Learning Neural Duplex Radiance Fields for Real-Time View Synthesis

Ziyu Wan · Christian Richardt · Aljaž Božič · Chao Li · Vijay Rengarajan · Seonghyeon Nam · Xiaoyu Xiang · Tuotuo Li · Bo Zhu · Rakesh Ranjan · Jing Liao

West Building Exhibit Halls ABC 008
[ Abstract ] [ Project Page ]
Wed 21 Jun 10:30 a.m. PDT — noon PDT


Neural radiance fields (NeRFs) enable novel view synthesis with unprecedented visual quality. However, to render photorealistic images, NeRFs require hundreds of deep multilayer perceptron (MLP) evaluations -- for each pixel. This is prohibitively expensive and makes real-time rendering infeasible, even on powerful modern GPUs. In this paper, we propose a novel approach to distill and bake NeRFs into highly efficient mesh-based neural representations that are fully compatible with the massively parallel graphics rendering pipeline. We represent scenes as neural radiance features encoded on a two-layer duplex mesh, which effectively overcomes the inherent inaccuracies in 3D surface reconstruction by learning the aggregated radiance information from a reliable interval of ray-surface intersections. To exploit local geometric relationships of nearby pixels, we leverage screen-space convolutions instead of the MLPs used in NeRFs to achieve high-quality appearance. Finally, the performance of the whole framework is further boosted by a novel multi-view distillation optimization strategy. We demonstrate the effectiveness and superiority of our approach via extensive experiments on a range of standard datasets.

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