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R.: Multicore collision detection between deformable models
 In SIAM/ACM Joint Conf. on Geometric and Solid & Physical Modeling (2009
"... We present a new parallel algorithm for interactive and continuous collision detection between deformable models. Our algorithm performs incremental hierarchical computations between successive frames and parallelizes the computation among multiple cores on current CPUs. The main computations incl ..."
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We present a new parallel algorithm for interactive and continuous collision detection between deformable models. Our algorithm performs incremental hierarchical computations between successive frames and parallelizes the computation among multiple cores on current CPUs. The main computations include front building and updating and performing the elementary tests between the triangle primitives. The overall algorithm can perform inter and intraobject collisions at interactive rates on current commodity processors on models with many tens of thousands of triangles. In practice, the performance of our algorithm almost scales linearly with the number of cores.
Collisionstreams: Fast GPUbased collision detection for deformable models
 In ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games
, 2011
"... into a funnel and pass through it under the pressure of a ball. This model has 47K vertices, 92K triangles, and a lot of selfcollisions. Our novel GPUbased CCD algorithm takes 4:4ms and 10ms per frame to compute all the collisions on a NVIDIA GeForce GTX 480 and a NVIDIA GeForce GTX 285, respectiv ..."
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Cited by 9 (1 self)
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into a funnel and pass through it under the pressure of a ball. This model has 47K vertices, 92K triangles, and a lot of selfcollisions. Our novel GPUbased CCD algorithm takes 4:4ms and 10ms per frame to compute all the collisions on a NVIDIA GeForce GTX 480 and a NVIDIA GeForce GTX 285, respectively. We present a fast GPUbased streaming algorithm to perform collision queries between deformable models. Our approach is based on hierarchical culling and reduces the computation to generating different streams. We present a novel stream registration method to compact the streams and efficiently compute the potentially colliding pairs of primitives. We also use a deferred front tracking method to lower the memory overhead. The overall algorithm has been implemented on different GPUs and we have evaluated its performance on nonrigid and deformable simulations. We highlight our speedups over prior CPUbased and GPUbased algorithms. In practice, our algorithm can perform interobject and intraobject computations on models composed of hundreds of thousands of triangles in tens of milliseconds. 1
Lazy Work Stealing for Continuous Hierarchy Traversal on Deformable Bodies
"... Abstract: This study presents the results of research in dynamic load balancing for Continuous Collision Detection (CCD) using Bounding Volumes Hierarchies (BVHs) on Graphics Processing Units (GPUs). Hierarchy traversal is a challenging problem for GPU computing, since the work load of traversal ha ..."
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Abstract: This study presents the results of research in dynamic load balancing for Continuous Collision Detection (CCD) using Bounding Volumes Hierarchies (BVHs) on Graphics Processing Units (GPUs). Hierarchy traversal is a challenging problem for GPU computing, since the work load of traversal has a very dynamic nature. Current research resulted in methods to dynamically balance load as the traversal is evaluated. Unfortunately, current gridbased GPU computing interfaces are not well suited for this type of computing and load balancing code can generate excessive overhead. This work presents a novel algorithm to address some of the most glaring problems. The algorithm uses the new concept of lazy work stealing, which tries to get the most out of the parallel capabilities of GPUs by greedy work stealing and lazy work evaluation. Also, the algorithm is designed to augment shared memory usage per block and diminish CPUGPU context exchange penalties. 1
Efficient Configuration Space Construction and Optimization
, 2013
"... The configuration space is a fundamental concept that is widely used in algorithmic robotics. Many applications in robotics, computeraided design, and related areas can be reduced to computational problems in terms of configuration spaces. In this dissertation, we address three main computational ..."
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The configuration space is a fundamental concept that is widely used in algorithmic robotics. Many applications in robotics, computeraided design, and related areas can be reduced to computational problems in terms of configuration spaces. In this dissertation, we address three main computational challenges related to configuration spaces: 1) how to efficiently compute an approximate representation of highdimensional configuration spaces; 2) how to efficiently perform geometric, proximity, and motion planning queries in highdimensional configuration spaces; and 3) how to model uncertainty in configuration spaces represented by noisy sensor data. We present new configuration space construction algorithms based on machine learning and geometric approximation techniques. These algorithms perform collision queries on many configuration samples. The collision query results are used to compute an approximate representation for the configuration space, which quickly converges to the exact configuration space. We highlight the efficiency of our algorithms for penetration depth computation and instancebased motion planning. We also present parallel GPUbased algorithms to accelerate the performance of optimization and search computations in configuration spaces. In particular, we design efficient GPUbased parallel knearest neighbor and parallel collision detection algorithms and use these algorithms to accelerate motion planning. In order to extend configuration space algorithms to handle noisy sensor data arising from realworld robotics applications, we model the uncertainty in the configuration space by formulating the collision probabilities for noisy data. We use these algorithms to perform reliable motion planning for the PR2 robot.
