使用COLMAP的相机标定功能: 在COLMAP中,你可以通过执行特征提取(feature_extractor)、特征匹配(如exhaustive_matcher)、稀疏重建(mapper)等步骤来进行相机标定。这些步骤会自动计算并优化相机内参。 bash colmap feature_extractor --database_path database.db --image_path images colmap exhaustive_matcher --database_...
colmap feature_extractor --database_path database.db --image_path images --ImageReader.camera_model PINHOLE colmap exhaustive_matcher --database_path database.db mkdir sparse colmap mapper --database_path database.db --image_path images --output_path sparse mkdir dense colmap image_undistorter ...
图像特征匹配 $colmap exhaustive_matcher --database_path$DATASET_PATH/database.db 其中database_path设置为特征点检测时工程数据文件保存的路径。 相机位姿求解与优化 $mkdir$DATASET_PATH/sparse$colmap mapper \ --database_path$DATASET_PATH/database.db \ --image_path$DATASET_PATH/images \ --output_pa...
RunExhaustiveMatcher (src/colmap/exe/feature.cc) RunExhaustiveMatcher中解析命令行参数,并用根据配置调用CreateExhaustiveFeatureMatcher创建ExhaustiveFeatureMatcher。ExhaustiveFeatureMatcher同样继承自Thread,ExhaustiveFeatureMatcher的Start在RunExhaustiveMatcher中被调用,然后调用虚函数Run。
commands.emplace_back("exhaustive_matcher", &RunExhaustiveMatcher); commands.emplace_back("feature_extractor", &RunFeatureExtractor); commands.emplace_back("feature_importer", &RunFeatureImporter); commands.emplace_back("hierarchical_mapper", &RunHierarchicalMapper); ...
subprocess.run(["colmap","exhaustive_matcher","--database_path","database.db"]) 1. 5. 稀疏重建 特征匹配完成后,可以进行稀疏重建: subprocess.run(["colmap","mapper","--database_path","database.db","--image_path",image_folder,"--output_path","output"]) ...
exhaustive_matcher:针对少量图像(几百张量级),可以获得足够快且最好的重建结果。它将每张图像与其余所有图像进行匹配,不过 block size 可能限制同时加载到内存中的图像数量。 sequential_matcher:针对顺序采集的视频图像,由于相邻帧存在视觉上的重叠且没有必要进行完全匹配,它只匹配视频流中的相邻帧。同时,这种匹配方式...
When we move to a multi-gpu machine, it seems that we are unable to predict the memory usage during the feature extraction phase, typically with the exhaustive matcher. It seems that we are able to increase the number of maximum features up to about 46000 until the exhaustive matcher ...
COLMAP已知相机内外参数重建稀疏稠密模型COLMAP已知相机内外参数重建稀疏/稠密模型 Reconstruct sparse/dense model from known camera poses 1. ⼿动指定所有相机内外参 在⽬录下⼿动新建cameras.txt, images.txt, 和 points3D.txt三个⽂本⽂件，⽬录⽰例：+── %ProjectPath%/created/sparse │ +...
colmap exhaustive_matcher--database_pathdatabase.db 1. 这一步骤会通过基于特征的匹配方法(exhaustive matcher)来查找图像之间的匹配。 5. 稀疏重建 通过图像中匹配的特征来重建相机位姿和稀疏的三维点云。 AI检测代码解析 mkdirsparse colmap mapper--database_pathdatabase.db--image_path/path/to/images--outpu...