Modeling 3D scenes by volumetric feature grids is one of the promising
directions of neural approximations to improve Neural Radiance Fields (NeRF).
Instant-NGP (INGP) introduced multi-resolution hash encoding from a lookup
table of trainable feature grids which enabled learning high-quality neural
graphics primitives in a matter of seconds. However, this improvement came at
the cost of higher storage size. In this paper, we address this challenge by
introducing instant learning of compression-aware NeRF features (CAwa-NeRF),
that allows exporting the zip compressed feature grids at the end of the model
training with a negligible extra time overhead without changing neither the
storage architecture nor the parameters used in the original INGP paper.
Nonetheless, the proposed method is not limited to INGP but could also be
adapted to any model. By means of extensive simulations, our proposed instant
learning pipeline can achieve impressive results on different kinds of static
scenes such as single object masked background scenes and real-life scenes
captured in our studio. In particular, for single object masked background
scenes CAwa-NeRF compresses the feature grids down to 6% (1.2 MB) of the
original size without any loss in the PSNR (33 dB) or down to 2.4% (0.53 MB)
with a slight virtual loss (32.31 dB).

通过引入压缩感知型 NeRF 特征，该研究实现了在模型训练结束时导出经过压缩的特征网格，极大地减小了存储空间占用，而且不需要改变存储架构或原始 INGP 论文中的参数。