Abstract The Hausdorff distance is a fundamental metric with widespread applications across various fields. However, its computation remains computationally expensive, especially for large‐scale datasets. This work targets exact point‐to‐point Hausdorff distance on point sets. In this work, we present RT‐HDIST, the first Hausdorff distance algorithm accelerated by ray‐tracing cores (RT‐cores). By reformulating the Hausdorff distance problem as a series of nearest‐neighbor searches and introducing a novel quantized voxel‐index space, RT‐HDIST achieves significant reductions in computational overhead while maintaining exact results. Extensive benchmarks demonstrate up to a two‐order‐of‐magnitude speedup over prior state‐of‐the‐art methods, underscoring RT‐HDIST's potential for real‐time and large‐scale applications.

We present a novel, linear programming (LP)-based scheduling algorithm that exploits heterogeneous multicore architectures such as CPUs and GPUs to accelerate a wide variety of proximity queries. To represent complicated performance relationships between heterogeneous architectures and different computations of proximity queries, we propose a simple, yet accurate model that measures the expected running time of these computations. Based on this model, we formulate an optimization problem that minimizes the largest time spent on computing resources, and propose a novel, iterative LP-based scheduling algorithm. Since our method is general, we are able to apply our method into various proximity queries used in five different applications that have different characteristics. Our method achieves an order of magnitude performance improvement by using four different GPUs and two hexa-core CPUs over using a hexa-core CPU only. Unlike prior scheduling methods, our method continually improves the performance, as we add more computing resources. Also, our method achieves much higher performance improvement compared with prior methods as heterogeneity of computing resources is increased. Moreover, for one of tested applications, our method achieves even higher performance than a prior parallel method optimized manually for the application. We also show that our method provides results that are close (e.g., 75 percent) to the performance provided by a conservative upper bound of the ideal throughput. These results demonstrate the efficiency and robustness of our algorithm that have not been achieved by prior methods. In addition, we integrate one of our contributions with a work stealing method. Our version of the work stealing method achieves 18 percent performance improvement on average over the original work stealing method. This result shows wide applicability of our approach.

Abstract We propose a novel, multi‐resolution method to efficiently perform large‐scale cloth simulation. Our cloth simulation method is based on a triangle‐based energy model constructed from a cloth mesh. We identify that solutions of the linear system of cloth simulation are smooth in certain regions of the cloth mesh and solve the linear system on those regions in a reduced solution space. Then we reconstruct the original solutions by performing a simple interpolation from solutions computed in the reduced space. In order to identify regions where solutions are smooth, we propose simplification metrics that consider stretching, shear, and bending forces, as well as geometric collisions. Our multi‐resolution method can be applied to many existing cloth simulation methods, since our method works on a general linear system. In order to demonstrate benefits of our method, we apply our method into four large‐scale cloth benchmarks that consist of tens or hundreds of thousands of triangles. Because of the reduced computations, we achieve a performance improvement by a factor of up to one order of magnitude, with a little loss of simulation quality.

We present a novel compressed bounding volume hierarchy (BVH) representation, random-accessible compressed bounding volume hierarchies (RACBVHs), for various applications requiring random access on BVHs of massive models. Our RACBVH representation is compact and transparently supports random access on the compressed BVHs without decompressing the whole BVH. To support random access on our compressed BVHs, we decompose a BVH into a set of clusters. Each cluster contains consecutive bounding volume (BV) nodes in the original layout of the BVH. Also, each cluster is compressed separately from other clusters and serves as an access point to the RACBVH representation. We provide the general BVH access API to transparently access our RACBVH representation. At runtime, our decompression framework is guaranteed to provide correct BV nodes without decompressing the whole BVH. Also, our method is extended to support parallel random access that can utilize the multicore CPU architecture. Our method can achieve up to a 12:1 compression ratio, and more importantly, can decompress 4.2 M BV nodes ({=}135 {\rm MB}) per second by using a single CPU-core. To highlight the benefits of our approach, we apply our method to two different applications: ray tracing and collision detection. We can improve the runtime performance by more than a factor of 4 as compared to using the uncompressed original data. This improvement is a result of the fast decompression performance and reduced data access time by selectively fetching and decompressing small regions of the compressed BVHs requested by applications.

Abstract We present a novel, hybrid parallel continuous collision detection (HPCCD) method that exploits the availability of multi‐core CPU and GPU architectures. HPCCD is based on a bounding volume hierarchy (BVH) and selectively performs lazy reconstructions. Our method works with a wide variety of deforming models and supports self‐collision detection. HPCCD takes advantage of hybrid multi‐core architectures – using the general‐purpose CPUs to perform the BVH traversal and culling while GPUs are used to perform elementary tests that reduce to solving cubic equations. We propose a novel task decomposition method that leads to a lock‐free parallel algorithm in the main loop of our BVH‐based collision detection to create a highly scalable algorithm. By exploiting the availability of hybrid, multi‐core CPU and GPU architectures, our proposed method achieves more than an order of magnitude improvement in performance using four CPU‐cores and two GPUs, compared to using a single CPU‐core. This improvement results in an interactive performance, up to 148 fps, for various deforming benchmarks consisting of tens or hundreds of thousand triangles.