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The automatic creation of digital art has a long history in computer graphics. In this work, we focus on approximating input images to mimic artwork by the artist Kumi Yamashita, as well as the popular scribble art style. Both have in common that the artists create the works by using a single, contiguous thread (Yamashita) or stroke (scribble) that is placed seemingly at random when viewed at close range, but perceived as a tone-mapped picture when viewed from a distance. Our approach takes a rasterized image as input and creates a single, connected path by iteratively sampling a set of candidate segments that extend the current path and greedily selecting the best one. The candidates are sampled according to art style specific constraints, i.e. conforming to continuity constraints in the mathematical sense for the scribble art style. To model the perceptual discrepancy between close and far viewing distances, we minimize the difference between the input image and the image created by rasterizing our path after applying the contrast sensitivity function, which models how human vision blurs images when viewed from a distance. Our approach generalizes to colored images by using one path per color. We evaluate our approach on a wide range of input images and show that it is able to achieve good results for both art styles in grayscale and color.

Boolean operators are an essential tool in a wide range of geometry processing and CAD/CAM tasks. We present a novel method, EMBER, to compute Boolean operations on polygon meshes which is exact, reliable, and highly performant at the same time. Exactness is guaranteed by using a plane-based representation for the input meshes along with recently introduced homogeneous integer coordinates. Reliability and robustness emerge from a formulation of the algorithm via generalized winding numbers and mesh arrangements. High performance is achieved by avoiding the (pre-)construction of a global acceleration structure. Instead, our algorithm performs an adaptive recursive subdivision of the scene’s bounding box while generating and tracking all required data on the fly. By leveraging a number of early-out termination criteria, we can avoid the generation and inspection of regions that do not contribute to the output. With a careful implementation and a work-stealing multi-threading architecture, we are able to compute Boolean operations between meshes with millions of triangles at interactive rates. We run an extensive evaluation on the Thingi10K dataset to demonstrate that our method outperforms state-of-the-art algorithms, even inexact ones like QuickCSG, by orders of magnitude.

The use of autoencoders for shape editing or generation through latent space manipulation suffers from unpredictable changes in the output shape. Our autoencoder-based method enables intuitive shape editing in latent space by disentangling latent sub-spaces into style variables and control points on the surface that can be manipulated independently. The key idea is adding a Lipschitz-type constraint to the loss function, i.e. bounding the change of the output shape proportionally to the change in latent space, leading to interpretable latent space representations. The control points on the surface that are part of the latent code of an object can then be freely moved, allowing for intuitive shape editing directly in latent space. We evaluate our method by comparing to state-of-the-art data-driven shape editing methods. We further demonstrate the expressiveness of our learned latent space by leveraging it for unsupervised part segmentation.

The research project HDV-Mess aims at a currently missing, but very crucial component for addressing important challenges in the field of connected and automated driving on public roads. The goal is to record traffic events at various relevant locations with high accuracy and to collect real traffic data as a basis for the development and validation of current and future sensor technologies as well as automated driving functions. For this purpose, it is necessary to develop a concept for a mobile modular system of measuring stations for highly accurate traffic data acquisition, which enables a temporary installation of a sensor and communication infrastructure at different locations. Within this paper, we first discuss the project goals before we present our traffic detection concept using mobile modular intelligent transport systems stations (ITS-Ss). We then explain the approaches for data processing of sensor raw data to refined trajectories, data communication, and data validation.

We present octree-embedded BSPs, a volumetric mesh data structure suited for performing a sequence of Boolean operations (iterated CSG) efficiently. At its core, our data structure leverages a plane-based geometry representation and integer arithmetics to guarantee unconditionally robust operations. These typically present considerable performance challenges which we overcome by using custom-tailored fixed-precision operations and an efficient algorithm for cutting a convex mesh against a plane. Consequently, BSP Booleans and mesh extraction are formulated in terms of mesh cutting. The octree is used as a global acceleration structure to keep modifications local and bound the BSP complexity. With our optimizations, we can perform up to 2.5 million mesh-plane cuts per second on a single core, which creates roughly 40-50 million output BSP nodes for CSG. We demonstrate our system in two iterated CSG settings: sweep volumes and a milling simulation.