𝑓VDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence

The paper introduces 𝑓VDB, a deep-learning framework designed to handle large-scale, sparse 3D data efficiently. It focuses on the IndexGrid structure and specialized GPU-accelerated operators for tasks like convolution, ray tracing, and sampling.

Engineers and specialists can benefit from 𝑓VDB by leveraging its memory-efficient IndexGrid structure and specialized convolution kernels optimized for different sparsity patterns. The framework provides significant speed and memory efficiency improvements over existing frameworks, enabling more effective handling of large-scale, sparse 3D datasets in deep learning applications.

Read full paper: https://arxiv.org/abs/2407.01781

Tags: 3D Vision, Deep Learning, Systems and Performance