  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.
