Compressing Explicit Voxel Grid Representations: fast NeRFs become also small
This work addresses the memory inefficiency problem for users of fast NeRF models, offering an incremental improvement in compression.
The paper tackles the high memory cost of explicit voxel grid NeRFs by proposing Re:NeRF, a compression method that reduces storage while maintaining comparable performance, as demonstrated on four benchmarks with three architectures.
NeRFs have revolutionized the world of per-scene radiance field reconstruction because of their intrinsic compactness. One of the main limitations of NeRFs is their slow rendering speed, both at training and inference time. Recent research focuses on the optimization of an explicit voxel grid (EVG) that represents the scene, which can be paired with neural networks to learn radiance fields. This approach significantly enhances the speed both at train and inference time, but at the cost of large memory occupation. In this work we propose Re:NeRF, an approach that specifically targets EVG-NeRFs compressibility, aiming to reduce memory storage of NeRF models while maintaining comparable performance. We benchmark our approach with three different EVG-NeRF architectures on four popular benchmarks, showing Re:NeRF's broad usability and effectiveness.