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
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor NavigationCode1
Differentiable Graph Module (DGM) for Graph Convolutional NetworksCode1
Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic SegmentationCode1
Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud SegmentationCode1
BPNet: Bézier Primitive Segmentation on 3D Point CloudsCode1
Generalized Few-Shot Point Cloud Segmentation Via Geometric WordsCode1
ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic SegmentationCode1
AGCN: Adversarial Graph Convolutional Network for 3D Point Cloud SegmentationCode1
DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point CloudsCode1
Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided EnhancementCode1
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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