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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 226–250 of 272 papers

TitleStatusHype
Learning from Mistakes: Self-Regularizing Hierarchical Representations in Point Cloud Semantic Segmentation—0
Learning Latent Part-Whole Hierarchies for Point Clouds—0
Learning Propagation for Arbitrarily-structured Data—0
Learning Spatial and Temporal Variations for 4D Point Cloud Segmentation—0
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation—0
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
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