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 51–100 of 272 papers

TitleStatusHype
AGCN: Adversarial Graph Convolutional Network for 3D Point Cloud SegmentationCode1
Continuous Conditional Random Field Convolution for Point Cloud SegmentationCode1
Learning with Noisy Labels for Robust Point Cloud SegmentationCode1
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud SegmentationCode1
DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point CloudsCode1
Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component ReasoningCode1
SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation NetworkCode1
PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point CloudsCode1
LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud SegmentationCode1
FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud SegmentationCode1
Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point CloudCode1
Point TransformerCode1
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor NavigationCode1
Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene CompletionCode1
GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous VehiclesCode1
Unsupervised Point Cloud Pre-Training via Occlusion CompletionCode1
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor SceneCode1
KPRNet: Improving projection-based LiDAR semantic segmentationCode1
Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer LabelsCode1
Dense-Resolution Network for Point Cloud Classification and SegmentationCode1
Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer LabelsCode1
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud SegmentationCode1
Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point CloudsCode1
Differentiable Graph Module (DGM) for Graph Convolutional NetworksCode1
Deep Learning for 3D Point Clouds: A SurveyCode1
Dynamic Graph CNN for Learning on Point CloudsCode1
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric SpaceCode1
PointNet: Deep Learning on Point Sets for 3D Classification and SegmentationCode1
TSDASeg: A Two-Stage Model with Direct Alignment for Interactive Point Cloud Segmentation—0
Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments—0
Point Cloud Segmentation of Agricultural Vehicles using 3D Gaussian Splatting—0
APCoTTA: Continual Test-Time Adaptation for Semantic Segmentation of Airborne LiDAR Point CloudsCode0
WLTCL: Wide Field-of-View 3-D LiDAR Truck Compartment Automatic Localization System—0
3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic GapCode0
ProtoGuard-guided PROPEL: Class-Aware Prototype Enhancement and Progressive Labeling for Incremental 3D Point Cloud Segmentation—0
Robust Unsupervised Domain Adaptation for 3D Point Cloud Segmentation Under Source Adversarial Attacks—0
GeoT: Geometry-guided Instance-dependent Transition Matrix for Semi-supervised Tooth Point Cloud SegmentationCode0
Depth-Aware Range Image-Based Model for Point Cloud Segmentation—0
Deep Unsupervised Segmentation of Log Point Clouds—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
Explainable LiDAR 3D Point Cloud Segmentation and Clustering for Detecting Airplane-Generated Wind Turbulence—0
PFSD: A Multi-Modal Pedestrian-Focus Scene Dataset for Rich Tasks in Semi-Structured EnvironmentsCode0
An Experimental Study of SOTA LiDAR Segmentation Models—0
Ground Awareness in Deep Learning for Large Outdoor Point Cloud Segmentation—0
3DSES: an indoor Lidar point cloud segmentation dataset with real and pseudo-labels from a 3D model—0
LiDAR-Based Vehicle Detection and Tracking for Autonomous Racing—0
The 2nd Place Solution from the 3D Semantic Segmentation Track in the 2024 Waymo Open Dataset Challenge—0
MRG: A Multi-Robot Manufacturing Digital Scene Generation Method Using Multi-Instance Point Cloud Registration—0
Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud Segmentation—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