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 201–210 of 272 papers

TitleStatusHype
Tinto: Multisensor Benchmark for 3D Hyperspectral Point Cloud Segmentation in the Geosciences—0
Towards Cross-device and Training-free Robotic Grasping in 3D Open World—0
Traffic Sign Timely Visual Recognizability Evaluation Based on 3D Measurable Point Clouds—0
Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation—0
TSDASeg: A Two-Stage Model with Direct Alignment for Interactive Point Cloud Segmentation—0
Twin Deformable Point Convolutions for Point Cloud Semantic Segmentation in Remote Sensing Scenes—0
U3DS^3: Unsupervised 3D Semantic Scene Segmentation—0
UAV LiDAR Point Cloud Segmentation of A Stack Interchange with Deep Neural Networks—0
Uncertainty Estimation in Deep Neural Networks for Point Cloud Segmentation in Factory Planning—0
Underground Mapping and Localization Based on Ground-Penetrating Radar—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