An Improved View Frustum Culling Method Using Octrees for 3d Real-Time Rendering

Carvalho, Paulo Roberto de; Santos, Maikon Cismoski dos; Schwartz, William Robson; Pedrini, Hélio

doi:10.1142/s0219467813500095

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…The approach is applied to computation of sound propagation, improving the performance of edge diffraction algorithms from a factor of 2 to 4. Carvalho et al [28] proposed an improvement to the visualization frustum culling method by using an octree based space partitioning method. This method is in some way similar to ours.…”

Section: Recent Approaches For Visibilitymentioning

confidence: 99%

An Adaptive and Hybrid Approach for Revisiting the Visibility Pipeline

Cunha

Gonçalves

2016

CLEIej

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We revisit the visibility problem, which is traditionally known in Computer Graphics and Vision fields as the process of computing a (potentially) visible set of primitives in the computational model of a scene. We propose a hybrid solution that uses a dry structure (in the sense of data reduction), a triangulation of the type Ja 1 , to accelerate the task of searching for visible primitives. We came up with a solution that is useful for real-time, on-line, interactive applications as 3D visualization. In such applications the main goal is to load the minimum amount of primitives from the scene during the rendering stage, as possible. For this purpose, our algorithm executes the culling by using a hybrid paradigm based on viewing-frustum, back-face culling and occlusion models. Results have shown substantial improvement over these traditional approaches if applied separately. This novel approach can be used in devices with no dedicated processors or with low processing power, as cell phones or embedded displays, or to visualize data through the Internet, as in virtual museums applications.

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Section: Recent Approaches For Visibilitymentioning

confidence: 99%

An Adaptive and Hybrid Approach for Revisiting the Visibility Pipeline

Cunha

Gonçalves

2016

CLEIej

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“…Moreover, the deep learning model [21]- [26] is also used to predict the trajectory of viewpoints, which improves the accuracy of prediction. The literature [27]- [30] introduces the visual cone clipping algorithm in the real-time rendering of large-scale terrain, which performs the necessary clipping of data objects according to the change in the viewpoint range to load data objects quickly and accurately. In addition, level-of-detail (LOD) technology [31]- [34] reduces the detailed information of data according to viewpoint position and object distance, thus improving the rendering efficiency.…”

Section: Introductionmentioning

confidence: 99%

Fast 3D Visualization of Massive Geological Data Based on Clustering Index Fusion

et al. 2022

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With the development of 3D visualization technology, the amount of geological data information is increasing, and the interactive display of big data faces severe challenges. Because traditional volume rendering methods cannot entirely load large-scale data into the memory owing to hardware limitations, a visualization method based on variational deep embedding clustering fusion Hilbert R-tree is proposed to solve slow display and stuttering issues when rendering massive geological data. By constructing an efficient data index structure, deep clustering algorithms and space-filling curves can be integrated into the data structure to improve the indexing efficiency. In addition, this method combines time forecasting, data scheduling, and loading modules to improve the accuracy and real-time data display rate, thereby improving the stability of 3D visualization of large-scale geological data. This method uses real geological data as the experimental dataset, comparing and analyzing the existing index structure and time-series prediction method. The experimental results indicate that when comparing the index of the variational deep embedded clustering-Hilbert R-tree (VDEC −HRT ) with that of the K-means Hilbert R-tree (KHRT ), the time required is reduced by 55.67%, the viewpoint prediction correctness of the proposed method is improved by 22.7% compared with Lagrange interpolation algorithm. And the overall rendering performance and quality of the system achieve the expected results. Ours experiments prove the feasibility and effectiveness of the proposed scheme in the visualization of large-scale geological data.INDEX TERMS 3D visualization of massive data, deep learning, Hilbert R-tree, deep clustering, time-series forecasting, view frustum culling.

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