Dr Partha Das (University of Amsterdam): Physics based photometric invariance for image understanding
17 September 2024, 11-12 h in PB-H 0103
The talk is part of the ZESS lecture series and hosted by the DFG research unit “Learning to Sense” (L2S).
The physics for image formation is known: light from light sources get reflected (and absorbed) by an object and reaches the sensor. Inverting this process is called intrinsics image decomposition (IID). Deep learning-based approaches have shown remarkably good performance in inverting this phenomenon. However, they often utilize very large datasets and complicated loss functions. Further, most of the approaches rely purely on image features, ignoring the underlying physics of image formation, adding to the data requirement and failing under the presence of strong photometric effects. In this talk, I will discuss about how the physics of image formation can be exploited to simplify the problem and allowing the network to learn the underlying model, rather than memorising the dataset. I will talk about my work where I focus on utilizing such physics-based modifications to simplify the IID problem, resulting in smaller networks that are trained on much smaller, purely synthetic datasets, while being generalisable to unseen real-world scenes.
Partha Das holds a PhD from the Computer Vision Lab under the Informatics Institute of the University of Amsterdam. He was supervised by Prof Theo Gevers and Dr Sezer Karaoglu. His research interests are in physics based image understanding, using deep learning & traditional approaches. Specifically, tasks like image formation, light estimation and colour correction, etc. He is interested in levying these approaches to build efficient & stable AR/VR/Mixed Reality systems.
