Fast Sparse 3D Convolution Network with VDB

• URL: http://arxiv.org/abs/2311.02762v2
• Date: Wed, 15 Nov 2023 04:38:09 GMT
• Title: Fast Sparse 3D Convolution Network with VDB
• Authors: Fangjun Zhou, Anyong Mao, Eftychios Sifakis
• Abstract summary: 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. 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.
• Score: 2.834312349049142
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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