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 171180 of 272 papers

TitleStatusHype
Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregation0
BelHouse3D: A Benchmark Dataset for Assessing Occlusion Robustness in 3D Point Cloud Semantic Segmentation0
Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model0
CamPoint: Boosting Point Cloud Segmentation with Virtual Camera0
Cascaded Non-local Neural Network for Point Cloud Semantic Segmentation0
Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks0
Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation0
Construct to Associate: Cooperative Context Learning for Domain Adaptive Point Cloud Segmentation0
Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data0
Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation0
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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