The proliferation of low-cost infrared cameras gives us a new angle for attacking many unsolved vision problems by leveraging a larger range of the electromagnetic spectrum. A first step to utilizing these images is to explore the statistics of infrared images and compare them to the corresponding statistics in the visible spectrum. In this paper, we analyze the power spectra as well as the marginal and joint wavelet coefficient distributions of datasets of indoor and outdoor images. We note that infrared images have noticeably less texture indoors where temperatures are more homogenous. the joint wavelet statistics also show strong correlation between object boundaries in IR and visible images, leading to high potential for vision applications using a combined statistical model.

In this paper we consider the problem of reconstructing the 3D position and surface normal of points on an unknown, arbitrarily-shaped refractive surface. We show that two viewpoints are sufficient to solve this problem in the general case, even if the refractive index is unknown. The key requirements are (1) knowledge of a function that maps each point on the two image planes to a known 3D point that refracts to it, and (2) light is refracted only once. We apply this result to the problem of reconstructing the time-varying surface of a liquid from patterns placed below it. To do this, we introduce a novel "stereo matching" criterion called refractive disparity, appropriate for refractive scenes, and develop an optimization-based algorithm for individually reconstructing the position and normal of each point projecting to a pixel in the input views. Results on reconstructing a variety of complex, deforming liquid surfaces suggest that our technique can yield detailed reconstructions that capture the dynamic behavior of free-flowing liquids.

We present a new method for reconstructing the exterior surface of a complex transparent scene with inhomogeneous interior (e.g., multiple interfaces, reflective or painted interiors, etc). Our approach involves capturing images of the scene from one or more viewpoints while moving a proximal light source to a 2D or 3D set of positions. This gives a 2D (or 3D) dataset per pixel, called the scatter trace. The key idea of our approach is that even though light transport within a transparent scene's interior can be exceedingly complex, the scatter trace of each pixel has a highlyconstrained geometry that (1) reveals the contribution of direct surface reflection, and (2) leads to a simple "scattertrace stereo" algorithm for computing the local geometry of the exterior surface (depth and surface normals). We present 3D reconstruction results for a variety of scenes that exhibit complex light transport phenomena.

In this paper we consider the problem of reconstructing the 3D position and surface normal of points on an unknown, arbitrarily-shaped refractive surface. We show that two viewpoints are sufficient to solve this problem in the general case, even if the refractive index is unknown. The key requirements are 1) knowledge of a function that maps each point on the two image planes to a known 3D point that refracts to it, and 2) light is refracted only once. We apply this result to the problem of reconstructing the time-varying surface of a liquid from patterns placed below it. To do this, we introduce a novel "stereo matching" criterion called refractive disparity, appropriate for refractive scenes, and develop an optimization-based algorithm for individually reconstructing the position and normal of each point projecting to a pixel in the input views. Results on reconstructing a variety of complex, deforming liquid surfaces suggest that our technique can yield detailed reconstructions that capture the dynamic behavior of free-flowing liquids.

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