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 131–140 of 272 papers

TitleStatusHype
Deep Unsupervised Segmentation of Log Point Clouds—0
Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds—0
Densify Your Labels: Unsupervised Clustering with Bipartite Matching for Weakly Supervised Point Cloud Segmentation—0
Depth-Aware Range Image-Based Model for Point Cloud Segmentation—0
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation—0
DRINet++: Efficient Voxel-as-point Point Cloud Segmentation—0
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation—0
Dynamic Clustering Transformer Network for Point Cloud Segmentation—0
Effective Early Stopping of Point Cloud Neural Networks—0
Effective Utilisation of Multiple Open-Source Datasets to Improve Generalisation Performance of Point Cloud Segmentation Models—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