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 151–160 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
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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