Tree rotations for improving bounding volume hierarchies

Kensler, Andrew

doi:10.1109/rt.2008.4634624

Cited by 26 publications

(12 citation statements)

References 13 publications

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“…In particular, we do not use primary or shadow rays because they are too view-dependent. We use 22 test scenes and 9 tree building algorithms: fast spatial mean-based LBVH [Lauterbach et al 2009;Karras 2012], two greedy sweep-based top-down algorithms (BBVH) [MacDonald and Booth 1990] and SBVH [Stich et al 2009], one bottom-up algorithm (Agglo) [Walter et al 2008], postprocess improvement using iterative reinsertion (Bittner) [Bittner et al 2013], two methods based on local tree rotations: hill climbing and simulated annealing [Kensler 2008], and treelet restructuring [Karras and Aila 2013] with and without triangle splitting (STreelet and Treelet, respectively). The last five are initialized using LBVH, and all algorithms use our own implementations relying on the default parameters recommended by the authors.…”

Section: Test Setupmentioning

confidence: 99%

On quality metrics of bounding volume hierarchies

Aila¹,

Karras²,

Laine³

2013

Proceedings of the 5th High-Performance Graphics Conference

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The surface area heuristic (SAH) is widely used as a predictor for ray tracing performance, and as a heuristic to guide the construction of spatial acceleration structures. We investigate how well SAH actually predicts ray tracing performance of a bounding volume hierarchy (BVH), observe that this relationship is far from perfect, and then propose two new metrics that together with SAH almost completely explain the measured performance. Our observations shed light on the increasingly common situation that a supposedly good tree construction algorithm produces trees that are slower to trace than expected. We also note that the trees constructed using greedy top-down algorithms are consistently faster to trace than SAH indicates and are also more SIMD-friendly than competing approaches.

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Section: Test Setupmentioning

confidence: 99%

On quality metrics of bounding volume hierarchies

Aila¹,

Karras²,

Laine³

2013

Proceedings of the 5th High-Performance Graphics Conference

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“…Gu et al [15] proposed a parallel approximative agglomerative clustering for accelerating the bottom-up BVH construction. Kensler [24], Bittner et al [5], and Karras and Aila [22] proposed optimizing the BVH by performing topological modifications of the existing hierarchy. Recently, Ganestam et al [12] introduced the Bonsai method performing a two-level SAH-based BVH build on a multi-core CPU.…”

Section: Related Workmentioning

confidence: 99%

Parallel BVH construction using k-means clustering

Meister

Bittner

2016

Vis Comput

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We propose a novel method for fast parallel construction of bounding volume hierarchies (BVH) on the GPU. Our method is based on a combination of divisible and agglomerative clustering. We use the k-means algorithm to subdivide scene primitives into clusters. From these clusters, we construct treelets using the agglomerative clustering algorithm. Applying this procedure recursively, we construct the entire bounding volume hierarchy. We implemented the method using parallel programming concepts on the GPU. The results show the versatility of the method: it can be used to construct medium-quality hierarchies very quickly, but also it can be used to construct high-quality hierarchies given a slightly longer computational time. We evaluate the method in the context of GPU ray tracing and show that it provides results comparable with other state-of-the-art GPU techniques for BVH construction. We also believe that our approach based on the k-means algorithm gives a new insight into how bounding volume hierarchies can be constructed.

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“…Gu et al [21] propose a parallel approximative agglomerative clustering for accelerating the bottom BVH construction. Kensler [22], Bittner et al [9], and Karras and Aila [23] propose to optimize the BVH by performing topological modifications of the existing tree. These approaches allow to decrease the expected cost of a BVH beyond the cost achieved by the traditional top down approach.…”

Section: Related Workmentioning

confidence: 99%