  NeRF-based techniques fit wide and deep multi-layer perceptrons (MLPs) to a
continuous radiance field that can be rendered from any unseen viewpoint.
However, the lack of surface and normals definition and high rendering times
limit their usage in typical computer graphics applications. Such limitations
have recently been overcome separately, but solving them together remains an
open problem. We present KiloNeuS, a neural representation reconstructing an
implicit surface represented as a signed distance function (SDF) from
multi-view images and enabling real-time rendering by partitioning the space
into thousands of tiny MLPs fast to inference. As we learn the implicit surface
locally using independent models, resulting in a globally coherent geometry is
non-trivial and needs to be addressed during training. We evaluate rendering
performance on a GPU-accelerated ray-caster with in-shader neural network
inference, resulting in an average of 46 FPS at high resolution, proving a
satisfying tradeoff between storage costs and rendering quality. In fact, our
evaluation for rendering quality and surface recovery shows that KiloNeuS
outperforms its single-MLP counterpart. Finally, to exhibit the versatility of
KiloNeuS, we integrate it into an interactive path-tracer taking full advantage
of its surface normals. We consider our work a crucial first step toward
real-time rendering of implicit neural representations under global
illumination.
