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 251260 of 272 papers

TitleStatusHype
T-MAE: Temporal Masked Autoencoders for Point Cloud Representation LearningCode0
Toward better boundary preserved supervoxel segmentation for 3D point cloudsCode0
When 3D Partial Points Meets SAM: Tooth Point Cloud Segmentation with Sparse LabelsCode0
On Adversarial Robustness of Point Cloud Semantic SegmentationCode0
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable TerrainCode0
LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D ImagesCode0
Towards Robust Few-shot Point Cloud Semantic SegmentationCode0
RGCNN: Regularized Graph CNN for Point Cloud SegmentationCode0
LatticeNet: Fast Point Cloud Segmentation Using Permutohedral LatticesCode0
Fast 3D Line Segment Detection From Unorganized Point CloudCode0
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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