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

Showing 131140 of 272 papers

TitleStatusHype
A Dataset for Analysing Complex Document Layouts in the Digital Humanities and Its Evaluation with Krippendorff’s AlphaCode0
Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud SegmentationCode1
Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental ModelCode0
Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?Code1
FEC: Fast Euclidean Clustering for Point Cloud SegmentationCode2
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR SegmentationCode1
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR SegmentationCode1
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation0
Learning Spatial and Temporal Variations for 4D Point Cloud Segmentation0
PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OcCo-PCNmean Corruption Error (mCE)1.17Unverified
2OcCo-PointNetmean Corruption Error (mCE)1.13Unverified
3PointNet++mean Corruption Error (mCE)1.11Unverified
4PointTransformersmean Corruption Error (mCE)1.05Unverified
5PointMLPmean Corruption Error (mCE)0.98Unverified
6PointMAEmean Corruption Error (mCE)0.93Unverified
7GDANetmean Corruption Error (mCE)0.92Unverified
8GDANetmean Corruption Error (mCE)0.89Unverified