6 accepted papers
This paper studies the problem of sparse residual regression, i.e., learning a linear model using a norm that favors solutions in which the residuals are sparsely distributed. This is a common problem in a wide range of computer vision applications where a linear system has a lot more equations than…
This paper presents a near-light photometric stereo method for spatially varying reflectances. Recent studies in photometric stereo proposed learning-based approaches to handle diverse real-world reflectances and achieve high accuracy compared to conventional methods. However, they assume distant (i…
In this paper, we consider the problem of estimating surface normals of a scene with spatially varying, general BRDFs observed by a static camera under varying, known, distant illumination. Unlike previous approaches that are mostly based on continuous local optimization, we cast the problem as a di…
We present a minimal imaging setup that harnesses both geometric and photometric approaches for shape and albedo recovery. We adopt a stereo camera and a flashlight to capture a stereo image pair and a flash/no-flash pair. From the stereo image pair, we recover a rough shape that captures low-freque…
This paper targets at discovering what a deep uncalibrated photometric stereo network learns to resolve the problem’s inherent ambiguity, and designing an effective network architecture based on the new insight to improve the performance. The recently proposed deep uncalibrated photometric stereo me…
We present a practical method for geometric point light source calibration. Unlike in prior works that use Lambertian spheres, mirror spheres, or mirror planes, our calibration target consists of a Lambertian plane and small shadow casters at unknown positions above the plane. Due to their small siz…