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 21–30 of 272 papers

TitleStatusHype
3DSES: an indoor Lidar point cloud segmentation dataset with real and pseudo-labels from a 3D model—0
LiDAR-Based Vehicle Detection and Tracking for Autonomous Racing—0
The 2nd Place Solution from the 3D Semantic Segmentation Track in the 2024 Waymo Open Dataset Challenge—0
MRG: A Multi-Robot Manufacturing Digital Scene Generation Method Using Multi-Instance Point Cloud Registration—0
CamPoint: Boosting Point Cloud Segmentation with Virtual Camera—0
Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud Segmentation—0
Generative Hard Example Augmentation for Semantic Point Cloud Segmentation—0
Impact of color and mixing proportion of synthetic point clouds on semantic segmentationCode0
The Bare Necessities: Designing Simple, Effective Open-Vocabulary Scene Graphs—0
Textured As-Is BIM via GIS-informed Point Cloud Segmentation—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