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 241250 of 272 papers

TitleStatusHype
Multi-modality Affinity Inference for Weakly Supervised 3D Semantic SegmentationCode0
PointWeb: Enhancing Local Neighborhood Features for Point Cloud ProcessingCode0
GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-wise TransformationsCode0
Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point CloudsCode0
MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic MonitoringCode0
Rethinking 3D LiDAR Point Cloud SegmentationCode0
Projection-based Point Convolution for Efficient Point Cloud SegmentationCode0
3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic GapCode0
GeoT: Geometry-guided Instance-dependent Transition Matrix for Semi-supervised Tooth Point Cloud SegmentationCode0
3D-model ShapeNet Core Classification using Meta-Semantic LearningCode0
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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