Fast Sparse 3D Convolution Network with VDB

2023-11-05 · Fangjun Zhou, Anyong Mao, Eftychios Sifakis

We proposed a new Convolution Neural Network implementation optimized for sparse 3D data inference. This implementation uses NanoVDB as the data structure to store the sparse tensor. It leaves a relatively small memory footprint while maintaining high performance. We demonstrate that this architecture is around 20 times faster than the state-of-the-art dense CNN model on a high-resolution 3D object classification network.

📄 PDF Abstract BibTeX arXiv:2311.02762

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3D Object Classification

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