Adrien Bousseau, GRAPHDECO Inria Research Team
Adrien Bousseau is a researcher from the GRAPHDECO project team at Inria Sophia Antipolis-Méditerranée. He is also the French principal investigator of the Inria@SiliconValley associate team CRISP (Creating and rendering images based on the study of perception) with UC Berkeley.
Specialist in digital imaging, Adrien received one of the three Eurographics best thesis prizes in 2011.
His project on “the interpretation of drawings for 3D design” has enabled him to obtain a grant from the European Research Council (ERC) in the Starting Research Position category of the 2016 call.
Meeting with the new prize-winner, who tells us about his background and plans
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Can you tell us about your background?
I was passionate about digital imaging very early on – something I chose to specialize in straight after my A levels by following technical studies at an IUT (University Institute of Technology). These studies enabled me to realize that in order to go from being a user of imaging tools to being a designer, I needed to master more general theoretical bases such as signal analysis and processing and digital simulation. This was what led me towards an engineering school, and motivated me to read my first research articles. Then followed research internships and a thesis at Inria, where I met my thesis supervisors Joëlle Thollot and François Sillion. They guided me in my research and introduced me to international authorities in the field, with whom I was quickly lucky enough to collaborate.
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What excites you about this field of research?
I like the fact that image synthesis is at the crossroads of several scientific fields – computer science, mathematics, physics – whilst still tackling very concrete problems. When we test our methods, we can see our results directly, and now even touch them thanks to 3D printing. My research on computer-aided drawing tools also allows me to study artistic techniques. Even if artists take certain liberties faced with the laws of physics, numerous drawing techniques are explained by the way in which shapes, materials and light interact in reality. I am passionate about these links between the artistic world and the scientific world.
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What is the subject of your project, which was chosen by the ERC?
My project is entitled “Interpretation of drawings for 3D design”. Drawing is a fundamental tool in design since it enables designers to rapidly externalize their ideas and show them to others. But, for the time being, these drawings cannot be interpreted by computers. In order to test the feasibility of their concepts, designers must create 3D models that are compatible with physical simulation software or 3D printers. However, although rough sketches can be drawn very quickly and freely, 3D modelling requires interaction with complex and rigid interfaces. That is why designers often wait until their concept is well under way before modelling them in 3D and carrying out simulations. The aim of my project is to automatically reconstruct 3D models from drawings in order to enrich the design exploration phase, thanks to the power of 3D engineering tools. For example, a car designer could assess the aerodynamics of the bodywork as soon as it has been drawn. My project has potential applications in numerous fields where drawing is an important design stage, such as industrial design, architecture or fashion.
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What is original about your approach?
Finding the 3D model that corresponds to a drawing is an ambiguous problem since each point of the drawing can be placed anywhere depth-wise. Existing solutions are limited to simple shapes or require the user to provide numerous instructions in order to explain to the algorithm how to interpret the drawing. The originality of my approach is to use the drawing techniques developed by professional designers in order to represent 3D shapes. For example, converging lines provide information on perspective, contours show the orientation of the surfaces, and other lines show their curvature directions (Figure below). The difficulty lies in identifying which techniques are used in a drawing and from this deduce the 3D shape. However testing all of the possible combinations of techniques would be too expensive. The solution I envisage is to use machine learning algorithms capable of identifying the techniques used in a drawing, and even directly predict the 3D shape represented. This approach does however raise the issue of the training data: it is not easy for us to obtain the thousands of drawings required to use such algorithms! I intend to tackle this problem by developing new synthesis algorithms of stylised images, capable of automatically generating synthetic drawings from 3D models.
The designers draw specific curves called sections (left, in red) to show the curvature directions of a surface. Our algorithm uses these lines to estimate the orientation of the surfaces and to calculate shade (right).
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How will this Grant help you with your research in concrete terms?
Automatically reconstructing a drawing in 3D is a very ambitious goal that will require the solving of numerous intermediary problems. First of all we must identify the drawing techniques used by professional designers and understand how they communicate a 3D shape. Then we must be capable of recognizing these techniques in new drawings. We must then merge the information provided by each technique in order to find the most plausible 3D model. Finally, a long-term goal is to enable the algorithm to adapt itself to the drawing style of each user. The ERC grant will give me the time and resources to tackle these different problems and their combination. In concrete terms, I intend to recruit three PhD students, two post-docs and an engineer with additional expertise in image synthesis, geometry, computer vision and machine learning. I also intend to recruit professional designers in order to create a database of drawings, which will enable us to study their techniques and test our algorithms.
Source: https://www.inria.fr/en/centre/sophia/news/adrien-bousseau-receives-an-erc-starting-grant
