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 171–180 of 272 papers

TitleStatusHype
Effective Early Stopping of Point Cloud Neural Networks—0
A Dataset for Analysing Complex Document Layouts in the Digital Humanities and Its Evaluation with Krippendorff’s AlphaCode0
Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental ModelCode0
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation—0
Learning Spatial and Temporal Variations for 4D Point Cloud Segmentation—0
PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage—0
3D-model ShapeNet Core Classification using Meta-Semantic LearningCode0
Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning—0
Point Cloud Semantic Segmentation using Multi Scale Sparse Convolution Neural Network—0
Sequential Point Clouds: 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