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Recent advances in Neural radiance fields (NeRF) have enabled high-fidelityscene reconstruction for novel view synthesis. However, NeRF requires hundredsof network evaluations per pixel to approximate a volume rendering integral,making it slow to train. Caching NeRFs into explicit data structures caneffectively enhance rendering speed but at the cost of higher memory usage. Toaddress these issues, we present Hyb-NeRF, a novel neural radiance field with amulti-resolution hybrid encoding that achieves efficient neural modeling andfast rendering, which also allows for high-quality novel view synthesis. Thekey idea of Hyb-NeRF is to represent the scene using different encodingstrategies from coarse-to-fine resolution levels. Hyb-NeRF exploitsmemory-efficiency learnable positional features at coarse resolutions and thefast optimization speed and local details of hash-based feature grids at fineresolutions. In addition, to further boost performance, we embed conetracing-based features in our learnable positional encoding that eliminatesencoding ambiguity and reduces aliasing artifacts. Extensive experiments onboth synthetic and real-world datasets show that Hyb-NeRF achieves fasterrendering speed with better rending quality and even a lower memory footprintin comparison to previous state-of-the-art methods.

Hyb-NeRF: A Multiresolution Hybrid Encoding for Neural Radiance Fields