Supervoxel for 3D point clouds
Introduction
We present a simple but effective supervoxel segmentation method for point clouds, which formalizes supervoxel segmentation as a subset selection problem. We develop an heuristic algorithm that utilizes local information to efficiently solve the subset selection problem. The proposed method can produce supervoxels with adaptive resolutions, and dose not rely the selection of seed points. The method is fully tested on three publicly available point cloud segmentation benchmarks, which cover the major point cloud types. The experimental results show that compared with the state-of-the-art supervoxel segmentation methods, the supervoxels extracted using our method preserve the object boundaries and small structures more effectively, which is reflected in a higher boundary recall and lower under-segmentation error.
The details can be found in the following ISPRS 2018 paper
Citing our work
If you find our works useful in your research, please consider citing:
Lin Y, Wang C, Zhai D, W Li, and J Li. Toward better boundary preserved supervoxel segmentation for 3D point clouds. Isprs Journal of Photogrammetry & Remote Sensing, vol. 143, pages 39-47, 2018.
BibTex
@article{Lin2018Supervoxel,
title = "Toward better boundary preserved supervoxel segmentation for 3D point clouds",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
volume = "143",
pages = "39 - 47",
year = "2018",
note = "ISPRS Journal of Photogrammetry and Remote Sensing Theme Issue “Point Cloud Processing”",
issn = "0924-2716",
doi = "https://doi.org/10.1016/j.isprsjprs.2018.05.004",
url = "http://www.sciencedirect.com/science/article/pii/S0924271618301370",
author = "Lin, Yangbin and Wang, Cheng and Zhai, Dawei and Li, Wei and Li, Jonathan",
keywords = "Supervoxel segmentation, Point clouds, Subset selection, Over-segmentation"
}
Install & complie
Please directly copy the code into your workspace and complie it with any complier that supports C++11. It dose not require linking any additional libraries.
Sample usage:
cl::geometry::point_cloud::SupervoxelSegmentation(points, neighbors, resolution, metric, &supervoxels, &labels);
Where, ‘points’ is the input 3D point cloud. It can be read from XYZ file by calling:
cl::geometry::io::ReadXYZPoints(filename.c_str(), &points);
‘neighbors’ gives the neighborhood for each point. It can be constrcuted by computing k-neareast neighbors of each point. For example:
const int k_neighbors = 15;
cl::Array<cl::Array<int> > neighbors(n_points);
cl::Array<cl::RPoint3D> neighbor_points(k_neighbors);
for (int i = 0; i < n_points; ++i) {
kdtree.FindKNearestNeighbors(kdtree.points()[i], k_neighbors, &neighbors[i]);
}
‘resolution’ is used to determine the number of supervoxels you want.
‘metric’ is used to evaluate the feature distance between two points. In our paper, we use the following metric, which is same to the VCCS.
class VCCSMetric {
public:
explicit VCCSMetric(double resolution)
: resolution_(resolution) {}
double operator() (const PointWithNormal& p1,
const PointWithNormal& p2) const {
return 1.0 - std::fabs(p1.normal * p2.normal) +
cl::geometry::Distance(p1, p2) / resolution_ * 0.4;
}
private:
double resolution_;
};
The output ‘supervoxels’ is an array that stores the indices of the representation points.
And ‘labels’ is used to denote which supervoxel owns the i-th point.
Please see main.cc for more details.
The file “test.xyz” can be found in test_data.
Comparison
For comparison, we also provide our implementation of VCCS and its variant (See main.cc for more details).
Please feel free to use it.
Sample results.
The first column is the orignal point cloud with ground-truth annotation. The second column is the supervoxel segmentation by VCCS (found in vccs_supervoxel.h) . The third column is the VCCS method with kNN variation (found in vccs_knn_supervoxel.h). And the last column is the result obtained by our method.
Please feel free to leave suggestions or comments to Dr. Lin ([email protected]), or Prof. Wang ([email protected])
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