Feature-space sampling in GCNNs preserves 3D classification accuracy with coarse discretization, enabling precomputation and faster training of equivariant models.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
3DPipe delivers up to 9x faster 3D spatial joins on polyhedra via GPU pipelining, multi-level pruning, and chunked streaming compared to prior GPU methods.
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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3DPipe: A Pipelined GPU Framework for Scalable Generalized Spatial Join over Polyhedral Objects
3DPipe delivers up to 9x faster 3D spatial joins on polyhedra via GPU pipelining, multi-level pruning, and chunked streaming compared to prior GPU methods.