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 91100 of 272 papers

TitleStatusHype
Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic SegmentationCode1
Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart Grid0
Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided EnhancementCode1
pCTFusion: Point Convolution-Transformer Fusion with Semantic Aware Loss for Outdoor LiDAR Point Cloud SegmentationCode0
Clustering based Point Cloud Representation Learning for 3D AnalysisCode1
See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data0
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
Point2Point : A Framework for Efficient Deep Learning on Hilbert sorted Point Clouds with applications in Spatio-Temporal Occupancy Prediction0
Dynamic Clustering Transformer Network for Point Cloud Segmentation0
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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