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

Showing 5160 of 272 papers

TitleStatusHype
HGL: Hierarchical Geometry Learning for Test-time Adaptation in 3D Point Cloud SegmentationCode1
Serialized Point Mamba: A Serialized Point Cloud Mamba Segmentation Model0
Uplifting Range-View-based 3D Semantic Segmentation in Real-Time with Multi-Sensor Fusion0
fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial IntelligenceCode4
Pamba: Enhancing Global Interaction in Point Clouds via State Space Model0
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 Scenes0
3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous DrivingCode1
Filling Missing Values Matters for Range Image-Based Point Cloud Segmentation0
Machine Vision-Based Assessment of Fall Color Changes and its Relationship with Leaf Nitrogen Concentration0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OcCo-PCNmean Corruption Error (mCE)1.17Unverified
2OcCo-PointNetmean Corruption Error (mCE)1.13Unverified
3PointNet++mean Corruption Error (mCE)1.11Unverified
4PointTransformersmean Corruption Error (mCE)1.05Unverified
5PointMLPmean Corruption Error (mCE)0.98Unverified
6PointMAEmean Corruption Error (mCE)0.93Unverified
7GDANetmean Corruption Error (mCE)0.92Unverified
8GDANetmean Corruption Error (mCE)0.89Unverified