We present a novel method for tuning geometric acoustic simulations based on ray tracing. Our formulation computes sound propagation paths from source to receiver and exploits the independence of visibility tests and validation tests to dynamically guide the simulation to high accuracy and performance. Our method makes no assumptions of scene layout and can account for moving sources, receivers, and geometry. We combine our guidance algorithm with a fast GPU sound propagation system for interactive simulation. Our implementation efficiently computes early specular paths and first order diffraction with a multiview tracing algorithm. We couple our propagation simulation with an audio output system supporting a high order interpolation scheme that accounts for attenuation, cross fading, and delay. The resulting system can render acoustic spaces composed of thousands of triangles interactively.

Recent GPU algorithms for constructing spatial hierarchies have achieved promising performance for moderately complex models by using the breadth-first search (BFS) construction order. While being able to exploit the massive parallelism on the GPU, the BFS order also consumes excessive GPU memory, which becomes a serious issue for interactive applications involving very complex models with more than a few million triangles. In this paper, we propose to use the partial breadth-first search (PBFS) construction order to control memory consumption while maximizing performance. We apply the PBFS order to two hierarchy construction algorithms. The first algorithm is for kd-trees that automatically balances between the level of parallelism and intermediate memory usage. With PBFS, peak memory consumption during construction can be efficiently controlled without costly CPU-GPU data transfer. We also develop memory allocation strategies to effectively limit memory fragmentation. The resulting algorithm scales well with GPU memory and constructs kd-trees of models with millions of triangles at interactive rates on GPUs with 1 GB memory. Compared with existing algorithms, our algorithm is an order of magnitude more scalable for a given GPU memory bound. The second algorithm is for out-of-core bounding volume hierarchy (BVH) construction for very large scenes based on the PBFS construction order. At each iteration, all constructed nodes are dumped to the CPU memory, and the GPU memory is freed for the next iteration's use. In this way, the algorithm is able to build trees that are too large to be stored in the GPU memory. Experiments show that our algorithm can construct BVHs for scenes with up to 20 M triangles, several times larger than previous GPU algorithms.

We present novel randomized algorithms for solving global motion planning problems that exploit the computational capabilities of many-core GPUs. Our approach uses thread and data parallelism to achieve high performance for all components of sample-based algorithms, including random sampling, nearest neighbor computation, local planning, collision queries and graph search. The approach can efficiently solve both the multi-query and single-query versions of the problem and obtain considerable speedups over prior CPU-based algorithms. We demonstrate the efficiency of our algorithms by applying them to a number of 6DOF planning benchmarks in 3D environments. Overall, this is the first algorithm that can perform real-time motion planning and global navigation using commodity hardware.

We present a novel k-nearest neighbor search algorithm (KNNS) for proximity computation in motion planning algorithm that exploits the computational capabilities of many-core GPUs. Our approach uses locality sensitive hashing and cuckoo hashing to construct an efficient KNNS algorithm that has linear space and time complexity and exploits the multiple cores and data parallelism effectively. In practice, we see magnitude improvement in speed and scalability over prior GPU-based KNNS algorithm. On some benchmarks, our KNNS algorithm improves the performance of overall planner by 20-40 times for CPU-based planner and up to 2 times for GPU-based planner.

Abstract We present novel parallel algorithms for collision detection and separation distance computation for rigid and deformable models that exploit the computational capabilities of many‐core GPUs. Our approach uses thread and data parallelism to perform fast hierarchy construction, updating, and traversal using tight‐fitting bounding volumes such as oriented bounding boxes (OBB) and rectangular swept spheres (RSS). We also describe efficient algorithms to compute a linear bounding volume hierarchy (LBVH) and update them using refitting methods. Moreover, we show that tight‐fitting bounding volume hierarchies offer improved performance on GPU‐like throughput architectures. We use our algorithms to perform discrete and continuous collision detection including self‐collisions, as well as separation distance computation between non‐overlapping models. In practice, our approach (gProximity) can perform these queries in a few milliseconds on a PC with NVIDIA GTX 285 card on models composed of tens or hundreds of thousands of triangles used in cloth simulation, surgical simulation, virtual prototyping and N‐body simulation. Moreover, we observe more than an order of magnitude performance improvement over prior GPU‐based algorithms.

Abstract We present two novel parallel algorithms for rapidly constructing bounding volume hierarchies on manycore GPUs. The first uses a linear ordering derived from spatial Morton codes to build hierarchies extremely quickly and with high parallel scalability. The second is a top‐down approach that uses the surface area heuristic (SAH) to build hierarchies optimized for fast ray tracing. Both algorithms are combined into a hybrid algorithm that removes existing bottlenecks in the algorithm for GPU construction performance and scalability leading to significantly decreased build time. The resulting hierarchies are close in to optimized SAH hierarchies, but the construction process is substantially faster, leading to a significant net benefit when both construction and traversal cost are accounted for. Our preliminary results show that current GPU architectures can compete with CPU implementations of hierarchy construction running on multicore systems. In practice, we can construct hierarchies of models with up to several million triangles and use them for fast ray tracing or other applications.

Abstract We present a novel representation and algorithm, ReduceM, for memory efficient ray tracing of large scenes. ReduceM exploits the connectivity between triangles in a mesh and decomposes the model into triangle strips. We also describe a new stripification algorithm, Strip‐RT, that can generate long strips with high spatial coherence. Our approach uses a two‐level traversal algorithm for ray‐primitive intersection. In practice, ReduceM can significantly reduce the storage overhead and ray trace massive models with hundreds of millions of triangles at interactive rates on desktop PCs with 4‐8GB of main memory.

We present an interactive algorithm to compute sound propagation paths for transmission, specular reflection and edge diffraction in complex scenes. Our formulation uses an adaptive frustum representation that is automatically sub-divided to accurately compute intersections with the scene primitives. We describe a simple and fast algorithm to approximate the visible surface for each frustum and generate new frusta based on specular reflection and edge diffraction. Our approach is applicable to all triangulated models and we demonstrate its performance on architectural and outdoor models with tens or hundreds of thousands of triangles and moving objects. In practice, our algorithm can perform geometric sound propagation in complex scenes at 4-20 frames per second on a multi-core PC.

We present a new approach for simulating real-time sound propagation in complex, virtual scenes with dynamic sources and objects. Our approach combines the efficiency of interactive ray tracing with the accuracy of tracing a volumetric representation. We use a four-sided convex frustum and perform clipping and intersection tests using ray packet tracing. A simple and efficient formulation is used to compute secondary frusta and perform hierarchical traversal. We demonstrate the performance of our algorithm in an interactive system for complex environments and architectural models with tens or hundreds of thousands of triangles. Our algorithm can perform real-time simulation and rendering on a high-end PC.