene_id': pair.get('scene_id'),
'source_id': pair.get('source_id'),
'target_id': pair.get('target_id'),
'source_path': pair.get('source_path'),
'target_path': pair.get('target_path'),
'source_points': 0,
'target_points': 0,
'source_down_points': 0,
'target_down_points': 0,
'ransac_fitness': 0.0,
'ransac_rmse': 0.0,
'icp_fitness': 0.0,
'icp_rmse': 0.0,
'runtime_seconds': 0.0,
'status': 'failed',
'error_message': ''
}
try:
start_time = time.time()
# Load point clouds
source = load_point_cloud(pair['source_path'])
target = load_point_cloud(pair['target_path'])
result['source_points'] = len(source.points)
result['target_points'] = len(target.points)
if result['source_points'] < 100 or result['target_points'] < 100:
result['error_message'] = 'Too few points'
result['runtime_seconds'] = time.time() - start_time
return result
# Preprocess
source_down, _, source_down_count = preprocess_point_cloud(source, voxel_size)
target_down, _, target_down_count = preprocess_point_cloud(target, voxel_size)
result['source_down_points'] = source_down_count
result['target_down_points'] = target_down_count
if source_down_count < 50 or target_down_count < 50:
result['error_message'] = 'Too few points after downsampling'
result['runtime_seconds'] = time.time() - start_time
return result
# Compute features
source_fpfh = compute_fpfh(source_down, voxel_size)
target_fpfh = compute_fpfh(target_down, voxel_size)
if source_fpfh is None or target_fpfh is None:
result['error_message'] = 'Failed to compute FPFH'
result['runtime_seconds'] = time.time() - start_time
return result
# RANSAC
ransac_result = global_registration(source_down, target_down, source_fpfh, target_fpfh, voxel_size)
result['ransac_fitness'] = ransac_result.fitness
result['ransac_rmse'] = ransac_result.inlier_rmse
# ICP
icp_result = local_registration(source_down, target_down, ransac_result.transformation, voxel_size)
result['icp_fitness'] = icp_result.fitness
result['icp_rmse'] = icp_result.inlier_rmse
result['status'] = 'success'
result['runtime_seconds'] = time.time() - start_time
except Exception as e:
result['error_message'] = str(e)
result['runtime_seconds'] = time.time() - start_time
return result
def main():
parser = argparse.ArgumentParser(description='Benchmark 3DMatch pairs')
parser.add_argument('--pair_index', required=True, help='Path to pair_index.json')
parser.add_argument('--output_csv', required=True, help='Output CSV file')
parser.add_argument('--output_json', required=True, help='Output JSON file')
parser.add_argument('--voxel_size', type=float, default=0.05, help='Voxel size for downsampling')
parser.add_argument('--max_pairs_per_scene', type=int, default=50, help='Max pairs per scene')
args = parser.parse_args()
# Load pair index
with open(args.pair_index) as f:
all_pairs = json.load(f)
print(f"Loaded {len(all_pairs)} pairs")
# Limit pairs per scene
scene_count = {}
pairs = []
for pair in all_pairs:
scene_id = pair['scene_id']
if scene_count.get(scene_id, 0) < args.max_pairs_per_scene:
pairs.append(pair)
scene_count[scene_id] = scene_count.get(scene_id, 0) + 1
print(f"Benchmarking {len(pairs)} pairs (max {args.max_pairs_per_scene} per scene)")
# Benchmark
results = []
for pair in tqdm(pairs, desc='Benchmarking'):
result = benchmark_pair(pair, args.voxel_size)
results.append(result)
# Save results
Path(args.output_csv).parent.mkdir(parents=True, exist_ok=True)
Path(args.output_json).parent.mkdir(parents=True, exist_ok=True)
# CSV
with open(args.output_csv, 'w', newline='') as f:
writer = csv.DictWriter(f, fieldnames=results[0].keys() if results else [])
writer.writeheader()
writer.writerows(results)
# JSON
with open(args.output_json, 'w') as f:
json.dump(results, f, indent=2)
# Summary
success_count = sum(1 for r in results if r['status'] == 'success')
print(f"\nResults saved to {args.output_csv} and {args.output_json}")
print(f"Success: {success_count}/{len(results)}")
if __name__ == '__main__':
main()
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