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 1–25 of 272 papers

TitleStatusHype
Generalizable Humanoid Manipulation with 3D Diffusion PoliciesCode4
fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial IntelligenceCode4
Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language AlignmentCode3
DINO in the Room: Leveraging 2D Foundation Models for 3D SegmentationCode2
Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language ModelCode2
Multimodality Helps Few-Shot 3D Point Cloud Semantic SegmentationCode2
Symbol as Points: Panoptic Symbol Spotting via Point-based RepresentationCode2
OneFormer3D: One Transformer for Unified Point Cloud SegmentationCode2
OctFormer: Octree-based Transformers for 3D Point CloudsCode2
Transformer-Based Visual Segmentation: A SurveyCode2
Point Transformer V2: Grouped Vector Attention and Partition-based PoolingCode2
FEC: Fast Euclidean Clustering for Point Cloud SegmentationCode2
Stratified Transformer for 3D Point Cloud SegmentationCode2
Masked Autoencoders for Point Cloud Self-supervised LearningCode2
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP FrameworkCode2
OpenMaskDINO3D : Reasoning 3D Segmentation via Large Language ModelCode1
Lidar Panoptic Segmentation in an Open WorldCode1
Towards Modality-agnostic Label-efficient Segmentation with Entropy-Regularized Distribution AlignmentCode1
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
ARCH2S: Dataset, Benchmark and Challenges for Learning Exterior Architectural Structures from Point CloudsCode1
3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous DrivingCode1
ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic SegmentationCode1
CurbNet: Curb Detection Framework Based on LiDAR Point Cloud SegmentationCode1
PointCT: Point Central Transformer Network for Weakly-supervised Point Cloud Semantic 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