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 211–220 of 272 papers

TitleStatusHype
PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentation—0
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach—0
Deep Parametric Continuous Convolutional Neural Networks—0
Boundary-Aware Geometric Encoding for Semantic Segmentation of Point CloudsCode0
TempNet: Online Semantic Segmentation on Large-Scale Point Cloud Series—0
Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation—0
Uncertainty Estimation in Deep Neural Networks for Point Cloud Segmentation in Factory Planning—0
PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentation—0
3D photogrammetry point cloud segmentation using a model ensembling framework—0
Stereo Frustums: A Siamese Pipeline for 3D Object Detection—0
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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