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 251–260 of 272 papers

TitleStatusHype
Point Attention Network for Semantic Segmentation of 3D Point Clouds—0
Learning Propagation for Arbitrarily-structured Data—0
Toward better boundary preserved supervoxel segmentation for 3D point cloudsCode0
Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation—0
A Unified Point-Based Framework for 3D SegmentationCode0
PointNLM: Point Nonlocal-Means for vegetation segmentation based on middle echo point clouds—0
PointWeb: Enhancing Local Neighborhood Features for Point Cloud ProcessingCode0
Oriented Point Sampling for Plane Detection in Unorganized Point CloudsCode0
Fast 3D Line Segment Detection From Unorganized Point CloudCode0
Point-to-Pose Voting based Hand Pose Estimation using Residual Permutation Equivariant Layer—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