The recent years have seen a surge of interest in methods for imaging beyond
the direct line of sight. The most prominent techniques rely on time-resolved
optical impulse responses, obtained by illuminating a diffuse wall with an
ultrashort light pulse and observing multi-bounce indirect reflections with an
ultrafast time-resolved imager. Reconstruction of geometry from such data,
however, is a complex non-linear inverse problem that comes with substantial
computational demands. In this paper, we employ convolutional feed-forward
networks for solving the reconstruction problem efficiently while maintaining
good reconstruction quality. Specifically, we devise a tailored autoencoder
architecture, trained end-to-end, that maps transient images directly to a
depth map representation. Training is done using an efficient transient
renderer for diffuse three-bounce indirect light transport that enables the
quick generation of large amounts of training data for the network. We examine
the performance of our method on a variety of synthetic and experimental
datasets and its dependency on the choice of training data and augmentation
strategies, as well as architectural features. We demonstrate that our
feed-forward network, even though it is trained solely on synthetic data,
generalizes to measured data from SPAD sensors and is able to obtain results
that are competitive with model-based reconstruction methods.

本文采用卷积前馈神经网络来解决超出直线视野成像中的复杂非线性细节重建问题，能够高效地生成大量的训练数据。实验结果表明，我们的前向传播网络，即使仅在合成数据上训练，也能推广到 SPAD 传感器的测量数据，并能够获得与基于模型的重建方法相媲美的结果。