Deep Polarization Imaging for 3D shape and SVBRDF Acquisition
This enables efficient 3D object capture for applications like computer graphics and vision, but it is incremental as it builds on existing polarization and deep learning techniques.
The paper tackles the problem of acquiring 3D shape and spatially varying reflectance from single-view polarization images under frontal flash illumination, achieving high-quality estimates of surface normals, depth, and SVBRDF with superior results compared to recent deep learning methods.
We present a novel method for efficient acquisition of shape and spatially varying reflectance of 3D objects using polarization cues. Unlike previous works that have exploited polarization to estimate material or object appearance under certain constraints (known shape or multiview acquisition), we lift such restrictions by coupling polarization imaging with deep learning to achieve high quality estimate of 3D object shape (surface normals and depth) and SVBRDF using single-view polarization imaging under frontal flash illumination. In addition to acquired polarization images, we provide our deep network with strong novel cues related to shape and reflectance, in the form of a normalized Stokes map and an estimate of diffuse color. We additionally describe modifications to network architecture and training loss which provide further qualitative improvements. We demonstrate our approach to achieve superior results compared to recent works employing deep learning in conjunction with flash illumination.