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 126–150 of 272 papers

TitleStatusHype
Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation—0
Deep FusionNet for Point Cloud Semantic Segmentation—0
Deep Learning Based 3D Segmentation: A Survey—0
Deep-learning-based classification and retrieval of components of a process plant from segmented point clouds—0
Deep Parametric Continuous Convolutional Neural Networks—0
Deep Unsupervised Segmentation of Log Point Clouds—0
Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds—0
Densify Your Labels: Unsupervised Clustering with Bipartite Matching for Weakly Supervised Point Cloud Segmentation—0
Depth-Aware Range Image-Based Model for Point Cloud Segmentation—0
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation—0
DRINet++: Efficient Voxel-as-point Point Cloud Segmentation—0
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation—0
Dynamic Clustering Transformer Network for Point Cloud Segmentation—0
Effective Early Stopping of Point Cloud Neural Networks—0
Effective Utilisation of Multiple Open-Source Datasets to Improve Generalisation Performance of Point Cloud Segmentation Models—0
Efficiently Expanding Receptive Fields: Local Split Attention and Parallel Aggregation for Enhanced Large-scale Point Cloud Semantic Segmentation—0
EffiPerception: an Efficient Framework for Various Perception Tasks—0
Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments—0
ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation—0
Evaluating the Impact of Point Cloud Colorization on Semantic Segmentation Accuracy—0
Explainable LiDAR 3D Point Cloud Segmentation and Clustering for Detecting Airplane-Generated Wind Turbulence—0
Exploring contextual modeling with linear complexity for point cloud segmentation—0
Extracting Contact and Motion from Manipulation Videos—0
False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation—0
Fast Geometric Surface based Segmentation of Point Cloud from Lidar Data—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