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.

Hyb-NeRF 是一种新颖的神经辐射场，具有多分辨率混合编码，实现了高效的神经建模和快速渲染，同时允许高质量的新视角合成。它通过从粗到细的分辨率级别使用不同的编码策略来表示场景，并利用可学习的位置特征以及基于哈希的特征网格的快速优化速度和局部细节。此外，为了进一步提高性能，还将锥形跟踪特征嵌入到可学习的位置编码中，消除了编码的歧义性并减少了走样伪影。广泛的合成和实际数据集实验证明，与以往最先进方法相比，Hyb-NeRF 在更快的渲染速度、更好的渲染质量和更低的内存占用方面取得了优势。