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 241–250 of 272 papers

TitleStatusHype
OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views—0
PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving—0
PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentation—0
Photogrammetric point cloud segmentation and object information extraction for creating virtual environments and simulations—0
PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentation—0
Point2Point : A Framework for Efficient Deep Learning on Hilbert sorted Point Clouds with applications in Spatio-Temporal Occupancy Prediction—0
Fine-grained Metrics for Point Cloud Semantic Segmentation—0
Point Cloud Segmentation based on Hypergraph Spectral Clustering—0
Point Cloud Segmentation of Agricultural Vehicles using 3D Gaussian Splatting—0
Point cloud segmentation using hierarchical tree for architectural models—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