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 31–40 of 272 papers

TitleStatusHype
Clustering based Point Cloud Representation Learning for 3D AnalysisCode1
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
Knowledge Distillation from 3D to Bird's-Eye-View for LiDAR Semantic SegmentationCode1
Spatiotemporal Self-supervised Learning for Point Clouds in the WildCode1
Position-Guided Point Cloud Panoptic Segmentation TransformerCode1
Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud SegmentationCode1
Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor SceneCode1
Zero-Shot Point Cloud Segmentation by Semantic-Visual Aware SynthesisCode1
Robust Point Cloud Segmentation with Noisy AnnotationsCode1
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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