A Brief Overview of Collision Detection

Weller, René

doi:10.1007/978-3-319-01020-5_2

Cited by 17 publications

(5 citation statements)

References 158 publications

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“…Faults in collision detection may occur for different reasons: (i) due to the absence of the collision property in an object, (ii) when there is great complexity in the mesh that represents the object, which causes a high computational cost for collision detection, (iii) in scenarios in which geometric primitives are used that do not fit the shape of the object correctly or even (iv) due to the complexity in calculating the collision when multiple geometric primitives are used to detect collisions in a single object [32].…”

Section: Collisionsmentioning

confidence: 99%

Exploiting deep reinforcement learning and metamorphic testing to automatically test virtual reality applications

de Andrade,

Nunes,

Delamaro

2023

Software Testing Verif & Rel

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SummaryDespite the rapid growth and popularization of virtual reality (VR) applications, which have enabled new concepts for handling and solving existing problems through VR in various domains, practices related to software engineering have not kept up with this growth. Recent studies indicate that one of the topics that is still little explored in this area is software testing, as VR applications can be built for practically any type of purpose, making it difficult to generalize knowledge to be applied. In this paper, we present an approach that combines metamorphic testing, agent‐based testing and machine learning to test VR applications, focusing on finding collision and camera‐related faults. Our approach proposes the use of metamorphic relations to detect faults in collision and camera components in VR applications, as well as the use of intelligent agents for the automatic generation of test data. To evaluate the proposed approach, we conducted an experimental study on four VR applications, and the results showed an of the solution ranging from 93% to 69%, depending on the complexity of the application tested. We also discussed the feasibility of extending the approach to identify other types of faults in VR applications. In conclusion, we discussed important trends and opportunities that can benefit both academics and practitioners.

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Section: Collisionsmentioning

confidence: 99%

Exploiting deep reinforcement learning and metamorphic testing to automatically test virtual reality applications

de Andrade,

Nunes,

Delamaro

2023

Software Testing Verif & Rel

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“…This makes the computation significantly more efficient and leads to shape approximations containing a smaller number of components. These approximations are useful for a variety of tasks, like mesh generation [31] and collision detection [62]. ACDs are also an important step in non-parametric shape segmentation methods [3,25].…”

Section: Approximate Convex Decompositionsmentioning

confidence: 99%

Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions

Gadelha¹,

RoyChowdhury²,

Sharma³

et al. 2020

Preprint

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The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years. But both of these problems suffer from relatively small training sets, creating the need for statistically efficient methods to learn 3D shape representations. In this work, we investigate the use of Approximate Convex Decompositions (ACD) as a self-supervisory signal for label-efficient learning of point cloud representations. Decomposing a 3D shape into simpler constituent parts or primitives is a fundamental problem in geometrical shape processing. There has been extensive work on such decompositions, where the criterion for simplicity of a constituent shape is often defined in terms of convexity for solid primitives. In this paper, we show that using the results of ACD to approximate a ground truth segmentation provides excellent self-supervision for learning 3D point cloud representations that are highly effective on downstream tasks. We report improvements over the state-of-theart in unsupervised representation learning on the ModelNet40 shape classification dataset and significant gains in few-shot part segmentation on the ShapeNetPart dataset. Code available at https://github.com/ matheusgadelha/PointCloudLearningACD . * equal contribution.

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“…Real-time collision detection operates in two stages, a filtering stage in which potentially colliding primitives are enumerated (broadphase) and a second stage in which exact intersection tests are performed and the responses computed (narrowphase) [52]. The distribution of computational load between the them can vary dramatically depending on the data structure.…”

Section: Performance Comparisonmentioning

confidence: 99%

Real-Time Collision Detection for Deformable Characters with Radial Fields

Friston

Steed

2019

IEEE Trans. Visual. Comput. Graphics

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Many techniques facilitate real-time collision detection against complex models. These typically work by pre-computing information about the spatial distribution of geometry into a form that can be quickly queried. When models deform though, expensive pre-computations are impractical. We present radial fields: a variant of distance fields parameterised in cylindrical space, rather than Cartesian space. This 2D parameterisation significantly reduces the memory and computation requirements of the field, while introducing minimal overhead in collision detection tests. The interior of the mesh is defined implicitly for the entire domain. Importantly, it maps well to the hardware rasteriser of the GPU. Radial fields are much more application-specific than traditional distance fields. For these applications - such as collision detection with articulated characters - however, the benefits are substantial.

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A Brief Overview of Collision Detection

Cited by 17 publications

References 158 publications

Exploiting deep reinforcement learning and metamorphic testing to automatically test virtual reality applications

Exploiting deep reinforcement learning and metamorphic testing to automatically test virtual reality applications

Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions

Real-Time Collision Detection for Deformable Characters with Radial Fields

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