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 181–190 of 272 papers

TitleStatusHype
Revisiting 2D Convolutional Neural Networks for Graph-based Applications—0
Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component ReasoningCode1
Weakly Supervised Pseudo-Label assisted Learning for ALS Point Cloud Semantic Segmentation—0
Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation—0
SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation NetworkCode1
Segmentation of EM showers for neutrino experiments with deep graph neural networksCode0
PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point CloudsCode1
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation—0
LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud SegmentationCode1
Deep Learning Based 3D Segmentation: A Survey—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