Ronald Richter
About
I am Ph.D. student working with the Computer Graphics Group at the Technical University of Berlin. My supervisor is Prof. Marc Alexa. In 2010 I graduated with a diploma (M.Sc.) in computer science at Technical University of Berlin.
My research interests in computer graphics range from image processing and retrieval over geometry processing (in particular segmentation and tessellation) to digital fabrication.
Publications
Constrained Modeling of 3-valent Meshes Using a Hyperbolic Deformation Metric Computer Graphics Forum, 2016
Ronald Richter, Jan Eric Kyprianidis, Boris Springborn and Marc Alexa
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Polygon meshes with 3-valent vertices often occur as the frame of free-form surfaces in architecture, in which rigid beams are connected in rigid joints. For modelling such meshes, it is desirable to measure the deformation of the joints' shapes. We show that it is natural to represent joint shapes as points in hyperbolic 3-space. This endows the space of joint shapes with a geometric structure that facilitates computation. We use this structure to optimize meshes towards different constraints, and we believe that it will be useful for other applications as well.
Beam Meshes Computers & Graphics, 40 years of Computer Graphics in Darmstadt, 2015
Ronald Richter and Marc Alexa
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We present an approach for representing free-form geometry with a set of beams with rectangular cross-section. This requires the edges of the mesh to be free of torsion. We generate such meshes in a two step procedure: first we generate a coarse, low valence mesh approximation using a new variant of anisotropic centroidal Voronoi tessellation. Then we modify the mesh and create beams by incorporating constraints using iterative optimization. For fabrication we provide solutions for designing the joints, generating a cutting place for CNC machines, and suggesting a building sequence. The approach is demonstrated at several virtual and real results.
Mahalanobis Centroidal Voronoi Tessellations Computers & Graphics, Proc. SMI 2014
Ronald Richter and Marc Alexa
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Anisotropic centroidal Voronoi tessellations (CVT) are a useful tool for segmenting surfaces in geometric modeling. We present a new approach to anisotropic CVT, where the local distance metric is learned from the embedding of the shape. Concretely, we define the distance metric implicitly as the minimizer of the CVT energy. Constraining the metric tensors to have unit determinant leads to the optimal distance metric being the inverse covariance matrix of the data (i.e. Mahalanobis distances). We explicitly cover the case of degenerate covariance and provide an algorithm to minimize the CVT energy. The resulting technique has applications in shape approximation, particularly in the case of noisy data, where normals are unreliable. We also put our approach in the context of other techniques. Among others, we show that Variational Shape Approximation can be interpreted in the same framework by constraining the metric tensor based on another norm.
Sketch-based Shape Retrieval ACM Transactions on Graphics, Proc. SIGGRAPH 2012
Mathias Eitz, Ronald Richter, Tamy Boubekeur, Kristian Hildebrand and Marc Alexa
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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.
Photosketcher: interactive sketch-based image synthesis IEEE Computer Graphics and Applications 2011
Mathias Eitz, Ronald Richter, Kristian Hildebrand, Tamy Boubekeur and Marc Alexa
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We introduce Photosketcher, an interactive system for progressively synthesizing novel images using only sparse user sketches as the input. Compared to existing approaches for synthesising images from parts of other images, Photosketcher works on the image content exclusively, no keywords or other metadata associated with the images is required. Users sketch the rough shape of a desired image part and we automatically search a large collection of images for images containing that part. The search is based on a bag-of-features approach using local descriptors for translation invariant part retrieval. The compositing step again is based on user scribbles: from the scribbles we predict the desired part using Gaussian Mixture Models and compute an optimal seam using Graphcut. Optionally, Photosketcher lets users blend the composite image in the gradient domain to further reduce visible seams. We demonstrate that the resulting system allows interactive generation of complex images.
Interactively browsing large image collections ACM SIGGRAPH Talks 2010
Ronald Richter, Mathias Eitz and Marc Alexa
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In this paper, we propose a novel method for interactively browsing large image collections, making the user an integral part of the interactive exploration by repeatedly exploiting the amazing ability of humans to quickly identify relevant images from a large set. The method requires only minimal input effort: users simply point to the image in the current display that seems most attractive to them. The system then assembles a representative set of other images from the collection that are likely in its perceptual vicinity. The resulting browsing approach is -- even for novice users -- extremely simple to use and enables an interactive exploration of the collection as well as target-oriented selection towards a specific mental image model.
