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 61–70 of 272 papers

TitleStatusHype
ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic SegmentationCode1
OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views—0
RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation—0
CurbNet: Curb Detection Framework Based on LiDAR Point Cloud SegmentationCode1
EffiPerception: an Efficient Framework for Various Perception Tasks—0
Refining Segmentation On-the-Fly: An Interactive Framework for Point Cloud Semantic Segmentation—0
3DRef: 3D Dataset and Benchmark for Reflection Detection in RGB and Lidar Data—0
Region-Transformer: Self-Attention Region Based Class-Agnostic Point Cloud Segmentation—0
Dynamic Prototype Adaptation with Distillation for Few-shot Point Cloud SegmentationCode0
Symbol as Points: Panoptic Symbol Spotting via Point-based RepresentationCode2
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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