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

Showing 1120 of 272 papers

TitleStatusHype
FEC: Fast Euclidean Clustering for Point Cloud SegmentationCode2
Symbol as Points: Panoptic Symbol Spotting via Point-based RepresentationCode2
OctFormer: Octree-based Transformers for 3D Point CloudsCode2
Multimodality Helps Few-Shot 3D Point Cloud Semantic SegmentationCode2
Stratified Transformer for 3D Point Cloud SegmentationCode2
Dense-Resolution Network for Point Cloud Classification and SegmentationCode1
Deep Learning for 3D Point Clouds: A SurveyCode1
Differentiable Graph Module (DGM) for Graph Convolutional NetworksCode1
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR SegmentationCode1
Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave RadarCode1
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Benchmark Results

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