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 191–200 of 272 papers

TitleStatusHype
Semantic Context Encoding for Accurate 3D Point Cloud Segmentation—0
Semantic Segmentation of Surface from Lidar Point Cloud—0
Sequential Point Clouds: A Survey—0
Serialized Point Mamba: A Serialized Point Cloud Mamba Segmentation Model—0
Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly—0
Stereo Frustums: A Siamese Pipeline for 3D Object Detection—0
TempNet: Online Semantic Segmentation on Large-Scale Point Cloud Series—0
Textured As-Is BIM via GIS-informed Point Cloud Segmentation—0
The 2nd Place Solution from the 3D Semantic Segmentation Track in the 2024 Waymo Open Dataset Challenge—0
The Bare Necessities: Designing Simple, Effective Open-Vocabulary Scene Graphs—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