Computer Graphics is about digital models for threedimensional geometric objects as well as images. These shapes and images may represent approximations of the real world or could be synthetic, i.e., exist only in the computer. Goals of computer graphics research are the generation of plausible and informative images, and computation with reasonable resources, i.e. in a short amount of time with little storage requirements. The models and algorithms for this task combine knowledge from different areas of mathematics and computer science.

Prof. Alexa had been elected to chair the technical program of SIGGRAPH 2013. SIGGRAPH regularly gathers thousands of scientists and professionals in the visual effects industry, and its technical program is the most prestigious and selective in the field. On the left, he opens the fast forward, a 30 second presentation for each of the accepted papers.

© Kristian Hildebrand

Most additive manufacturing technologies work by layering, i.e. slicing the shape and then generating each slice independently. This introduces an anisotropy into the process, often as different accuracies in the tangential and normal directions, but also in terms of other parameters such as build speed or tensile strength and strain. We model this as an anisotropic cubic element. Our approach then finds a compromise between modeling each part of the shape individually in the best possible direction and using one direction for the whole shape part. In particular, we compute an orthogonal basis and consider only the three basis vectors as slice normals (i.e. fabrication directions). Then we optimize a decomposition of the shape along this basis so that each part can be consistently sliced along one of the basis vectors.

In simulation, we show that this approach is superior to slicing the whole shape in one direction, only. It also has clear benefits if the shape is larger than the build volume of the available equipment.

The Dagstuhl seminar on ‘Computational Aspects of Fabrication’ organized by Alexa, Bickel, Matusik, McMains, Rushmeier has been accepted. More information here.

A radio feature about our recent research on sketch-based based modeling and sketch recognition on Deutschlandradio Kultur (German only): DRadio Kultur – Strichzeichnungen

- Scene assembled in a few minutes using our sketch-based retrieval system
- © Mathias Eitz

We develop a system for 3D object retrieval based on sketched feature lines as input. For objective evaluation, we collect a large number of query sketches from human users that are related to an existing data base of objects. The sketches turn out to be generally quite abstract with large local and global deviations from the original shape. Based on this observation, we decide to use a bag-of-features approach over computer generated line drawings of the objects. We develop a targeted feature transform based on Gabor filters for this system. We can show objectively that this transform is better suited than other approaches from the literature developed for similar tasks. Moreover, we demonstrate how to optimize the parameters of our, as well as other approaches, based on the gathered sketches. In the resulting comparison, our approach is significantly better than any other system described so far.

Please see our project page for more details.

- We explore how humans sketch and recognize objects from 250 categories
- © Mathias Eitz

Humans have used sketching to depict our visual world since prehistoric times. Even today, sketching is possibly the only rendering technique readily available to all humans. This paper is the first large scale exploration of human sketches. We analyze the distribution of non-expert sketches of everyday objects such as ‘teapot’ or ‘car’. We ask humans to sketch objects of a given category and gather 20,000 unique sketches evenly distributed over 250 object categories. With this dataset we perform a perceptual study and find that humans can correctly identify the object category of a sketch 73% of the time. We compare human performance against computational recognition methods. We develop a bag-of-features sketch representation and use multi-class support vector machines, trained on our sketch dataset, to classify sketches. The resulting recognition method is able to identify unknown sketches with 56% accuracy (chance is 0.4%). Based on the computational model, we demonstrate an interactive sketch recognition system. We release the complete crowd-sourced dataset of sketches to the community.

Please see our project page for more details.

- cardboard model
- © Kristian Hildebrand

We introduce an algorithm and representation for fabricating 3D shape abstractions using mutually intersecting planar cut-outs. The planes have prefabricated slits at their intersections and are assembled by sliding them together. Based on an analysis of construction rules, we propose an extended binary space partitioning tree as an efficient representation of such cardboard models which allows us to quickly evaluate the feasibility of newly added planar elements. The complexity of insertion order quickly increases with the number of planar elements and manual analysis becomes intractable. We provide tools for generating cardboard sculptures with guaranteed constructibility.

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