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 71–80 of 272 papers

TitleStatusHype
Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer LabelsCode1
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud SegmentationCode1
Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point CloudsCode1
Differentiable Graph Module (DGM) for Graph Convolutional NetworksCode1
Deep Learning for 3D Point Clouds: A SurveyCode1
Dynamic Graph CNN for Learning on Point CloudsCode1
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric SpaceCode1
PointNet: Deep Learning on Point Sets for 3D Classification and SegmentationCode1
TSDASeg: A Two-Stage Model with Direct Alignment for Interactive Point Cloud Segmentation—0
Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments—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