Feature-space sampling in GCNNs preserves 3D classification accuracy with coarse discretization, enabling precomputation and faster training of equivariant models.
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4 Pith papers cite this work. Polarity classification is still indexing.
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EdgeFormer converts point cloud edge detection into local-patch point classification with a transformer and reports competitive results against six baselines.
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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Discretizing Group-Convolutional Neural Networks for 3D Geometry in Feature Space
Feature-space sampling in GCNNs preserves 3D classification accuracy with coarse discretization, enabling precomputation and faster training of equivariant models.
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EdgeFormer: local patch-based edge detection transformer on point clouds
EdgeFormer converts point cloud edge detection into local-patch point classification with a transformer and reports competitive results against six baselines.
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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.