Spatial splits in bounding volume hierarchies

Stich, Martin; Friedrich, Heiko; Dietrich, Andreas

doi:10.1145/1572769.1572771

Cited by 105 publications

(93 citation statements)

References 20 publications

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“…Ernst and Greiner [EG07] suggest to precompute several bounding boxes for each primitive and use them independently during top down construction. More recently, to achieve higher performance Stich et al [SFD09] and Popov et al [PGDS09] suggest to use spatial splits during the BVH construction, hence reintroducing some properties of kd-trees to BVHs.…”

Section: Related Workmentioning

confidence: 99%

Fast Insertion‐Based Optimization of Bounding Volume Hierarchies

Bittner

Hapala

Havran

2013

Computer Graphics Forum

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We present an algorithm for fast optimization of bounding volume hierarchies (BVH) for efficient ray tracing. We perform selective updates of the hierarchy driven by the cost model derived from the surface area heuristic. In each step the algorithm updates a fraction of the hierarchy nodes in order to minimize the overall hierarchy cost. The updates are realized by simple operations on the tree nodes: removal, search, and insertion. Our method can quickly reduce the cost of the hierarchy constructed by the traditional techniques such as the surface area heuristic. We evaluate the properties of the proposed method on fourteen test scenes of different complexity including individual objects and architectural scenes. The results show that our method can improve a BVH initially constructed with the surface area heuristic by up to 27% and a BVH constructed with the spatial median split by up to 88%.

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Section: Related Workmentioning

confidence: 99%

Fast Insertion‐Based Optimization of Bounding Volume Hierarchies

Bittner

Hapala

Havran

2013

Computer Graphics Forum

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“…For meshes, at the higher level, we use the Hierarchical Linear Bounding Volume Hierarchy (HLBVH) [Pantaleoni and Luebke 2010]. At the lower level, we use the Split Bounding Volume Hierarchy (SBVH) [Stich et al 2009]. The SBVH is able to provide tighter bounding volumes and is more efficient in intersection tests.…”

Section: Constructing Hierarchiesmentioning

confidence: 99%

GPU-based out-of-core many-lights rendering

et al. 2013

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Figure 1: Example scenes rendered using our approach on an NVIDIA GTX 680 GPU with 2GB of memory. The left image is a museum scene, which consists of 117.1 million triangles and 32.4 million lights. The total storage sizes of geometry and lights are 14.3 GB and 3.75 GB respectively. The middle image shows an airport scene with two Boeing 777 models that has total 669.3 million (46.3 GB) triangles and 18 million (2.1 GB) lights. The right image is a carnival scene. There are 17.1 million (1.76 GB) triangles and 256 (29.6 GB) million lights. Our method takes 3m55s, 10m15s and 1m22s to shade the museum, the airport and the carnival scenes respectively, and requires an additional 1m20s, 7m25s and 1m14s to build acceleration structures on these lights and geometry. AbstractIn this paper, we present a GPU-based out-of-core rendering approach under the many-lights rendering framework. Many-lights rendering is an efficient and scalable rendering framework for a large number of lights. But when the data sizes of lights and geometry are both beyond the in-core memory storage size, the data management of these two out-of-core data becomes critical. In our approach, we formulate such a data management as a graph traversal optimization problem that first builds out-of-core lights and geometry data into a graph, and then guides shading computations by finding a shortest path to visit all vertices in the graph. Based on the proposed data management, we develop a GPU-based out-of-GPU-core rendering algorithm that manages data between the CPU host memory and the GPU device memory. Two main steps are taken in the algorithm: the out-of-core data preparation to pack data into optimal data layouts for the many-lights rendering, and the outof-core shading using graph-based data management. We demonstrate our algorithm on scenes with out-of-core detailed geometry and out-of-core lights. Results show that our approach generates complex global illumination effects with increased data access coherence and has one order of magnitude performance gain over the CPU-based approach.

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“…Methods proposed by Dammertz and Keller [7] and Ernst and Greiner [8] recommend splitting triangles to get a better quality data structure. Stich et al [9] and Popov et al [10] proposed variations to BVHs to improve their quality. The construction of Spatial BVH (SBVH) proposed in [9] is considerably higher as an efficient building method is mapped onto GPU is still an issue.…”

Section: Background and Previous Workmentioning

confidence: 99%

“…Stich et al [9] and Popov et al [10] proposed variations to BVHs to improve their quality. The construction of Spatial BVH (SBVH) proposed in [9] is considerably higher as an efficient building method is mapped onto GPU is still an issue. Such a data structure is really good for static scenes but not for scenes with dynamically changing geometry.…”

Section: Background and Previous Workmentioning

confidence: 99%

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Raytracing Dynamic Scenes on the GPU Using Grids

Guntury

Narayanan

2012

IEEE Trans. Visual. Comput. Graphics

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Abstract-Raytracing dynamic scenes at interactive rates have received a lot of attention recently. We present a few strategies for high performance raytracing on a commodity GPU. The construction of grids needs sorting, which is fast on today's GPUs. The grid is thus the acceleration structure of choice for dynamic scenes as per-frame rebuilding is required. We advocate the use of appropriate data structures for each stage of raytracing, resulting in multiple structure building per frame. A perspective grid built for the camera achieves perfect coherence for primary rays. A perspective grid built with respect to each light source provides the best performance for shadow rays. Spherical grids handle lights positioned inside the model space and handle spotlights. Uniform grids are best for reflection and refraction rays with little coherence. We propose an Enforced Coherence method to bring coherence to them by rearranging the ray to voxel mapping using sorting. This gives the best performance on GPUs with only user-managed caches. We also propose a simple, Independent Voxel Walk method, which performs best by taking advantage of the L1 and L2 caches on recent GPUs. We achieve over 10 fps of total rendering on the Conference model with one light source and one reflection bounce, while rebuilding the data structure for each stage. Ideas presented here are likely to give high performance on the future GPUs as well as other manycore architectures.

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Spatial splits in bounding volume hierarchies

Cited by 105 publications

References 20 publications

Fast Insertion‐Based Optimization of Bounding Volume Hierarchies

Fast Insertion‐Based Optimization of Bounding Volume Hierarchies

GPU-based out-of-core many-lights rendering

Raytracing Dynamic Scenes on the GPU Using Grids

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