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 151–175 of 272 papers

TitleStatusHype
Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network—0
Few-Shot Learning of Part-Specific Probability Space for 3D Shape Segmentation—0
Filling Missing Values Matters for Range Image-Based Point Cloud Segmentation—0
From a Point Cloud to a Simulation Model: Bayesian Segmentation and Entropy based Uncertainty Estimation for 3D Modelling—0
From CAD models to soft point cloud labels: An automatic annotation pipeline for cheaply supervised 3D semantic segmentation—0
FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data—0
Generating synthetic photogrammetric data for training deep learning based 3D point cloud segmentation models—0
Generative Hard Example Augmentation for Semantic Point Cloud Segmentation—0
GeoSpark: Sparking up Point Cloud Segmentation with Geometry Clue—0
Ground Awareness in Deep Learning for Large Outdoor 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
Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud Segmentation—0
Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations—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
IPC-Net: 3D point-cloud segmentation using deep inter-point convolutional layers—0
Joint 3D Point Cloud Segmentation using Real-Sim Loop: From Panels to Trees and Branches—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
LatticeNet: Fast Spatio-Temporal Point Cloud Segmentation Using Permutohedral Lattices—0
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
Show:102550
← PrevPage 7 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