RWoDSN extracts feature points from point clouds via a novel DSN descriptor and random walk graph analysis, reporting 22% higher recall than prior state-of-the-art with 0.784 precision.
Deep learning for 3d point clouds: A survey
3 Pith papers cite this work. Polarity classification is still indexing.
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Gaussian and related cropping strategies for point cloud subclouds improve 3D neural network performance over spherical cropping on large outdoor scenes.
A systematic literature survey that categorizes deep learning architectures for point cloud classification, part segmentation, and semantic segmentation, evaluates them on benchmarks, and discusses innovations, limitations, and future directions.
citing papers explorer
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Random Walk on Point Clouds for Feature Detection
RWoDSN extracts feature points from point clouds via a novel DSN descriptor and random walk graph analysis, reporting 22% higher recall than prior state-of-the-art with 0.784 precision.
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From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments
Gaussian and related cropping strategies for point cloud subclouds improve 3D neural network performance over spherical cropping on large outdoor scenes.
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A Systematic Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation
A systematic literature survey that categorizes deep learning architectures for point cloud classification, part segmentation, and semantic segmentation, evaluates them on benchmarks, and discusses innovations, limitations, and future directions.