Abstract Quantifying the predictive uncertainty emerged as a possible solution to common challenges like overconfidence, lack of explainability, and robustness of deep neural networks, albeit one that is often computationally expensive. Many real-world applications are multi-modal in nature and hence benefit from multi-task learning. In autonomous driving or robotics, for example, the joint solution of semantic segmentation and monocular depth estimation has proven to be valuable. To this end, we introduce EMUFormer, a novel student-teacher distillation approach for efficient multi-task uncertainties in the context of joint semantic segmentation and monocular depth estimation. By leveraging the predictive uncertainties of the teacher, EMUFormer achieves new state-of-the-art results on Cityscapes and NYUv2 and additionally estimates reliable predictive uncertainties for both tasks that are comparable or superior to a Deep Ensemble despite being an order of magnitude more efficient to compute. These findings even extend to out-of-domain and domain adaptation scenarios, highlighting EMUFormer’s remarkable reliability.

We provide a generic framework for the hand–eye calibration of vision-guided industrial robots. In contrast to traditional methods, we explicitly model the uncertainty of the robot in a stochastically founded way. Albeit the repeatability of modern industrial robots is high, their absolute accuracy typically is much lower. This uncertainty—especially if not considered—deteriorates the result of the hand–eye calibration. Our proposed framework does not only result in a high accuracy of the computed hand–eye pose but also provides reliable information about the uncertainty of the robot. It further provides corrected robot poses for a convenient and inexpensive robot calibration. Our framework is computationally efficient and generic in several regards. It supports the use of a calibration target as well as self-calibration without the need for known 3-D points. It optionally enables the simultaneous calibration of the interior camera parameters. The framework is also generic with regard to the robot type and, hence, supports antropomorphic as well as selective compliance assembly robot arm (SCARA) robots, for example. Simulated and real experiments show the validity of the proposed methods. An extensive evaluation of our framework on a public dataset shows a considerably higher accuracy than 15 state-of-the-art methods.

We provide a generic framework for the hand– eye calibration of vision-guided industrial robots. In contrast to traditional methods, we explicitly model the uncertainty of the robot in a statistically sound manner. Albeit the precision of modern industrial robots is high, their absolute accuracy typically is much lower. This uncertainty — if not considered — deteriorates the result of the hand–eye calibration. Our proposed framework not only results in a high accuracy of the computed hand–eye pose but also provides reliable information about the uncertainty of the robot. It further provides corrected robot poses for a convenient and inexpensive robot calibration. Our framework is generic in several respects: It supports the use of a calibration target as well as self-calibration without the need for known 3D points. It optionally allows the simultaneous calibration of the interior camera parameters. The framework is also generic with regard to the robot type, and hence supports articulated as well as SCARA robots, for example. Simulated and real experiments show the validity of the proposed methods.

Abstract We propose a camera model for line-scan cameras with telecentric lenses. The camera model assumes a linear relative motion with constant velocity between the camera and the object. It allows to model lens distortions, while supporting arbitrary positions of the line sensor with respect to the optical axis. We comprehensively examine the degeneracies of the camera model and propose methods to handle them. Furthermore, we examine the relation of the proposed camera model to affine cameras. In addition, we propose an algorithm to calibrate telecentric line-scan cameras using a planar calibration object. We perform an extensive evaluation of the proposed camera model that establishes the validity and accuracy of the proposed model. We also show that even for lenses with very small lens distortions, the distortions are statistically highly significant. Therefore, they cannot be omitted in real-world applications.

Abstract Climate change imposes tremendous, complex challenges on humanity. Thoughtfully designed games can support solving them. This article presents a review of climate games and thereby updates the review conducted by Reckien and Eisenack in 2011. It provides an overview of published climate games and reveals the development of the field over the last years. A total of 119 climate games were found whereof 52 were already part of the review of 2011. The broad variety of discovered games indicates a lively community and different settings where such tools are being applied. A substantial number of games addressed topics such as international climate conferences, global impacts of global decisions, and effects of individual decisions on their local environment. Other topics, however, were largely absent. They included - amongst others - the connection between climate change and health, and games that bride local and global levels. Furthermore, the game types “video games” and “alternate reality games” were not applied frequently. Both, the absent topics and the scarcely used game types open up possibilities to develop the field. Forty-six per cent of the games listed by Reckien and Eisenack seem to have disappeared and could not be found for this review, an observation that may need further attention.

