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 221–230 of 272 papers

TitleStatusHype
Point-to-Pose Voting based Hand Pose Estimation using Residual Permutation Equivariant Layer—0
POIRot: A rotation invariant omni-directional pointnet—0
ProtoGuard-guided PROPEL: Class-Aware Prototype Enhancement and Progressive Labeling for Incremental 3D Point Cloud Segmentation—0
ProtoTransfer: Cross-Modal Prototype Transfer for Point Cloud Segmentation—0
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More—0
PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage—0
Pyramid Architecture for Multi-Scale Processing in Point Cloud Segmentation—0
Refining Segmentation On-the-Fly: An Interactive Framework for Point Cloud Semantic Segmentation—0
Region-Transformer: Self-Attention Region Based Class-Agnostic Point Cloud Segmentation—0
Revisiting 2D Convolutional Neural Networks for Graph-based Applications—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