Andrew Fitzgibbon

TL;DR: A system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware, which fuse all of the depth data streamed from a Kinect sensor into a single global implicit surface model of the observed scene in real- time.

TL;DR: This work takes an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler per-pixel classification problem, and generates confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes.

TL;DR: A survey of the theory and methods of photogrammetric bundle adjustment can be found in this article, with a focus on general robust cost functions rather than restricting attention to traditional nonlinear least squares.

TL;DR: This work takes an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler per-pixel classification problem, and generates confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes.

TL;DR: This paper presents a new efficient method for fitting ellipses to scattered data that is ellipse-specific so that even bad data will always return an ellipso, and can be solved naturally by a generalized eigensystem.