Vision & Learning

The Vision & Learning Group (VLG) focuses on learning from interaction in physical environments. Complex and safe manipulation and navigation technology leverage precise 3D geometry and scene understanding in conjunction with strong world-aware action selection frameworks. Learned concepts are effectively transfered to new domains.

Research

The VLG is conducting cutting edge research on a number of fields leading fundamental research to pioneer applications.

Resources

Driven by academic research results, the VLG contribution to the state-of-the-art methods in Perception, Reasoning, Action and Learning passes through shared resources to ensure reproducibility of results.

Selected Publications

Modelling Partially Observable Systems using Graph-based Memory and Topological Priors

S. Morad, S. Liwicki, R. Kortvelesy, R. Mecca, A. Prorok
Learning for Dynamics and Control Conference

LUCES: A Dataset for Near-Field Point Light Source Photometric Stereo

R. Mecca, F. Logothetis, I. Budvytis and R. Cipolla
British Machine Vision Conference

PX-NET: Simple and Efficient Pixel-Wise Training of Photometric Stereo Networks

F. Logothetis, I. Budvytis, R. Mecca, R. Cipolla
International Conference on Computer Vision

Lifted Semantic Graph Embedding for Omnidirectional Place Recognition

C. Zhang, I. Budvytis, S. Liwicki and R. Cipolla
International Conference on 3D Vision

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