diff --git a/CHANGELOG.md b/CHANGELOG.md index 7fb1cf1e1aa..ba4497f10bc 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -51,6 +51,7 @@ - Fix tensor EstimatePointWiseNormalsWithFastEigen3x3 (PR #6980) - Fix alpha shape reconstruction if alpha too small for point scale (PR #6998) - Fix render to depth image on Apple Retina displays (PR #7001) +- Fix infinite loop in segment_plane if num_points < ransac_n (PR #7032) ## 0.13 diff --git a/cpp/open3d/geometry/PointCloudSegmentation.cpp b/cpp/open3d/geometry/PointCloudSegmentation.cpp index 514739f026b..8eb0a4972da 100644 --- a/cpp/open3d/geometry/PointCloudSegmentation.cpp +++ b/cpp/open3d/geometry/PointCloudSegmentation.cpp @@ -29,7 +29,6 @@ class RandomSampler { explicit RandomSampler(const size_t total_size) : total_size_(total_size) {} std::vector operator()(size_t sample_size) { - std::lock_guard lock(mutex_); std::vector samples; samples.reserve(sample_size); @@ -43,13 +42,11 @@ class RandomSampler { valid_sample++; } } - return samples; } private: size_t total_size_; - std::mutex mutex_; }; /// \class RANSACResult @@ -156,21 +153,7 @@ std::tuple> PointCloud::SegmentPlane( utility::LogError("Probability must be > 0 and <= 1.0"); } - RANSACResult result; - - // Initialize the best plane model. - Eigen::Vector4d best_plane_model = Eigen::Vector4d(0, 0, 0, 0); - size_t num_points = points_.size(); - RandomSampler sampler(num_points); - // Pre-generate all random samples before entering the parallel region - std::vector> all_sampled_indices; - all_sampled_indices.reserve(num_iterations); - for (int i = 0; i < num_iterations; i++) { - all_sampled_indices.push_back(sampler(ransac_n)); - } - - // Return if ransac_n is less than the required plane model parameters. if (ransac_n < 3) { utility::LogError( "ransac_n should be set to higher than or equal to 3."); @@ -183,6 +166,17 @@ std::tuple> PointCloud::SegmentPlane( std::vector{}); } + RANSACResult result; + Eigen::Vector4d best_plane_model = Eigen::Vector4d(0, 0, 0, 0); + + RandomSampler sampler(num_points); + // Pre-generate all random samples before entering the parallel region + std::vector> all_sampled_indices; + all_sampled_indices.reserve(num_iterations); + for (int i = 0; i < num_iterations; i++) { + all_sampled_indices.push_back(sampler(ransac_n)); + } + // Use size_t here to avoid large integer which acceed max of int. size_t break_iteration = std::numeric_limits::max(); int iteration_count = 0;