SOTAVerified

Point Cloud Segmentation

3D point cloud segmentation is the process of classifying point clouds into multiple homogeneous regions, the points in the same region will have the same properties. The segmentation is challenging because of high redundancy, uneven sampling density, and lack explicit structure of point cloud data. This problem has many applications in robotics such as intelligent vehicles, autonomous mapping and navigation.

Source: 3D point cloud segmentation: A survey

Papers

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Showing 231–240 of 272 papers

TitleStatusHype
SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel—0
Few-Shot Learning of Part-Specific Probability Space for 3D Shape Segmentation—0
Fast Geometric Surface based Segmentation of Point Cloud from Lidar Data—0
Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes—0
YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud—0
3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds—0
Indoor Point Cloud Segmentation Using Iterative Gaussian Mapping and Improved Model Fitting—0
Local Model Feature Transformations—0
Photogrammetric point cloud segmentation and object information extraction for creating virtual environments and simulations—0
Point Cloud Segmentation based on Hypergraph Spectral Clustering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OcCo-PCNmean Corruption Error (mCE)1.17—Unverified
2OcCo-PointNetmean Corruption Error (mCE)1.13—Unverified
3PointNet++mean Corruption Error (mCE)1.11—Unverified
4PointTransformersmean Corruption Error (mCE)1.05—Unverified
5PointMLPmean Corruption Error (mCE)0.98—Unverified
6PointMAEmean Corruption Error (mCE)0.93—Unverified
7GDANetmean Corruption Error (mCE)0.92—Unverified
8GDANetmean Corruption Error (mCE)0.89—Unverified