  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.