Discrete Differential Geometry of Thin Materials for Computational Mechanics
, 2013
"... Instead of applying numerical methods directly to governing equations, another approach to computation is to discretize the geometric structure specific to the problem first, and then compute with the discrete geometry. This structurerespecting discretedifferentialgeometric (DDG) approach often ..."
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Instead of applying numerical methods directly to governing equations, another approach to computation is to discretize the geometric structure specific to the problem first, and then compute with the discrete geometry. This structurerespecting discretedifferentialgeometric (DDG) approach often leads to new algorithms that more accurately track the physically behavior of the system with less computational effort. Thin objects, such as pieces of cloth, paper, sheet metal, freeform masonry, and steelglass structures are particularly rich in geometric structure and so are wellsuited for DDG. I show how understanding the geometry of time integration and contact leads to new algorithms, with strong correctness guarantees, for simulating thin elastic objects in contact; how the performance of these algorithms can be dramatically improved without harming the geometric structure, and thus the guarantees, of the original formulation; how the geometry of static equilibrium can be used to efficiently solve design problems related to masonry or glass buildings; and how discrete developable surfaces can be used to model thin sheets undergoing isometric deformation.
Interactive Continuous Collision Detection for Topology Changing Models Using Dynamic Clustering
"... Figure 1 : This simulation is generated using finite element solver on a mesh with about 9K triangles [Tang et al. 2011b]. Our novel continuous collision detection (CCD) algorithm takes about 1.1 second (on average) on a single CPU core to perform all intraobject and selfcollision queries. It is ..."
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Figure 1 : This simulation is generated using finite element solver on a mesh with about 9K triangles [Tang et al. 2011b]. Our novel continuous collision detection (CCD) algorithm takes about 1.1 second (on average) on a single CPU core to perform all intraobject and selfcollision queries. It is about 5X faster than prior CCD algorithms for deformable models. Abstract We present a fast algorithm for continuous collision detection between deformable models. Our approach performs no precomputation and can handle general triangulated models undergoing topological changes. We present a fast decomposition algorithm that represents the mesh boundary using hierarchical clusters and only needs to perform intercluster collision checks. The key idea is to compute such clusters quickly and merge them to generate a dynamic bounding volume hierarchy. The overall approach reduces the overhead of computing the hierarchy and also reduces the number of false positives. We highlight the the algorithm's performance on many complex benchmarks generated from medical simulations and crash analysis. In practice, we observe 1.4 to 5 times speedup over prior CCD algorithms for deformable models in our benchmarks.
Analysis of Axis Aligned Bounding Box in Distributed Virtual Environment
"... Axis Aligned Bounding Box (AABB) is the simple method for object collision detection, but it has limitation in detection process. In decades, some better methods have been generated such as Oriented Bounding Box (OBB) and HPCCD. Unfortunately, these methods are not used in DVE. This paper aims to an ..."
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Axis Aligned Bounding Box (AABB) is the simple method for object collision detection, but it has limitation in detection process. In decades, some better methods have been generated such as Oriented Bounding Box (OBB) and HPCCD. Unfortunately, these methods are not used in DVE. This paper aims to analyze why most DVEs still use AABB in detecting objects collision in the environment. This research begins with developing the suitable DVE. The DVE should make many users collaborate with each other, and it has physics activities such as gravity pole, movement, etc. Each user is able to create objects and they should be visible to other users. To detect the object collision, AABB is implemented in the DVE. Further, to analyze the collision detection process and the performance of DVE, there are two parameters used, i.e. runtime and frame rate of simulation application. The experiment results show that adding the computation workload into AABB on DVE increases the runtime significantly compared with regular application. The lack of performance is also shown by the application frame rates in which strictly decrease so that the DVE performance degrades.