Shell: A Spatial Decomposition Data Structure for Ray Traversal on GPU

Xiao, Kai; Hu, Xiaobo Sharon; Zhou, Bo; Chen, Danny Ziyi

doi:10.1109/tc.2015.2409855

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…If the distance between two particles p i and p j satisfies

(‖‖, x_{i} - x_{j}) \leq \max false(r_{i}, r_{j} false)

, then p i and p j are neighbors. A k‐dimensional (KD) tree is built to store the particle set P , such that the neighbor particles can be queried efficiently …”

Section: Methodsmentioning

confidence: 99%

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Visualization of fluid simulation: An SPH‐based multi‐resolution method

Zhang

Liu

2018

Concurrency and Computation

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Summary Fluid simulation is an important research topic since it is widely used in virtual training, education, computer games, and digital entertainment. Generating fluid simulation results with high degree of visual realism is challenging in these applications. We present a visualization approach for fluid simulation in this paper. Our method allows students to understand the complex natural phenomena so as to solve the fluid engineering problems intuitively. In this study, an SPH (Smoothed Particle Hydrodynamics)‐based multi‐resolution method is proposed for fluid simulation. The method can keep the accuracy while improve the efficiency significantly. We construct a multi‐resolution fluid model from bottom to top by combining fluid computation and surface reconstruction. We first compute multi‐resolution particles with a novel adaptive SPH model in the bottom layer. Next, surface particles are extracted and pre‐processed to provide samples for surface extraction. Then, the multi‐resolution surface reconstruction model is built in the top layer. Meshes from different reconstruction resolutions are merged to obtain the final fluid surface. Experimental results show that our approach can effectively represent multi‐scale fluid details and efficiently produce visual‐pleasing fluid simulation results.

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“…If the distance between two particles p i and p j satisfies

(‖‖, x_{i} - x_{j}) \leq \max false(r_{i}, r_{j} false)

, then p i and p j are neighbors. A k‐dimensional (KD) tree is built to store the particle set P , such that the neighbor particles can be queried efficiently …”

Section: Methodsmentioning

confidence: 99%

“…A k-dimensional (KD) tree is built to store the particle set P, such that the neighbor particles can be queried efficiently. 35…”

Section: Local Adaptive Multi-resolution Fluid Computation Modelmentioning

confidence: 99%

Visualization of fluid simulation: An SPH‐based multi‐resolution method

Zhang

Liu

2018

Concurrency and Computation

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show abstract

“…A spatial join query undertaking is isolated into a few sub-spatial join inquiries, which can keep running in parallel to enhance the query execution. A state-of-the-art method for computing approximate nearest neighbor (ANN) fields is the Propagation-assisted K-d tree [23]. In this method, each query patch needs descending search in the K-d tree and propagation search in the nearby patches.…”

Section: B K-d Treementioning

confidence: 99%

CONNEKT: Co-Located Nearest Neighbor Search using KNN Querying with K-D Tree

Sharmiladevi¹,

Sathya²,

Kumar³

2019

IJRTE

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Data about entities or objects associated with geographical or location information could be called as spatial data. Spatial data helps in identifying and positioning anyone or anything globally anywhere across the world. K-d tree) inorder to make the querying process efficient. The algorithm is analyzed using a hypothetical data set generated through QGIS. Instances of various spatial features that are closely found together are called as spatial co-located patterns. So far, the spatial co-located patterns have been used only for knowledge discovery process but it would serve a wide variety of applications if analyzed intensively. One such application is to use co-location pattern mining for a context aware based search. Hence the main aim of this work is to extend the K-Nearest Neighbor (KNN) querying to co-located instances for context aware based querying or location-based services (LBS). For the above-said purpose, co-located nearest neighbor search algorithm namely "CONNEKT" is proposed. The co-located instances are mapped onto a K-dimensional tree (

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Tree structured data processing on GPUs

Yang

Bhavsar

et al. 2017

2017 7th International Conference on Cloud Computing, Data Science &Amp; Engineering - Confluence

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Shell: A Spatial Decomposition Data Structure for Ray Traversal on GPU

Cited by 4 publications

References 28 publications

Visualization of fluid simulation: An SPH‐based multi‐resolution method

Visualization of fluid simulation: An SPH‐based multi‐resolution method

CONNEKT: Co-Located Nearest Neighbor Search using KNN Querying with K-D Tree

Tree structured data processing on GPUs

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