143,187 research outputs found
Single-shot layered reflectance separation using a polarized light field camera
We present a novel computational photography technique for single shot separation of diffuse/specular reflectance as well as novel angular domain separation of layered reflectance. Our solution consists of a two-way polarized light field (TPLF) camera which simultaneously captures two orthogonal states of polarization. A single photograph of a subject acquired with the TPLF camera under polarized illumination then enables standard separation of diffuse (depolarizing) and polarization preserving specular reflectance using light field sampling. We further demonstrate that the acquired data also enables novel angular separation of layered reflectance including separation of specular reflectance and single scattering in the polarization preserving component, and separation of shallow scattering from deep scattering in the depolarizing component. We apply our approach for efficient acquisition of facial reflectance including diffuse and specular normal maps, and novel separation of photometric normals into layered reflectance normals for layered facial renderings. We demonstrate our proposed single shot layered reflectance separation to be comparable to an existing multi-shot technique that relies on structured lighting while achieving separation results under a variety of illumination conditions
Separating Reflection and Transmission Images in the Wild
The reflections caused by common semi-reflectors, such as glass windows, can
impact the performance of computer vision algorithms. State-of-the-art methods
can remove reflections on synthetic data and in controlled scenarios. However,
they are based on strong assumptions and do not generalize well to real-world
images. Contrary to a common misconception, real-world images are challenging
even when polarization information is used. We present a deep learning approach
to separate the reflected and the transmitted components of the recorded
irradiance, which explicitly uses the polarization properties of light. To
train it, we introduce an accurate synthetic data generation pipeline, which
simulates realistic reflections, including those generated by curved and
non-ideal surfaces, non-static scenes, and high-dynamic-range scenes.Comment: accepted at ECCV 201
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