Vincent Sitzmann

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Real-world sensors suffer from noise, blur, and other imperfections that make high-level computer vision tasks like scene segmentation, tracking, and scene understanding difficult. Making highlevel computer vision networks robust is imperative for real-world applications like autonomous driving, robotics, and surveillance. We propose a novel end-to-end(More)
Traditional cinematography has relied for over a century on a well-established set of editing rules, called continuity editing, to create a sense of situational continuity. Despite massive changes in visual content across cuts, viewers in general experience no trouble perceiving the discontinuous flow of information as a coherent set of events. However,(More)
Convolutional Neural Networks (CNNs) are often used to extract a lower-dimensional feature descriptor from images. Though they have recently been deployed successfully to solve tasks in computer vision such as semantic segmentation, image reconstruction or depth understanding, the inner workings of CNNs are still subject to current research. Recent research(More)
A broad class of problems at the core of computational imaging, sensing, and low-level computer vision reduces to the inverse problem of extracting latent images that follow a prior distribution, from measurements taken under a known physical image formation model. Traditionally, hand-crafted priors along with iterative optimization methods have been used(More)
This article describes the sampling design, survey methodology and findings of a natural resources survey conducted in the Rathbun Lake Watershed in Southern Iowa in 1999-2000. The goal of the survey was to quantify the erosion from all sources on agricultural lands and the ecological health of streams for each of 61 subwatersheds in the area. A total of(More)
Understanding how humans explore virtual environments is crucial for many applications, such as developing compression algorithms or designing effective cinematic virtual reality (VR) content, as well as to develop predictive computational models. We have recorded 780 head and gaze trajectories from 86 users exploring omnidirectional stereo panoramas using(More)
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