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 126–150 of 272 papers

TitleStatusHype
Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations—0
Zero-shot point cloud segmentation by transferring geometric primitivesCode0
Point Transformer V2: Grouped Vector Attention and Partition-based PoolingCode2
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud SegmentationCode1
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
Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud SegmentationCode1
Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental ModelCode0
Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?Code1
FEC: Fast Euclidean Clustering for Point Cloud SegmentationCode2
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR SegmentationCode1
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR SegmentationCode1
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation—0
Learning Spatial and Temporal Variations for 4D Point Cloud Segmentation—0
PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage—0
3D-model ShapeNet Core Classification using Meta-Semantic LearningCode0
SemAffiNet: Semantic-Affine Transformation for Point Cloud SegmentationCode1
Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning—0
Point Cloud Semantic Segmentation using Multi Scale Sparse Convolution Neural Network—0
Sequential Point Clouds: A Survey—0
Stratified Transformer for 3D Point Cloud SegmentationCode2
Masked Autoencoders for Point Cloud Self-supervised LearningCode2
Contrastive Boundary Learning for Point Cloud SegmentationCode1
RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep LearningCode1
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP FrameworkCode2
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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