  Recent advances in Neural radiance fields (NeRF) have enabled high-fidelity
scene reconstruction for novel view synthesis. However, NeRF requires hundreds
of network evaluations per pixel to approximate a volume rendering integral,
making it slow to train. Caching NeRFs into explicit data structures can
effectively enhance rendering speed but at the cost of higher memory usage. To
address these issues, we present Hyb-NeRF, a novel neural radiance field with a
multi-resolution hybrid encoding that achieves efficient neural modeling and
fast rendering, which also allows for high-quality novel view synthesis. The
key idea of Hyb-NeRF is to represent the scene using different encoding
strategies from coarse-to-fine resolution levels. Hyb-NeRF exploits
memory-efficiency learnable positional features at coarse resolutions and the
fast optimization speed and local details of hash-based feature grids at fine
resolutions. In addition, to further boost performance, we embed cone
tracing-based features in our learnable positional encoding that eliminates
encoding ambiguity and reduces aliasing artifacts. Extensive experiments on
both synthetic and real-world datasets show that Hyb-NeRF achieves faster
rendering speed with better rending quality and even a lower memory footprint
in comparison to previous state-of-the-art methods.
