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 101–125 of 272 papers

TitleStatusHype
2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision—0
Generative Hard Example Augmentation for Semantic Point Cloud Segmentation—0
Generating synthetic photogrammetric data for training deep learning based 3D point cloud segmentation models—0
Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation—0
Deep FusionNet for Point Cloud Semantic Segmentation—0
Ground Awareness in Deep Learning for Large Outdoor Point Cloud Segmentation—0
Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data—0
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation—0
HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor—0
FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data—0
From CAD models to soft point cloud labels: An automatic annotation pipeline for cheaply supervised 3D semantic segmentation—0
Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations—0
Deep Parametric Continuous Convolutional Neural Networks—0
Improving Graph Representation for Point Cloud Segmentation via Attentive Filtering—0
Indoor Point Cloud Segmentation Using Iterative Gaussian Mapping and Improved Model Fitting—0
From a Point Cloud to a Simulation Model: Bayesian Segmentation and Entropy based Uncertainty Estimation for 3D Modelling—0
Filling Missing Values Matters for Range Image-Based Point Cloud Segmentation—0
Joint 3D Point Cloud Segmentation using Real-Sim Loop: From Panels to Trees and Branches—0
Few-Shot Learning of Part-Specific Probability Space for 3D Shape Segmentation—0
Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network—0
Label-Efficient LiDAR Semantic Segmentation with 2D-3D Vision Transformer Adapters—0
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach—0
Label Name is Mantra: Unifying Point Cloud Segmentation across Heterogeneous Datasets—0
Depth-Aware Range Image-Based Model for Point Cloud Segmentation—0
Construct to Associate: Cooperative Context Learning for Domain Adaptive Point Cloud Segmentation—0
Show:102550
← PrevPage 5 of 11Next →

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