activity
20152022
most citedFast-SCNN: Fast Semantic Segmentation Network

367 citations · 579 across the 15 of their papers we have counts for

collaborators

29 papers

cs.CV202233 cited

Model-Based Imitation Learning for Urban Driving

Anthony Hu, Gianluca Corrado, Nicolas Griffiths +6

An accurate model of the environment and the dynamic agents acting in it offers great potential for improving motion planning. We present MILE: a Model-based Imitation LEarning app…

cs.CV202222 cited

A CNN Based Approach for the Point-Light Photometric Stereo Problem

Fotios Logothetis, Roberto Mecca, Ignas Budvytis +1

Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagat…

cs.CV20229 cited

IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty

Gwangbin Bae, Ignas Budvytis, Roberto Cipolla

Single image surface normal estimation and depth estimation are closely related problems as the former can be calculated from the latter. However, the surface normals computed from…

cs.CV202210 cited

DigiFace-1M: 1 Million Digital Face Images for Face Recognition

Gwangbin Bae, Martin de La Gorce, Tadas Baltrusaitis +5

State-of-the-art face recognition models show impressive accuracy, achieving over 99.8% on Labeled Faces in the Wild (LFW) dataset. Such models are trained on large-scale datasets…

cs.CV2022

SPARC: Sparse Render-and-Compare for CAD model alignment in a single RGB image

Florian Langer, Gwangbin Bae, Ignas Budvytis +1

Estimating 3D shapes and poses of static objects from a single image has important applications for robotics, augmented reality and digital content creation. Often this is done thr…

cs.LG2022

Contrastive Unsupervised Learning of World Model with Invariant Causal Features

Rudra P. K. Poudel, Harit Pandya, Roberto Cipolla

In this paper we present a world model, which learns causal features using the invariance principle. In particular, we use contrastive unsupervised learning to learn the invariant…