In photography, as in other imaging areas, the aspect ratio of a picture is dictated by the aspect ratio of an image sensor. In this paper, we present a flexible and efficient lensless approach to overcome aspect ratio dependency. We introduce a theory of coded aperture imaging when the aspect ratio of the scene is mismatched with the aspect ratio of the sensor. Using our prototype, we demonstrate how our theoretical results can be effectively applied for ultra-wide imaging with a conventional image sensor, and for normal aspect ratio imaging with an ultra-wide sensor. We use the latter application to perform high frame rate imaging with a rolling shutter sensor by dividing the sensor area into multiple narrow sub-areas along the rolling shutter direction.

We propose a novel, practical solution for high quality reconstruction of axially-symmetric transparent objects. While a special case, such transparent objects are ubiquitous in the real world. Common examples of these are glasses, goblets, tumblers, carafes, etc., that can have very unique and visually appealing forms making their reconstruction interesting for vision and graphics applications. Our acquisition setup involves imaging such objects from a single viewpoint while illuminating them from directly behind with a few patterns emitted by an LCD panel. Our reconstruction step is then based on optimization of the objects geometry and its refractive index to minimize the difference between observed and simulated transmission/refraction of rays passing through the object. We exploit the objects axial symmetry as a strong shape prior which allows us to achieve robust reconstruction from a single viewpoint using a simple, commodity acquisition setup. We demonstrate high quality reconstruction of several common rotationally symmetric as well as more complex n-fold symmetric transparent objects with our approach.

We present a novel approach for on-site acquisition of surface reflectance for planar, spatially varying, isotropic samples in uncontrolled outdoor environments. Our method exploits the naturally occurring linear polarization of incident and reflected illumination for this purpose. By rotating a linear polarizing filter in front of a camera at three different orientations, we measure the polarization reflected off the sample and combine this information with multi-view analysis and inverse rendering in order to recover per-pixel, high resolution reflectance and surface normal maps. Specifically, we employ polarization imaging from two near orthogonal views close to the Brewster angle of incidence in order to maximize polarization cues for surface reflectance estimation. To the best of our knowledge, our method is the first to successfully extract a complete set of reflectance parameters with passive capture in completely uncontrolled outdoor settings. To this end, we analyze our approach under the general, but previously unstudied, case of incident partial linear polarization (due to the sky) in order to identify the strengths and weaknesses of the method under various outdoor conditions. We provide practical guidelines for on-site acquisition based on our analysis, and demonstrate high quality results with an entry level DSLR as well as a mobile phone.

We investigate the problem of identifying the position of a viewer inside a room of planar mirrors with unknown geometry in conjunction with the room's shape parameters. We consider the observations to consist of angularly resolved depth measurements of a single scene point that is being observed via many multi-bounce interactions with the specular room geometry. Applications of this problem statement include areas such as calibration, acoustic echo cancelation and time-of-flight imaging. We theoretically analyze the problem and derive sufficient conditions for a combination of convex room geometry, observer, and scene point to be reconstruct able. The resulting constructive algorithm is exponential in nature and, therefore, not directly applicable to practical scenarios. To counter the situation, we propose theoretically devised geometric constraints that enable an efficient pruning of the solution space and develop a heuristic randomized search algorithm that uses these constraints to obtain an effective solution. We demonstrate the effectiveness of our algorithm on extensive simulations as well as in a challenging real-world calibration scenario.

We introduce three-dimensional kaleidoscopic imaging, a promising alternative for recording multi-view imagery. The main limitation of multi-view reconstruction techniques is the limited number of views that are available from multi-camera systems, especially for dynamic scenes. Our new system is based on imaging an object inside a kaleidoscopic mirror system. We show that this approach can generate a large number of high-quality views well distributed over the hemisphere surrounding the object in a single shot. In comparison to existing multi-view systems, our method offers a number of advantages: it is possible to operate with a single camera, the individual views are perfectly synchronized, and they have the same radiometric and colorimetric properties. We describe the setup both theoretically, and provide methods for a practical implementation. Enabling interfacing to standard multi-view algorithms for further processing is an important goal of our techniques.