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 76–100 of 272 papers

TitleStatusHype
T-MAE: Temporal Masked Autoencoders for Point Cloud Representation LearningCode0
Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation—0
Densify Your Labels: Unsupervised Clustering with Bipartite Matching for Weakly Supervised Point Cloud Segmentation—0
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More—0
Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language AlignmentCode3
OneFormer3D: One Transformer for Unified Point Cloud SegmentationCode2
DatasetNeRF: Efficient 3D-aware Data Factory with Generative Radiance FieldsCode0
U3DS^3: Unsupervised 3D Semantic Scene Segmentation—0
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation—0
2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision—0
PointHR: Exploring High-Resolution Architectures for 3D Point Cloud SegmentationCode1
Addressing Data Misalignment in Image-LiDAR Fusion on Point Cloud Segmentation—0
Generalized Few-Shot Point Cloud Segmentation Via Geometric WordsCode1
Towards Robust Few-shot Point Cloud Semantic SegmentationCode0
Compositional Semantic Mix for Domain Adaptation in Point Cloud SegmentationCode1
Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic SegmentationCode1
Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart Grid—0
Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided EnhancementCode1
pCTFusion: Point Convolution-Transformer Fusion with Semantic Aware Loss for Outdoor LiDAR Point Cloud SegmentationCode0
Clustering based Point Cloud Representation Learning for 3D AnalysisCode1
See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data—0
Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave RadarCode1
BPNet: Bézier Primitive Segmentation on 3D Point CloudsCode1
Point2Point : A Framework for Efficient Deep Learning on Hilbert sorted Point Clouds with applications in Spatio-Temporal Occupancy Prediction—0
Dynamic Clustering Transformer Network for 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