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 3140 of 272 papers

TitleStatusHype
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
Multimodality Helps Few-Shot 3D Point Cloud Semantic SegmentationCode2
Exploring contextual modeling with linear complexity for point cloud segmentation0
Generalizable Humanoid Manipulation with 3D Diffusion PoliciesCode4
Evaluating the Impact of Point Cloud Colorization on Semantic Segmentation Accuracy0
Underground Mapping and Localization Based on Ground-Penetrating Radar0
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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