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 3140 of 272 papers

TitleStatusHype
FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud SegmentationCode1
ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic SegmentationCode1
Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud SegmentationCode1
Dynamic Graph CNN for Learning on Point CloudsCode1
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
Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?Code1
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor SceneCode1
Clustering based Point Cloud Representation Learning for 3D AnalysisCode1
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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