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.

翻译：近期神经辐射场（NeRF）的进展使得高保真场景重建用于新视角合成成为可能。然而，NeRF需要对每个像素进行数百次网络评估以近似体积渲染积分，导致训练速度缓慢。将NeRF缓存到显式数据结构中可有效提升渲染速度，但代价是内存占用更高。为解决这些问题，我们提出Hyb-NeRF——一种采用多分辨率混合编码的新型神经辐射场，实现了高效的神经建模与快速渲染，同时支持高质量的新视角合成。Hyb-NeRF的核心思想是利用从粗到细分辨率层级的不同编码策略来表示场景：在粗分辨率下利用内存高效的可学习位置特征，在细分辨率下利用基于哈希特征网格的快速优化速度与局部细节。此外，为进一步提升性能，我们在可学习位置编码中嵌入基于锥体追踪的特征，消除了编码歧义并减少混叠伪影。在合成与真实数据集上的大量实验表明，与先前最先进方法相比，Hyb-NeRF在实现更快渲染速度的同时，具备更优的渲染质量，甚至内存占用更低。