Benjamin Planche

I am a passionate research scientist at Siemens Corporate Technology in Princeton, NJ. There, I develop novel computer vision and machine learning solutions, focusing on data scarcity problems and industrial vision systems.

I obtained my PhD summa cum laude from the Faculty of Computer Science and Mathematics at the University of Passau, under the supervision of Prof. Dr. Harald Kosch.

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 from INSA-Lyon (France) and the University of Passau (Germany), with first-class honors and a multinational excellence award. I co-authored a book on applied computer vision. I am also sharing my knowledge and experience on online platforms such as StackOverflow or applying them to the creation of aesthetic demos. I love visual art.

Email / Resume / Google Scholar / StackOverflow / LinkedIn

News

(Jul. 2020) Finally defended my thesis and received my PhD summa cum laude!
(Jan. 2020) New position as Research Scientist at Siemens Corporate Technology, Princeton, NJ!
(Jun. 2019) Our book is out: Hands-On Computer Vision With TensorFlow 2!
(Jun. 2019) New paper in NeurIPS 2019: Incremental Scene Synthesis.
(Jun. 2019) New paper in IROS 2019: Seeing Beyond Appearance.

Book

Fond of sharing my knowledge and experience with others, I recently 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:

Hands-On Computer Vision With TensorFlow 2

Benjamin Planche and Eliot Andres
Packt Publishing, 2018

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, 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.

Incremental Scene Synthesis

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.

Paper Teaser - Pipeline and Results for Reverse Domain Adaptation

Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition

Benjamin Planche^*, Sergey Zakharov^*, Ziyan Wu, Harald Kosch, Andreas Hutter, Slobodan Ilic
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019 (* 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.

Keep it Unreal: Bridging the Realism Gap for 2.5D Recognition with Geometry Priors Only

Sergey Zakharov^*, Benjamin Planche^*, Ziyan Wu, Harald Kosch, Andreas Hutter, Slobodan Ilic
International Conference on 3D Vision (3DV), 2018 [oral] (* 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.

Paper Teaser - Pipeline and Results for Depth Sensor Simulation

DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD Models for 2.5D Recognition

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.

Paper Teaser - Illustration of Triplets and Pairs

3D Object Instance Recognition and Pose Estimation Using Triplet Loss with Dynamic Margin

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.

The Brightnest Web-Based Home Automation System

Benjamin Planche^*, Bryan Isaac Malyn^*, Daniel Buldon Blanco^*, Manuel Cerrillo Bermejo^*
International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI), 2014 [oral] (* 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.