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
ARCH2S: Dataset, Benchmark and Challenges for Learning Exterior Architectural Structures from Point CloudsCode1
3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous DrivingCode1
ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic SegmentationCode1
CurbNet: Curb Detection Framework Based on LiDAR Point Cloud SegmentationCode1
PointCT: Point Central Transformer Network for Weakly-supervised Point Cloud Semantic SegmentationCode1
PointHR: Exploring High-Resolution Architectures for 3D Point Cloud SegmentationCode1
Generalized Few-Shot Point Cloud Segmentation Via Geometric WordsCode1
Compositional Semantic Mix for Domain Adaptation in Point Cloud SegmentationCode1
Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic SegmentationCode1
Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided EnhancementCode1
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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