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 7180 of 272 papers

TitleStatusHype
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor SceneCode1
ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic SegmentationCode1
Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?Code1
Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud SegmentationCode1
HGL: Hierarchical Geometry Learning for Test-time Adaptation in 3D Point Cloud SegmentationCode1
Learning with Noisy Labels for Robust Point Cloud SegmentationCode1
LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud SegmentationCode1
Point TransformerCode1
Depth-Aware Range Image-Based Model for Point Cloud Segmentation0
Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model0
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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