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 26–50 of 272 papers

TitleStatusHype
ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic SegmentationCode1
Lidar Panoptic Segmentation in an Open WorldCode1
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud SegmentationCode1
GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous VehiclesCode1
Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud SegmentationCode1
Learning with Noisy Labels for Robust Point Cloud SegmentationCode1
LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud SegmentationCode1
Knowledge Distillation from 3D to Bird's-Eye-View for LiDAR Semantic SegmentationCode1
Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?Code1
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor SceneCode1
Differentiable Graph Module (DGM) for Graph Convolutional NetworksCode1
Deep Learning for 3D Point Clouds: A SurveyCode1
Dense-Resolution Network for Point Cloud Classification and SegmentationCode1
DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point CloudsCode1
Clustering based Point Cloud Representation Learning for 3D AnalysisCode1
KPRNet: Improving projection-based LiDAR semantic segmentationCode1
Dynamic Graph CNN for Learning on Point CloudsCode1
Compositional Semantic Mix for Domain Adaptation in Point Cloud SegmentationCode1
ARCH2S: Dataset, Benchmark and Challenges for Learning Exterior Architectural Structures from Point CloudsCode1
Continuous Conditional Random Field Convolution for Point Cloud SegmentationCode1
Contrastive Boundary Learning for Point Cloud SegmentationCode1
CurbNet: Curb Detection Framework Based on LiDAR Point Cloud SegmentationCode1
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR SegmentationCode1
BPNet: Bézier Primitive Segmentation on 3D Point CloudsCode1
FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud SegmentationCode1
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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