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 151–175 of 272 papers

TitleStatusHype
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
Tinto: Multisensor Benchmark for 3D Hyperspectral Point Cloud Segmentation in the Geosciences—0
Urban GeoBIM construction by integrating semantic LiDAR point clouds with as-designed BIM models—0
Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network—0
Label Name is Mantra: Unifying Point Cloud Segmentation across Heterogeneous Datasets—0
GeoSpark: Sparking up Point Cloud Segmentation with Geometry Clue—0
From CAD models to soft point cloud labels: An automatic annotation pipeline for cheaply supervised 3D semantic segmentation—0
Learning from Mistakes: Self-Regularizing Hierarchical Representations in Point Cloud Semantic Segmentation—0
Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data—0
SAT: Size-Aware Transformer for 3D Point Cloud Semantic Segmentation—0
Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly—0
Improving Graph Representation for Point Cloud Segmentation via Attentive Filtering—0
ProtoTransfer: Cross-Modal Prototype Transfer for Point Cloud Segmentation—0
Adversarially Masking Synthetic To Mimic Real: Adaptive Noise Injection for Point Cloud Segmentation Adaptation—0
Effective Utilisation of Multiple Open-Source Datasets to Improve Generalisation Performance of Point Cloud Segmentation Models—0
PointResNet: Residual Network for 3D Point Cloud Segmentation and Classification—0
Learning Latent Part-Whole Hierarchies for Point Clouds—0
Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations—0
Zero-shot point cloud segmentation by transferring geometric primitivesCode0
Effective Early Stopping of Point Cloud Neural Networks—0
A Dataset for Analysing Complex Document Layouts in the Digital Humanities and Its Evaluation with Krippendorff’s AlphaCode0
Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental ModelCode0
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation—0
Learning Spatial and Temporal Variations for 4D 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