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 151–160 of 272 papers

TitleStatusHype
Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes—0
WLTCL: Wide Field-of-View 3-D LiDAR Truck Compartment Automatic Localization System—0
YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud—0
Point Attention Network for Semantic Segmentation of 3D Point Clouds—0
Trainable Pointwise Decoder Module for Point Cloud Segmentation—0
2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision—0
3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds—0
3D photogrammetry point cloud segmentation using a model ensembling framework—0
3D point cloud segmentation using GIS—0
3DRef: 3D Dataset and Benchmark for Reflection Detection in RGB and Lidar Data—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