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 101110 of 272 papers

TitleStatusHype
Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud Segmentation0
CamPoint: Boosting Point Cloud Segmentation with Virtual Camera0
Impact of color and mixing proportion of synthetic point clouds on semantic segmentationCode0
The Bare Necessities: Designing Simple, Effective Open-Vocabulary Scene Graphs0
Textured As-Is BIM via GIS-informed Point Cloud Segmentation0
Towards Cross-device and Training-free Robotic Grasping in 3D Open World0
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
Multiscale Graph Construction Using Non-local Cluster Features0
Automated Image-Based Identification and Consistent Classification of Fire Patterns with Quantitative Shape Analysis and Spatial Location Identification0
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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