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 4150 of 272 papers

TitleStatusHype
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud SegmentationCode1
Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud SegmentationCode1
Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?Code1
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR SegmentationCode1
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR SegmentationCode1
SemAffiNet: Semantic-Affine Transformation for Point Cloud SegmentationCode1
Contrastive Boundary Learning for Point Cloud SegmentationCode1
RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep LearningCode1
PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud SegmentationCode1
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point ModelingCode1
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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