Benjamin Planche

I am a passionate Ph.D. student at the University of Passau and Siemens Corporate Technology, where I develop novel computer vision and machine learning solutions.

I have been working in various research labs around the world (LIRIS in France, Mitsubishi Electric in Japan, Siemens in Germany). I have a double Master's degree with first-class honors from INSA-Lyon in France and the University of Passau in Germany. My research efforts are focused on developing smarter visual systems with less data, targeting industrial applications. I am also sharing my knowledge and experience on online platforms such as StackOverflow or applying them to the creation of aesthetic demos.

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I am interested in computer vision, machine learning, domain adaptation, image processing, and photography. Much of my research is about training robust recognition systems on scarce data and bridging the gap between real and synthetic modalities.

We present a method to incrementally generate complete 2D or 3D scenes. Our framework can register observations from a non-localized agent in a global representation, which can be used to synthesize new views as well as fill in gaps in the representation while observing global consistency.

Tackling real/synthetic domain adaptation from a different angle, we introduce a pipeline to map unseen target samples into the synthetic domain used to train task-specific methods. Denoising the data and retaining only the features these recognition algorithms are familiar with, our solution greatly improves their performance.

We propose a novel approach leveraging only CAD models to bridge the realism gap for depth images. Purely trained on synthetic data, playing against an extensive augmentation pipeline in an unsupervised manner, our GAN learns to effectively segment depth images and recover the clean synthetic-looking depth information even from partial occlusions.

We present an end-to-end framework which simulates the whole mechanism of depth sensors, generating realistic depth data from 3D models by comprehensively modeling vital factors, e.g., sensor noise, material reflectance, surface geometry. Our solution covers a wider range of devices and achieves more realistic results than previous methods.

Inspired by the descriptor learning approach of Wohlhart et al. \cite{wohlhart2015learning}, we propose a method that introduces the dynamic margin in the manifold learning triplet loss function. Introducing the dynamic margin allows for faster training times and better accuracy of the resulting low dimensional manifolds.

Brightnest is a generic and user-friendly web-based Home Automation System. Its interface provides users with information on the whole system or with control over the devices and their rules. The modular architecture is based on “JS Drivers”, their REST API imitating the way a computer usually handles new devices.