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 161170 of 272 papers

TitleStatusHype
DRINet++: Efficient Voxel-as-point Point Cloud Segmentation0
Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregation0
False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation0
Continuous Conditional Random Field Convolution for Point Cloud SegmentationCode1
Occlusion-robust Visual Markerless Bone Tracking for Computer-Assisted Orthopaedic Surgery0
3D point cloud segmentation using GIS0
LatticeNet: Fast Spatio-Temporal Point Cloud Segmentation Using Permutohedral Lattices0
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation0
Learning with Noisy Labels for Robust Point Cloud SegmentationCode1
Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds0
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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