6 papers
Panoptic Segmentation Forecasting
Colin Graber, Grace Tsai, Michael Firman +2
Our goal is to forecast the near future given a set of recent observations. We think this ability to forecast, i.e., to anticipate, is integral for the success of autonomous agents…
DiverseNet: When One Right Answer is not Enough
Michael Firman, Neill D. F. Campbell, Lourdes Agapito +1
Many structured prediction tasks in machine vision have a collection of acceptable answers, instead of one definitive ground truth answer. Segmentation of images, for example, is s…
Learning Stereo from Single Images
Jamie Watson, Oisin Mac Aodha, Daniyar Turmukhambetov +2
Supervised deep networks are among the best methods for finding correspondences in stereo image pairs. Like all supervised approaches, these networks require ground truth data duri…
Footprints and Free Space from a Single Color Image
Jamie Watson, Michael Firman, Aron Monszpart +1
Understanding the shape of a scene from a single color image is a formidable computer vision task. However, most methods aim to predict the geometry of surfaces that are visible to…
Self-Supervised Monocular Depth Hints
Jamie Watson, Michael Firman, Gabriel J. Brostow +1
Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser scans or other ground-trut…
RGBD Datasets: Past, Present and Future
Michael Firman
Since the launch of the Microsoft Kinect, scores of RGBD datasets have been released. These have propelled advances in areas from reconstruction to gesture recognition. In this pap…