Openings
We have several openings (internship & full-time). More details and application here.
Book
Fond of sharing my knowledge and experience with others, I got the opportunity to co-author a book, teaching how to leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras. While some technical examples in the book are now slightly outdated (with regard to the TensorFlow API), the book also covers the foundations of deep learning, illustrated with publicly-available code examples (see GitHub link below).
Benjamin Planche and Eliot Andres
Packt Publishing, 2019
Computer vision solutions are becoming increasingly common, making their way in fields such as health, automobile, social media, and robotics. With the release of TensorFlow 2, the brand new version of Google's open source framework for machine learning, it is the perfect time to jump on board and start leveraging deep learning for your visual applications!
This book is a practical guide to building high performance systems for object detection, segmentation, video processing, smartphone applications, and more. By its end, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0.
[Amazon | Packt | GitHub]
Research
I am interested in computer vision, domain adaptation, image rendering, inverse problems, and photography. Much of my research is about training robust recognition systems on scarce data and bridging the gap between real and synthetic modalities.
Benjamin Planche,
Rajat Vikram Singh
IEEE/CVF International Conference on Computer Vision (ICCV), 2021
We introduce DDS, a novel end-to-end differentiable simulation pipeline for the generation of realistic depth scans, built on physics-based 3D rendering and custom block-matching algorithms. Each module can be differentiated w.r.t sensor and scene parameters; e.g., to automatically tune the simulation for new devices over some provided scans or to leverage the pipeline as a 3D-to-2.5D transformer within larger computer-vision applications.
[full version, with sup-mat]
Peri AkivaEC,
Benjamin PlancheEC,
Aditi Roy,
Kristin Dana,
Peter Oudemans,
Michael Mars
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021 (EC equal contribution)
We propose an end-to-end cranberry health monitoring system to enable and support real time cranberry over-heating assessment and facilitate informed decisions that may sustain the economic viability of farms. Our system performs: 1) cranberry fruit segmentation to delineate fruit regions that are exposed to sun, 2) prediction of cloud coverage and sun irradiance to estimate the inner temperature of exposed cranberries.
Benjamin Planche,
Xuejian Rong,
Ziyan Wu,
Srikrishna Karanam,
Harald Kosch,
YingLi Tian,
Jan Ernst,
Andreas Hutter
Annual Conference on Neural Information Processing Systems (NeurIPS), 2019
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.
Benjamin PlancheEC,
Sergey ZakharovEC,
Ziyan Wu,
Harald Kosch,
Andreas Hutter,
Slobodan Ilic
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019 (EC equal contribution)
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.
Sergey ZakharovEC,
Benjamin PlancheEC,
Ziyan Wu,
Harald Kosch,
Andreas Hutter,
Slobodan Ilic
International Conference on 3D Vision (3DV), 2018 [oral] (EC equal contribution)
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.
Benjamin Planche,
Ziyan Wu,
Kai Ma,
Shanhui Sun,
Stefan Kluckner,
Oliver Lehmann,
Terrence Chen,
Andreas Hutter,
Sergey Zakharov,
Harald Kosch,
Jan Ernst
International Conference on 3D Vision (3DV), 2017 [oral]
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.
Sergey Zakharov,
Wadim Kehl,
Benjamin Planche,
Andreas Hutter,
Slobodan Ilic
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017
Inspired by the descriptor learning approach of Wohlhart et al. [link], 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.
Benjamin PlancheEC,
Bryan Isaac MalynEC,
Daniel Buldon BlancoEC,
Manuel Cerrillo BermejoEC
International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI), 2014 [oral] (EC equal contribution)
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.
