Neural radiance field (NeRF) has shown remarkable performance in generating photo-realistic novel views. Among recent NeRF related research, the approaches that involve the utilization of explicit structures like grids to manage features achieve exceptionally fast training by reducing the complexity of multilayer perceptron (MLP) networks. However, storing features in dense grids demands a substantial amount of memory space, resulting in a notable memory bottleneck within computer system. Consequently, it leads to a significant increase in training times without prior hyper-parameter tuning. To address this issue, in this work, we are the first to propose MF-NeRF, a memory-efficient NeRF framework that employs a Mixed-Feature hash table to improve memory efficiency and reduce training time while maintaining reconstruction quality. Specifically, we first design a mixed-feature hash encoding to adaptively mix part of multi-level feature grids and map it to a single hash table. Following that, in order to obtain the correct index of a grid point, we further develop an index transformation method that transforms indices of an arbitrary level grid to those of a canonical grid. Extensive experiments benchmarking with state-of-the-art Instant-NGP, TensoRF, and DVGO, indicate our MF-NeRF could achieve the fastest training time on the same GPU hardware with similar or even higher reconstruction quality.

翻译：神经辐射场（NeRF）在生成逼真的新视角方面表现出色。在近期NeRF相关研究中，利用网格等显式结构管理特征的方法通过降低多层感知器（MLP）网络复杂度实现了极快的训练速度。然而，在密集网格中存储特征需要大量内存空间，导致计算机系统出现显著的内存瓶颈。这进而导致在没有超参数预调优的情况下训练时间显著增加。为解决该问题，本文首次提出MF-NeRF——一种采用混合特征（Mixed-Feature）哈希表的高内存效率NeRF框架，通过提升内存效率、减少训练时间的同时保持重建质量。具体而言，我们首先设计了一种混合特征哈希编码（mixed-feature hash encoding），自适应地混合部分多级特征网格并映射至单一哈希表。随后，为获取网格点的正确索引，我们进一步开发了一种索引变换方法，将任意层级网格的索引转换为规范网格的索引。与当前最先进的Instant-NGP、TensoRF及DVGO进行的大量基准测试表明，我们的MF-NeRF在同一GPU硬件上可实现最快的训练速度，同时保持相似甚至更优的重建质量。