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 231–240 of 272 papers

TitleStatusHype
Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation—0
LiDAR-Based Vehicle Detection and Tracking for Autonomous Racing—0
Local Model Feature Transformations—0
Machine Vision-Based Assessment of Fall Color Changes and its Relationship with Leaf Nitrogen Concentration—0
Pamba: Enhancing Global Interaction in Point Clouds via State Space Model—0
MirageRoom: 3D Scene Segmentation with 2D Pre-trained Models by Mirage Projection—0
MRG: A Multi-Robot Manufacturing Digital Scene Generation Method Using Multi-Instance Point Cloud Registration—0
Multiscale Graph Construction Using Non-local Cluster Features—0
Point Cloud Semantic Segmentation using Multi Scale Sparse Convolution Neural Network—0
Occlusion-robust Visual Markerless Bone Tracking for Computer-Assisted Orthopaedic Surgery—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