Structured light scanners reconstruct 3D information about a scene by projecting a known pattern of light, such as a laser line or time-multiplexed gray code, …
Automatic recognition and segmentation of objects in images is a central open problem in computer vision. The failure of the segmentation-then-recognition paradigm to produce reliable …
With the rise of computer-aided design and automated fabrication machines, many people bemoan the loss of “hands-on” crafts. This thesis is about assisting "hands-on" users …
Many types of data observed in high dimensional spaces typically lie on or near low-dimensional manifolds and have much lower intrinsic dimension. Visual data captured …
Robotic perception often fails on reflective and transparent surfaces. We will describe a new method of passive RGB sensing which uses a calibrated camera located …
Progress in deep feedforward neural networks has spawned great successes in many practical applications, but these models struggle to reproduce human-level generalization in tasks that …
We propose a general-purpose probabilistic framework for scene understanding tasks. We show that several classical scene understanding tasks can be modeled and addressed under a …
Traditional visual odometry approaches often rely on estimating the world in the form a 3D cloud of points from keyframes, which are then projected onto …
We develop new representations and algorithms for three-dimensional (3D) scene understanding from images and videos. To model cluttered indoor scenes, we introduce object descriptors that …
Computer vision problems can be generally divided into three categories: recognition, reconstruction and reorganization. Specifically, reorganization problem is usually considered as a mid-stage process that …
