This thesis explores two challenges for imaging in the presence of optical scatterers: retrieving optical properties of the scene through novel imaging modalities and establishing optical asymmetry using scattering. In many optical imaging applications, such as non-invasive functional neuroimaging, light scattering leads to reduced performance, including reduced contrast and spatial resolution. Traditional imaging relies solely on light-intensity measurements. This may be insufficient for inverse rendering, i.e. retrieval of geometric and optical properties of the target from captured images. The first part of this thesis explores how time-of-flight measurements can improve the spatial resolution for imaging through volumetric-scattering media. This time-of-flight imaging approach, time-of-flight diffuse optical tomography (ToF-DOT), uses both physics-based and data-driven models to improve the algorithm runtime, measurement collection latency, and spatial resolution of the image reconstruction. I show that ToF-DOT can improve the state-of-the-art for imaging through densely scattering media. The second part of this thesis explores polarimetric inverse rendering. This technology, polarization-aided neural decomposition of radiance (PANDORA), combines polarimetric imaging with neural rendering algorithms to achieve state-of-the-art inverse rendering results. PANDORA can perform material estimation and 3D geometry estimation, and can handle complex surface interactions, such as subsurface scattering. Finally, the third part of this thesis explores how light scattering can be used to develop an optically asymmetric plume. Such a plume would degrade the image quality more heavily in one viewing direction compared to the opposite direction. This work explores how differentiable rendering and end-to-end optimization can be used to design this plume.