B-SLAM-SIM: A Novel Approach to Evaluate the Fusion of Visual SLAM and GPS by Example of Direct Sparse Odometry and Blender - Kalisz, Adam; Particke, Florian; Penk, Dominik; Hiller, Markus; Thielecke, Jörn (Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, 2019)
In order to account for sensor deficiancies, usually a multi-sensor approach is used where various sensors complement each other. However, synchronization of highly accurate Global Positioning System (GPS) and video measurements requires specialized hardware which is not straightforward to set up. This paper proposes a full simulation environment for data generation and evaluation of Visual Simultaneous Localization and Mapping (Visual SLAM) and GPS based on free and open software. Specifically, image data is created by rendering a virtual environment where camera effects such as Motion Blur and Rolling Shutter can be added. Consequently, a ground truth camera trajectory is available and can be distorted via additive Gaussian noise to understand all parameters involved in the use of fusion algorithms such as the Kalman Filter. The proposed evaluation framework will be published as open source online at https://master.kalisz.co for free use by the research communit
Keywords: Fusion; Global Positioning System; Visual Simultaneous Localization and Mapping; GPS; SLAM; Simulation; Blender

DeNos22: A Pipeline to Learn Object Tracking Using Simulated Depth - Penk, Dominik; Horn, Maik; Strohmeyer, Christoph; Bauer, Frank; Stamminger, Marc (Volume 5: VISAPP, 2023)
Abstract: We propose a novel pipeline to construct a learning based 6D object pose tracker, which is solely trained on synthetic depth images. The only required input is a (geometric) CAD model of the target object. Training data is synthesized by rendering stereo images of the CAD model, in front of a large variety of backgrounds generated by point-based re-renderings of prerecorded background scenes. Finally, depth from stereo is applied in order to mimic the behavior of depth sensors. The synthesized training input generalizes well to real-world scenes, but we further show how to improve real-world inference using robust estimators to counteract the errors introduced by the sim-to-real transfer. As a result, we show that our 6D pose trackers achieve state-of-the-art results without any annotated real-world data, solely based on a CAD-model of the target object.<br

DETERMINATION of DETAILED MORPHOLOGICAL FEATURES for PHENOTYPING of SUGAR BEET PLANTS USING 3D-STEREOSCOPIC DATA - Scholz, O.; Uhrmann, F.; Wolff, A.; Pieger, K.; Penk, Dominik (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019)
The sugar beet is the primary source of sugar in Europe and large parts of the world. Tools to determine plant traits with high precision and high throughput are required for the breeding process to quantify the effects of genetic and environmental factors on plant development and yield. In this work, we propose a method to gain a limited yet significant set of descriptive parameters for sugar beet plants. Using optical methods, a 3D representation of each plant is generated and subsequently segmented manually. A customized leaf model developed specifically for sugar beet plants then models the leaves, yielding a vector of descriptive parameters for each leaf. The resulting data is then compared to plant assessments of the same plants performed by sugar beet experts in order to evaluate the viability of automatic plant assessment in the sugar beet breeding process.
CRIS-Team Scopus Importer:2021-04-20
Keywords: 3D; Leaf model; Phenotyping; Plant modelling; Plant morphology; Precision Farming; Remote sensing; Sugar beet

Real-time Depth of Field using Multi-Layer Filtering - Selgrad, Kai; Reintges, Christian; Penk, Dominik; Wagner, Pascal; Stamminger, Marc (Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games 2015, 2015)
We present a novel technique for rendering depth of field that addresses difficult overlap cases, such as close, but out-of-focus, geometry in the near-field. Such scene configurations are not managed well by state-of-the-art post-processing approaches since essential information is missing due to occlusion. Our proposed algorithm renders the scene from a single camera position and computes a layered image using a single pass by constructing per-pixel lists. These lists can be filtered progressively to generate differently blurred representations of the scene. We show how this structure can be exploited to generate depth of field in real-time, even in complicated scene constellations.
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD9.realti
Keywords: GRK-1773

Reflective surface reconstruction from inverse deflectometric measurements - Penk, Dominik; Sturm, Roman; Seifert, Lars; Stamminger, Marc; Greiner, Günther (VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2020)
Reconstructing reflective surfaces is a difficult task since most algorithms rely on photometric consistency between multiple views on the target object. However specular reflections are highly view dependent and thus violate this assumption. Previous work therefore often incorporates additional information, like polarization or the distortion of a known pattern, to perform specular surface reconstruction. We present a novel analysis by synthesis approach that defines an optimization problem using samples directly on the reconstructed surface. Based on this framework we describe two different setups for reconstruction, one using a line laser to create a reflection pattern and a second one, that uses point measurements to provide ray-measurement correspondences achieving improved accuracy.
CRIS-Team Scopus Importer:2020-04-28
Keywords: Numerical Optimization; Quality Control; Reconstruction; Simulation

Visualization Aided Interface Reconstruction - Penk, Dominik; Müller, Jonas; Felfer, Peter; Grosso, Roberto; Stamminger, Marc (VMV 2020, 25th International Symposium on Vision, Modeling, and Visualization, 2020)
Modern atom probe tomography measurements generate large point clouds of atomic locations in solids. A common analysis task in these datasets is to put the location of specific atom types in relation to crystallographic features such as the interface between two crystals (grain boundaries). In cases where these features represent surfaces, their extraction is carried out manually in most cases. In this paper we propose a method for semi automatic extraction of such two dimensional manifold and non-manifold surfaces from a given dataset. We first aid the user to filter the atom data by providing an interactive visualization of the dataset tailored towards enhancing these interfaces. Once a desired set of points representing the interface is found, we provide an automatic surface extraction method to compute an explicit parametric representation of the visualized surface. In case of non-manifold interface structures, this parametric representation is then used to calculate the intersections of the individual manifold parts of the interfaces.<br