We are looking for motivated researchers to start as PhD candidates in the Computation, Appearance and Manufacturing Group. The candidates will research the exiting and rising field of computational fabrication. The group has a particular focus on appearance manufacturing using a wide range of advanced manufacturing devices, among which 3D printers. Appearance manufacturing concerns the fabrication of objects with a given appearance and has a variety of applications from medical devices and cultural heritage preservation to stop-motion animation. We have a keen interest in using deep learning methods in the context of manufacturing, where we have taken successful initial steps.
Strong applicants with a master degree (or those approaching the end of their master studies) in computer science or a related field are encouraged to apply. Research background in one or more of the following areas is necessary: computer graphics (with a preference for rendering), geometry processing, image processing or machine learning. Solid skills in mathematics and related programming languages (C++ / Python / OpenGL) are also required.
Please send your application or any inquiry to Dr. Vahid Babaei (firstname.lastname@example.org). The application should include a CV, motivation letter and copy of transcripts. Names of two references agreed to write a recommendation letter are also required.
We are witnessing the day-to-day improvement of manufacturing devices in the era of advanced manufacturing. Advanced manufacturing bridges the gap between R&D activities and product development, boosts the economic competitiveness and creates high-quality jobs. In Computation, Appearance and Manufacturing group, we focus on inventing new computational tools that release the full potential of advanced manufacturing processes, such as additive manufacturing (also known as 3D printing). With the immense growth of the manufacturing hardware in resolution, scale and speed, the algorithm complexity increases even more dramatically. Our group therefore aims at developing hardware-aware, scalable algorithms for advanced manufacturing.
We have a particular interest in visual appearance of objects and strive for better algorithms that help manufacturing products with novel and useful appearance characteristics. Design for manufacturing of objects with high-fidelity appearance specifications is a key engineering task. Thus, there remains a huge opportunity for revolutionizing the computational appearance design and manufacturing, and significantly improving the appearance quality of many products. The results of this research will immediately enable numerous applications in rapid prototyping and manufacturing of end-use products. This spans several application domains from medical devices and surgical training, to cultural heritage preservation and anti-counterfeiting.
Multi-material 3D Appearance Printing
The hardware capable of multi-material 3D printing is very new: 3D printers capable of "full-color" printing are becoming available only now. It is, however, challenging to exploit this capability to its fullest, to perfectly reproduce a certain appearance, since this requires finding the ideal arrangement of basis materials. Our research in this axis aims at overcoming several technical challenges:
(a) The addition of a third dimension along which we can arrange materials introduces both opportunities and challenges. For example, V. Babaei recently showed how to take advantage of this property by introducing a novel ink combination strategy without the need for the status-quo halftoning methods (SIGGRAPH 2017).
(b) It is crucial that we can predict the appearance of a given printed structure accurately and quickly. Prediction accuracy is the paramount requirement for many high-end applications. The simulation should also be rapid since the iterative optimization procedures for computing the multi-material structures resulting in a certain appearance rely on the forward simulation at each optimization step.
(c) Multi-material 3D printing of appearance is an unexplored research area, with almost no existing standards. Today, we do not have a clear insight about the necessary ``primary'' materials in order to span a reasonable gamut of appearance (unlike the CMYK or RGB systems in color reproduction).
Computational Fine Art Reproduction
Fine art objects are instruments for aesthetic contemplation as well as social scientific studies. Fine art artifacts are exposed to different dangers, such as aging and destruction even though they already incur huge costs to museums for conserving them. It is essential that we learn to preserve this heritage for future generations and ourselves. We take fine art as a primary case study for our research on appearance reproduction. Beside the importance for cultural heritage preservation, work of art is an ideal case study since all elements of appearance are present: 3D texture, spectral color, gloss and translucency. We have just published a system for multispectral reproduction of oil paintings, which leverages 3D printing capabilities resulting in unprecedented spectral fidelity (SIGGRAPH ASIA 2018). In the context of fine art facsimile printing, we are currently interested in the selection of the type and concentration of printing inks, which is a challenging optimization problem.
Novel Computational Appearance Manufacturing Methods
Additive manufacturing brings many advantages with its ability to produce almost any shape and its unique multi-material capabilities. Yet some objects are impossible (wood, stone), or complicated (metal), or extremely slow (any large object) to make with additive manufacturing. We are developing computational tools for appearance fabrication using other manufacturing technologies. Particularly, we are interested in physical editing of existing products using, for example, direct ink writing, subtractive milling or laser marking.