Rene Weller

A unified approach for physically-based simulations and haptic rendering

Based on our new geometric data structure, the inner sphere trees, we present fast and stable algorithms for different kinds of collision detection queries between rigid objects at haptic rates. Namely, proximity queries and the penetration volume, which is related to the water displacement of the overlapping region and thus corresponds to a physically motivated force. The latter allows us to define a novel penalty-based collision response scheme that provides continuous forces and torques which are applicable to physically-based simulations as well as to haptic rendering scenarios. Moreover, we present a time-critical version of the penetration volume computation that is able to achieve very tight bounds within a fixed budget of query time. The main idea of our new data structure is to bound the object from the inside with a set of non-overlapping bounding volumes. The results show performance at haptic rates both for proximity and penetration volume queries, independent from the polygon count of the objects.

Inner sphere trees for proximity and penetration queries

We present a novel geometric data structure for approximate collision detection at haptic rates between rigid objects. Our data structure, which we call inner sphere trees, supports different kinds of queries, namely, proximity queries and a new method for interpenetration computation, the penetration volume, which is related to the water displacement of the overlapping region and, thus, corresponds to a physically motivated force. The main idea is to bound objects from the inside with a set of non-overlapping spheres. Based on such sphere packings, a “inner bounding volume hierarchy” can be constructed. In order to do so, we propose to use an AI clustering algorithm, which we extend and adapt here. The results show performance at haptic rates both for proximity and penetration volume queries for models consisting of hundreds of thousands of polygons.

A model for the expected running time of collision detection using AABBs trees

In this paper, we propose a model to estimate the expected running time of hierarchical collision detection that utilizes AABB trees, which are a frequently used type of bounding volume (BV). We show that the average running time for the simultaneous traversal of two binary AABB trees depends on two characteristic parameters: the overlap of the root BVs and the BV diminishing factor within the hierarchies. With this model, we show that the average running time is in O(n) or even in O(logn) for realistic cases. Finally, we present some experiments that confirm our theoretical considerations. We believe that our results are interesting not only from a theoretical point of view, but also for practical applications, e. g., in time-critical collision detection scenarios where our running time prediction could help to make the best use of CPU time available.

A benchmarking suite for 6-DOF real time collision response algorithms

We present a benchmarking suite for rigid object collision detection and collision response schemes. The proposed benchmarking suite can evaluate both the performance as well as the quality of the collision response. The former is achieved by densely sampling the configuration space of a large number of highly detailed objects; the latter is achieved by a novel methodology that comprises a number of models for certain collision scenarios. With these models, we compare the force and torque signals both in direction and magnitude. Our device-independent approach allows objective predictions for physically-based simulations as well as 6-DOF haptic rendering scenarios. In the results, we show a comprehensive example application of our benchmarks comparing two quite different algorithms utilizing our proposed benchmarking suite. This proves empirically that our methodology can become a standard evaluation framework.

Inner Sphere Trees and Their Application to Collision Detection

We present a novel geometric data structure for approximate collision detection at haptic rates between rigid objects. Our data structure, which we call inner sphere trees, supports different kinds of queries, namely, proximity queries and the penetration volume, which is related to the water displacement of the overlapping region and, thus, corresponds to a physically motivated force. Moreover, we present a time-critical version of the penetration volume computation that is able to achieve very tight upper and lower bounds within a fixed budget of query time. The main idea is to bound the object from the inside with a bounding volume hierarchy, which can be constructed based on dense sphere packings. In order to build our new hierarchy, we propose to use an AI clustering algorithm, which we extend and adapt here. The results show performance at haptic rates both for proximity and penetration volume queries for models consisting of hundreds of thousands of polygons.

ProtoSphere: a GPU-assisted prototype guided sphere packing algorithm for arbitrary objects

Filling objects densely with sets of non overlapping spheres has been investigated for centuries. Once started as a pure intellectual challenge, today, sphere packings have diverse applications in a wide spectrum of scientific and engineering disciplines, for example in automated radiosurgical treatment planning, investigation of processes such as sedimentation, compaction and sintering, in powder metallurgy for three-dimensional laser cutting, in cutting different natural crystals, the discrete element method is based on them, and so forth.

Kinaptic — Techniques and insights for creating competitive accessible 3D games for sighted and visually impaired users

We present the first accessible game that allows a fair competition between sighted and blind people in a shared virtual 3D environment. We use an asymmetric setup that allows touchless interaction via Kinect, for the sighted player, and haptic, wind, and surround audio feedback, for the blind player. We evaluated our game in an in-the-wild study. The results show that our setup is able to provide a mutually fun game experience while maintaining a fair winning chance for both players. Based on our study, we also suggest guidelines for future developments of games for visually impaired people that could help to further include blind people into society.

Multi agent system optimization in virtual vehicle testbeds

Modelling, simulation, and optimization play a crucial role in the development and testing of autonomous vehicles. The ability to compute, test, assess, and debug suitable configurations reduces the time and cost of vehicle development. Until now, engineers are forced to manually change vehicle configurations in virtual testbeds in order to react to inappropriate simulated vehicle performance. Such manual adjustments are very time consuming and are also often made ad-hoc, which decreases the overall quality of the vehicle engineering process. In order to avoid this manual adjustment as well as to improve the overall quality of these adjustments, we present a novel comprehensive approach to modelling, simulation, and optimization of such vehicles. Instead of manually adjusting vehicle configurations, engineers can specify simulation goals in a domain specific modelling language. The simulated vehicle performance is then mapped to these simulation goals and our multi-agent system computes for optimized vehicle configuration parameters in order to satisfy these goals. Consequently, our approach does not need any supervision and gives engineers visual feedback of their vehicle configuration expectations. Our evaluation shows that we are able to optimize vehicle configuration sets to meet simulation goals while maintaining real-time performance of the overall simulation.

Parallel Collision Detection in Constant Time

We prove that the maximum number of intersecting pairs spheres between two sets of polydisperse sphere packings is linear in the worst case. This observation is the basis for a new collision detection algorithm. Our new approach guarantees a linear worst case running time for arbitrary 3D objects. Additionally, we present a parallelization of our new algorithm that runs in constant time, even in the worst case. Consequently, it is perfectly suited for all time-critical environments that allow only a fixed time budget for finding collision. Our implementation using CUDA shows collision detection at haptic rates for complex objects.

kDet: Parallel Constant Time Collision Detection for Polygonal Objects

We define a novel geometric predicate and a class of objects that enables us to prove a linear bound on the number of intersecting polygon pairs for colliding 3D objects in that class. Our predicate is relevant both in theory and in practice: it is easy to check and it needs to consider only the geometric properties of the individual objects – it does not depend on the configuration of a given pair of objects. In addition, it characterizes a practically relevant class of objects: we checked our predicate on a large database of real‐world 3D objects and the results show that it holds for all but the most pathological ones.