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
Differentiable Graph Module (DGM) for Graph Convolutional NetworksCode1
Deep Learning for 3D Point Clouds: A SurveyCode1
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR SegmentationCode1
GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous VehiclesCode1
Continuous Conditional Random Field Convolution for Point Cloud SegmentationCode1
Compositional Semantic Mix for Domain Adaptation in Point Cloud SegmentationCode1
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
CurbNet: Curb Detection Framework Based on LiDAR Point Cloud SegmentationCode1
Contrastive Boundary Learning for Point Cloud SegmentationCode1
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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