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 51–75 of 272 papers

TitleStatusHype
HGL: Hierarchical Geometry Learning for Test-time Adaptation in 3D Point Cloud SegmentationCode1
Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud SegmentationCode1
Uplifting Range-View-based 3D Semantic Segmentation in Real-Time with Multi-Sensor Fusion—0
fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial IntelligenceCode4
Pamba: Enhancing Global Interaction in Point Clouds via State Space Model—0
ARCH2S: Dataset, Benchmark and Challenges for Learning Exterior Architectural Structures from Point CloudsCode1
Twin Deformable Point Convolutions for Point Cloud Semantic Segmentation in Remote Sensing Scenes—0
3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous DrivingCode1
Filling Missing Values Matters for Range Image-Based Point Cloud Segmentation—0
Machine Vision-Based Assessment of Fall Color Changes and its Relationship with Leaf Nitrogen Concentration—0
ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic SegmentationCode1
OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views—0
RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation—0
CurbNet: Curb Detection Framework Based on LiDAR Point Cloud SegmentationCode1
EffiPerception: an Efficient Framework for Various Perception Tasks—0
Refining Segmentation On-the-Fly: An Interactive Framework for Point Cloud Semantic Segmentation—0
3DRef: 3D Dataset and Benchmark for Reflection Detection in RGB and Lidar Data—0
Region-Transformer: Self-Attention Region Based Class-Agnostic Point Cloud Segmentation—0
Dynamic Prototype Adaptation with Distillation for Few-shot Point Cloud SegmentationCode0
Symbol as Points: Panoptic Symbol Spotting via Point-based RepresentationCode2
MirageRoom: 3D Scene Segmentation with 2D Pre-trained Models by Mirage Projection—0
Construct to Associate: Cooperative Context Learning for Domain Adaptive Point Cloud Segmentation—0
Multi-modality Affinity Inference for Weakly Supervised 3D Semantic SegmentationCode0
PointCT: Point Central Transformer Network for Weakly-supervised Point Cloud Semantic SegmentationCode1
Point Cloud Segmentation Using Transfer Learning with RandLA-Net: A Case Study on Urban Areas—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