169 research outputs found
Iterative Universal Rigidity
A bar framework determined by a finite graph and configuration in
space is universally rigid if it is rigid in any . We provide a characterization of universally rigidity for any
graph and any configuration in terms of a sequence of affine
subsets of the space of configurations. This corresponds to a facial reduction
process for closed finite dimensional convex cones.Comment: 41 pages, 12 figure
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A Lixel for every Pixel
Shadow mapping is a very useful tool for generating shadows in many real-time rendering settings and is even used in some off-line renderers. One of the difficulties when using a shadow map is obtaining a sufficiently dense sampling on shadowed surfaces to minimize shadow aliasing. Endlessly upping the light-image resolution is not always a viable option. In this paper we describe a shadow mapping technique that guarantees, that over a small number of chosen planes of interest (such as a floor and a wall), the shadow map is, in fact, perfectly sampled, ie. for each pixel in the viewer camera, there will be exactly one lixel in the shadow map that samples the exact same geometric point.Engineering and Applied Science
Low-level Vision by Consensus in a Spatial Hierarchy of Regions
We introduce a multi-scale framework for low-level vision, where the goal is
estimating physical scene values from image data---such as depth from stereo
image pairs. The framework uses a dense, overlapping set of image regions at
multiple scales and a "local model," such as a slanted-plane model for stereo
disparity, that is expected to be valid piecewise across the visual field.
Estimation is cast as optimization over a dichotomous mixture of variables,
simultaneously determining which regions are inliers with respect to the local
model (binary variables) and the correct co-ordinates in the local model space
for each inlying region (continuous variables). When the regions are organized
into a multi-scale hierarchy, optimization can occur in an efficient and
parallel architecture, where distributed computational units iteratively
perform calculations and share information through sparse connections between
parents and children. The framework performs well on a standard benchmark for
binocular stereo, and it produces a distributional scene representation that is
appropriate for combining with higher-level reasoning and other low-level cues.Comment: Accepted to CVPR 2015. Project page:
http://www.ttic.edu/chakrabarti/consensus
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