Existing time-resolved non-line-of-sight (NLOS) imaging methods reconstruct
hidden scenes by inverting the optical paths of indirect illumination measured
at visible relay surfaces. These methods are prone to reconstruction artifacts
due to inversion ambiguities and capture noise, which are typically mitigated
through the manual selection of filtering functions and parameters. We
introduce a fully-differentiable end-to-end NLOS inverse rendering pipeline
that self-calibrates the imaging parameters during the reconstruction of hidden
scenes, using as input only the measured illumination while working both in the
time and frequency domains. Our pipeline extracts a geometric representation of
the hidden scene from NLOS volumetric intensities and estimates the
time-resolved illumination at the relay wall produced by such geometric
information using differentiable transient rendering. We then use gradient
descent to optimize imaging parameters by minimizing the error between our
simulated time-resolved illumination and the measured illumination. Our
end-to-end differentiable pipeline couples diffraction-based volumetric NLOS
reconstruction with path-space light transport and a simple ray marching
technique to extract detailed, dense sets of surface points and normals of
hidden scenes. We demonstrate the robustness of our method to consistently
reconstruct geometry and albedo, even under significant noise levels.

通过可分的端到端非直视成像管道，我们以在时间和频率领域同时工作的方式，在重建隐藏场景过程中自动校准成像参数，并从非直视体积强度中提取隐藏场景的几何表示，估计由该几何信息产生的继电墙上的时间分辨照明。