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 241–250 of 272 papers

TitleStatusHype
GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-wise TransformationsCode0
MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic MonitoringCode0
LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D ImagesCode0
POIRot: A rotation invariant omni-directional pointnet—0
On Universal Equivariant Set NetworksCode0
On the Over-Smoothing Problem of CNN Based Disparity EstimationCode0
IPC-Net: 3D point-cloud segmentation using deep inter-point convolutional layers—0
Point Attention Network for Semantic Segmentation of 3D Point Clouds—0
Learning Propagation for Arbitrarily-structured Data—0
Toward better boundary preserved supervoxel segmentation for 3D point cloudsCode0
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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