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

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Showing 21–30 of 272 papers

TitleStatusHype
Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave RadarCode1
Generalized Few-Shot Point Cloud Segmentation Via Geometric WordsCode1
3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous DrivingCode1
ARCH2S: Dataset, Benchmark and Challenges for Learning Exterior Architectural Structures from Point CloudsCode1
Clustering based Point Cloud Representation Learning for 3D AnalysisCode1
FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud SegmentationCode1
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud SegmentationCode1
CurbNet: Curb Detection Framework Based on LiDAR Point Cloud SegmentationCode1
Deep Learning for 3D Point Clouds: A SurveyCode1
Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?Code1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OcCo-PCNmean Corruption Error (mCE)1.17—Unverified
2OcCo-PointNetmean Corruption Error (mCE)1.13—Unverified
3PointNet++mean Corruption Error (mCE)1.11—Unverified
4PointTransformersmean Corruption Error (mCE)1.05—Unverified
5PointMLPmean Corruption Error (mCE)0.98—Unverified
6PointMAEmean Corruption Error (mCE)0.93—Unverified
7GDANetmean Corruption Error (mCE)0.92—Unverified
8GDANetmean Corruption Error (mCE)0.89—Unverified