Research Engineer: Graphics for Image-Based Rendering & Learning

The goal of this position is to develop and extend two main software platforms in the research group, and is in the context of the ERC Advanced Grant FUNGRAPH ( ). The position is at the GraphDeco group at Inria Sophia-Antipolis (, in the beautiful French Riviera.

The first platform is an image synthesis platform for training and testing computer vision and computer graphics algorithms. We have already set up an initial pipeline based on 3DSMax ( and the Mitsuba renderer (, including our own custom plugins to parse the 3D scenes and render high quality images, as well as to run various computer vision algorithms on the rendered images (structure from motion, multi-view stereo). We have notably used the platform to generate large collections of rendered images for training machine learning algorithms. The platform was notably used for the publication in SIGGRAPH 2019 “Multi-view Relighting Using a Geometry-Aware Network” ( The engineer will be in charge of designing and implementing novel features of the pipeline to make it more flexible and easy-to-use. These include (among others) automating the generation of new scenes by modifying the geometry, materials and lighting of existing scenes and providing support for the various research projects in the group. The task will involve programming in c++ and python both in an OpenGL-based system (see below) and in mitsuba.

The second platform is our Image-Based Rendering system based on C++ and OpenGL that has been used for over 10 recent publications in the group. We have recently restructured our codebase into a shared core, providing functionality for multi-view imaging and basic Image-Based Rendering functionality, and separate code repositories for each project. The engineer will complete the integration task of the various projects and will have overall responsibility of providing an opensource version that will be progressively released in the near future. The task includes programming in C++ and OpenGL, but also the use of machine learning libraries and python.

The ideal candidate will have a Masters in Computer Graphics, with extensive experience in building complex graphics systems in C++ as well as extensive knowledge of the theory and practice of the graphics pipeline (including GPU rendering and ray-tracing/global illumination). The ability to read, comprehend and implement research papers is also necessary. Knowledge of python and OpenCV will be very helpful, knowledge of cmake and some experience in deep learning and CNNs will also be appreciated. Fluency in spoken and written English is a requirement.

To apply, please send your CV, letter of motivation and for recent graduates academic transcripts of the last 3 years of study to George dot Drettakis at

Example papers integrated in our IBR platform:

  • [Chaurasia13] G. CHAURASIA, S. DUCHENE, O. SORKINE-HORNUNG, G. DRETTAKIS, Depth Synthesis and Local Warps for Plausible Image-based Navigation, ACM Transactions on Graphics 32, 2013,

  • [Hedman16] HEDMAN, P., RITSCHEL, T., DRETTAKIS, G., & BROSTOW, G. (2016). Scalable inside-out image-based rendering. ACM Transactions on Graphics (TOG), 35(6), 231.

  • [Hedman18] P. HEDMAN, J. PHILIP, T. PRICE, J.-M. FRAHM, G. DRETTAKIS, G. BROSTOW, Deep Blending for Free-Viewpoint Image-Based Rendering, ACM Transactions on Graphics (SIGGRAPH Asia Conference Proceedings) 37, 6, November 2018,

  • [Rodriguez18] S. RODRIGUEZ, A. BOUSSEAU, F. DURAND, G. DRETTAKIS, Exploiting Repetitions for Image-Based Rendering of Facades, Computer Graphics Forum (Proceedings of the Eurographics Symposium on Rendering) 37, 4, 2018,

  • [Philip19] J. PHILIP, M. GHARBI, T. ZHOU, A. EFROS, G. DRETTAKIS, Multi-view Relighting Using a Geometry-Aware Network, ACM Transactions on Graphics (SIGGRAPH Conference Proceedings) 38, 4, July 2019,

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