We propose a camera model for cameras with hypercentric lenses. Because of their geometry, hypercentric lenses allow to image the top and the sides of an object simultaneously. This makes them useful for certain inspections tasks, for which otherwise multiple images would have to be acquired and stitched together. After describing the projection geometry of hypercentric lenses, we derive a camera model for hypercentric lenses that is intuitive for the user. Furthermore, we describe how to determine the parameter values of the model by calibrating the camera with a planar calibration object. We also apply our camera model to two example applications: in the first application, we show how two cameras with hypercentric lenses can be used for dense 3D reconstruction. For an efficient reconstruction, the images are rectified such that corresponding points occur in the same image row. Standard rectification methods would result in perspective distortions in the images that would prevent stereo matching algorithms from robustly establishing correspondences. Therefore, we propose a new rectification method for objects that are approximately cylindrical in shape, which enables a robust and efficient reconstruction. In the second application, we show how to unwrap cylindrical objects to simplify further inspection tasks. For the unwrapping, the pose of the cylinder must be known. We show how to determine the pose of the cylinder based on a single camera image and based on two images of a stereo camera setup.

This paper describes an approach for recognizing instances of a 3D object in a single camera image and for determining their 3D poses. A hierarchical model is generated solely based on the geometry information of a 3D CAD model of the object. The approach does not rely on texture or reflectance information of the object's surface, making it useful for a wide range of industrial and robotic applications, e.g., bin-picking. A hierarchical view-based approach that addresses typical problems of previous methods is applied: It handles true perspective, is robust to noise, occlusions, and clutter to an extent that is sufficient for many practical applications, and is invariant to contrast changes. For the generation of this hierarchical model, a new model image generation technique by which scale-space effects can be taken into account is presented. The necessary object views are derived using a similarity-based aspect graph. The high robustness of an exhaustive search is combined with an efficient hierarchical search. The 3D pose is refined by using a least-squares adjustment that minimizes geometric distances in the image, yielding a position accuracy of up to 0.12 percent with respect to the object distance, and an orientation accuracy of up to 0.35 degree in our tests. The recognition time is largely independent of the complexity of the object, but depends mainly on the range of poses within which the object may appear in front of the camera. For efficiency reasons, the approach allows the restriction of the pose range depending on the application. Typical runtimes are in the range of a few hundred ms.

This paper addresses the problem of recognizing free-form 3D objects in point clouds. Compared to traditional approaches based on point descriptors, which depend on local information around points, we propose a novel method that creates a global model description based on oriented point pair features and matches that model locally using a fast voting scheme. The global model description consists of all model point pair features and represents a mapping from the point pair feature space to the model, where similar features on the model are grouped together. Such representation allows using much sparser object and scene point clouds, resulting in very fast performance. Recognition is done locally using an efficient voting scheme on a reduced two-dimensional search space. We demonstrate the efficiency of our approach and show its high recognition performance in the case of noise, clutter and partial occlusions. Compared to state of the art approaches we achieve better recognition rates, and demonstrate that with a slight or even no sacrifice of the recognition performance our method is much faster then the current state of the art approaches.

This paper provides a method for recognizing 3D objects in a single camera image and for determining their 3D poses.

UNLABELLED: Tropical is a software for simulation and parameter estimation of reaction-diffusion models. Based on spatio-temporal microscopy images, Tropical estimates reaction and diffusion coefficients for user-defined models. Tropical allows the investigation of systems with an inhomogeneous distribution of molecules, making it well suited for quantitative analyses of microscopy experiments such as fluorescence recovery after photobleaching (FRAP). AVAILABILITY: Tropical is available free of charge for academic use at http://www.dkfz.de/tbi/projects/modellingAndSimulationOfCelluarSystems/tropical.jsp after signing a material transfer agreement.