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.

翻译：我们提出了一种针对稀疏3D数据推理优化的新型卷积神经网络实现。该实现采用NanoVDB作为存储稀疏张量的数据结构，在保持高性能的同时，占用相对较小的内存空间。我们证明，该架构在高分辨率3D物体分类网络上的运行速度比当前最先进的密集CNN模型快约20倍。