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 2130 of 272 papers

TitleStatusHype
Generalized Few-Shot Point Cloud Segmentation Via Geometric WordsCode1
3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous DrivingCode1
Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave RadarCode1
BPNet: Bézier Primitive Segmentation on 3D Point CloudsCode1
Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?Code1
DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point CloudsCode1
ARCH2S: Dataset, Benchmark and Challenges for Learning Exterior Architectural Structures from Point CloudsCode1
Dense-Resolution Network for Point Cloud Classification and SegmentationCode1
CurbNet: Curb Detection Framework Based on LiDAR Point Cloud SegmentationCode1
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR 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