Research into non-line-of-sight imaging problems has gained momentum in recent years motivated by intriguing prospective applications in e.g. medicine and autonomous driving. While transient image formation is well understood and there exist various reconstruction approaches for non-line-of-sight scenes that combine efficient forward renderers with optimization schemes, those approaches suffer from runtimes in the order of hours even for moderately sized scenes. Furthermore, the ill-posedness of the inverse problem often leads to instabilities in the optimization.Inspired by the latest advances in direct-line-of-sight inverse rendering that have led to stunning results for reconstructing scene geometry and appearance, we present a fast differentiable transient renderer that accelerates the inverse rendering runtime to minutes on consumer hardware, making it possible to apply inverse transient imaging on a wider range of tasks and in more time-critical scenarios. We demonstrate its effectiveness on a series of applications using various datasets and show that it can be used for self-supervised learning.

Time-correlated imaging is an emerging sensing modality that has been shown to enable promising application scenarios, including lidar ranging, fluorescence lifetime imaging, and even non-line-of-sight sensing. A leading technology for obtaining time-correlated light measurements are single-photon avalanche diodes (SPADs), which are extremely sensitive and capable of temporal resolution on the order of tens of picoseconds. However, the rare and expensive optical setups used by researchers have so far prohibited these novel sensing techniques from entering the mass market. Fortunately, SPADs also exist in a radically cheaper and more power-efficient version that has been widely deployed as proximity sensors in mobile devices for almost a decade. These commodity SPAD sensors can be obtained at a mere few cents per detector pixel. However, their inferior data quality and severe technical drawbacks compared to their high-end counterparts necessitate the use of additional optics and suitable processing algorithms. In this paper, we adopt an existing evaluation platform for commodity SPAD sensors, and modify it to unlock time-of-flight (ToF) histogramming and hence computational imaging. Based on this platform, we develop and demonstrate a family of hardware/software systems that, for the first time, implement applications that had so far been limited to significantly more advanced, higher-priced setups: direct ToF depth imaging, non-line-of-sight object tracking, and material classification.

Computational photography encompasses a diversity of imaging techniques, but one of the core operations performed by many of them is to compute image differences. An intuitive approach to computing such differences is to capture several images sequentially and then process them jointly. In this paper, we introduce a snapshot difference imaging approach that is directly implemented in the sensor hardware of emerging time-of-flight cameras. With a variety of examples, we demonstrate that the proposed snapshot difference imaging technique is useful for direct-global illumination separation, for direct imaging of spatial and temporal image gradients, for direct depth edge imaging, and more.

We propose a material classification method using raw time-of-flight (ToF) measurements. ToF cameras capture the correlation between a reference signal and the temporal response of material to incident illumination. Such measurements encode unique signatures of the material, i.e. the degree of subsurface scattering inside a volume. Subsequently, it offers an orthogonal domain of feature representation compared to conventional spatial and angular reflectance-based approaches. We demonstrate the effectiveness, robustness, and efficiency of our method through experiments and comparisons of real-world materials.