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 5160 of 272 papers

TitleStatusHype
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR SegmentationCode1
OpenMaskDINO3D : Reasoning 3D Segmentation via Large Language ModelCode1
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point ModelingCode1
GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous VehiclesCode1
Deep Learning for 3D Point Clouds: A SurveyCode1
Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic SegmentationCode1
Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud SegmentationCode1
Dense-Resolution Network for Point Cloud Classification and SegmentationCode1
Generalized Few-Shot Point Cloud Segmentation Via Geometric WordsCode1
AGCN: Adversarial Graph Convolutional Network for 3D 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