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 51–60 of 272 papers

TitleStatusHype
AGCN: Adversarial Graph Convolutional Network for 3D Point Cloud SegmentationCode1
Continuous Conditional Random Field Convolution for Point Cloud SegmentationCode1
Learning with Noisy Labels for Robust Point Cloud SegmentationCode1
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud SegmentationCode1
DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point CloudsCode1
Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component ReasoningCode1
SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation NetworkCode1
PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point CloudsCode1
LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud SegmentationCode1
FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud SegmentationCode1
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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