499 citations · 2.2k across the 41 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
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…
FIND: An Unsupervised Implicit 3D Model of Articulated Human Feet
Oliver Boyne, James Charles, Roberto Cipolla
In this paper we present a high fidelity and articulated 3D human foot model. The model is parameterised by a disentangled latent code in terms of shape, texture and articulated po…
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…