基于改进式DDGI的Web3D场景云渲染

doi:10.11996/JG.j.2095-302X.2024061364

图学学报 ›› 2024, Vol. 45 ›› Issue (6): 1364-1374.DOI: 10.11996/JG.j.2095-302X.2024061364

• 计算机图形学与虚拟现实 • 上一篇下一篇

基于改进式DDGI的Web3D场景云渲染

刘畅¹(), 张宇明¹, 张乾², 欧巧凤¹, 赵同硕¹, 陈昊¹(), 石磊³

1.南昌航空大学信息工程学院，江西南昌 330063
2.同济大学软件学院，上海 200082
3.江西科技师范大学音乐学院，江西南昌 330038

收稿日期:2024-07-04 接受日期:2024-08-25 出版日期:2024-12-31 发布日期:2024-12-24
通讯作者:陈昊(1982-)，男，教授，博士。主要研究方向为人工智能、模式识别等。E-mail：chenhaoshl@nchu.edu.cn
第一作者:刘畅(1983-)，男，讲师，博士。主要研究方向为虚拟现实和计算机图形学。E-mail：70202@nchu.edu.cn
基金资助:
国家重点研发计划项目(2023YFC3604100);国家自然科学基金(62262043);江西省自然科学基金(20242BAB25074)

Web3D global illumination cloud rendering based on advanced DDGI

LIU Chang¹(), ZHANG Yuming¹, ZHANG Qian², OU Qiaofeng¹, ZHAO Tongshuo¹, CHEN Hao¹(), SHI Lei³

1. School of Information Engineering, Nanchang Hangkong University, Nanchang Jiangxi 330063, China
2. School of Software Engineering, Tongji University, Shanghai 200082, China
3. School of Music, Jiangxi Science and Technology Normal University, Nanchang Jiangxi 330038, China

Received:2024-07-04 Accepted:2024-08-25 Published:2024-12-31 Online:2024-12-24
Contact: CHEN Hao (1982-), professor, Ph.D. His main research interests cover artificial intelligence, pattern recognition, etc. E-mail：chenhaoshl@nchu.edu.cn
First author：LIU Chang (1983-), lecturer, Ph.D. His main research interests cover virtual reality and computer graphics. E-mail：70202@nchu.edu.cn
Supported by:
National Key Research and Development Program of China Grant(2023YFC3604100);National Natural Science Foundation of China(62262043);Jiangxi Provincial Natural Science Foundation of China(20242BAB25074)

摘要/Abstract

摘要：

针对Web3D为应用在各类设备上的兼容性而导致的渲染性能不足，无法进行实时全局光照渲染等问题，提出一种页云绘制策略，利用布局优化算法对动态漫反射全局光照(DDGI)技术进行改进，显著增强Web3D环境中的全局光照渲染效率与质量。首先，通过在云服务器上进行细分检测以及布局优化策略自动优化DDGI的布局以适应场景需求；其次，利用页云绘制策略将全局光照计算任务根据设备计算资源进行分摊；最后，将低数据量的全局光照信息传输至Web客户端，在允许用户通过Web界面进行互动，实时调整如视点、模型、光源等场景资源的情况下，在Web客户端实现实时绘制高质量的动态全局光照效果。研究成果证明，该方法提升渲染质量效果显著，为Web3D技术的发展提供了一种有效的渲染优化方案。

关键词: 云渲染, 全局光照, Web3D, DDGI, 实时渲染

Abstract:

A Web-Cloud rendering strategy was proposed to address the issues of insufficient rendering performance and the inability to perform real-time global illumination rendering caused by the compatibility of Web3D applications on various devices. This strategy aimed to improve the dynamic diffuse global illumination (DDGI) technology through layout optimization algorithms, significantly enhancing the efficiency and quality of global illumination rendering in the Web3D environment. Firstly, the DDGI layout was automatically optimized through segmentation detection and layout optimization strategies on the cloud server to meet the requirements of the scenario. Secondly, the page cloud rendering strategy allocated global illumination computing tasks based on the device computing resources. Finally, the low-data global lighting information was transmitted to the web client, allowing users to interact through the web interface and make real-time adjustments to scene resources such as viewpoints, models, and lighting. This approach enabled the real-time rendering of high-quality dynamic global lighting effects on the web client. The research results demonstrated that the improved DDGI-based Web3D scene cloud rendering significantly enhanced rendering quality, providing an effective rendering optimization solution for the advancement of Web3D technology.

Key words: cloud rendering, global illumination, Web3D, DDGI, real-time rendering

中图分类号:

TP391

刘畅, 张宇明, 张乾, 欧巧凤, 赵同硕, 陈昊, 石磊. 基于改进式DDGI的Web3D场景云渲染[J]. 图学学报, 2024, 45(6): 1364-1374.

LIU Chang, ZHANG Yuming, ZHANG Qian, OU Qiaofeng, ZHAO Tongshuo, CHEN Hao, SHI Lei. Web3D global illumination cloud rendering based on advanced DDGI[J]. Journal of Graphics, 2024, 45(6): 1364-1374.

图/表 15

图1 页端渲染结果图((a)直接光照；(b) DDGI；(c)改进式DDGI)

Fig. 1 Web rendering result ((a) Direct illumination; (b) DDGI; (c) Advanced DDGI)

图2 动态漫反射全局光照的页云绘制架构

Fig. 2 Dynamic diffuse global illumination for Web-Cloud rendering architecture

图3 辐照度贴图对场景的影响

Fig. 3 The effect of irradiance mapping on the scene

图4 布局优化方法基本流程((a)原场景；(b)简化场景；(c)分类结果；(d)布局优化结果)

Fig. 4 Layout optimization method basic flow ((a) The original scene; (b) Simplify the scene; (c) Classification results; (d) Layout optimization results)

图5 布局优化前后对比图((a)布局优化前；(b)布局优化后；(c)优化前资源数；(d)优化后资源数)

Fig. 5 Layout optimization before and after comparison result ((a) Before layout optimization; (b) After layout optimization; (c) Number of resources before optimization; (d) Number of resources after optimization)

表1 场景数据

Table 1 Scene data

场景	三角面片数量/k	场景大小/M
Sponza	262.3	89.7
Kindergarten	115.2	72.3
San_miguel	5617.5	1863.7

图6 测试场景结果对比图((a) GT；(b) DDGI；(c)本文方法)

Fig. 6 Layout optimization before and after comparison result comparison of test scene results ((a) GT; (b) DDGI; (c) Ours)

图7 3种方法测试渲染效果图((a) GT；(b) DDGI；(c)细分未布局优化；(d)细分且布局优化)

Fig. 7 Three ways to test renderings ((a) GT; (b) DDGI; (c) Subdivision without layout optimization; (d) Subdivision and layout optimization)

表2 3种方法测试结果

Table 2 Test results of the three methods

方法	SSIM	帧率	数据量/K	资源数
DDGI	0.73	120	480	3
细分未布局优化	0.78	85	833	48
细分且布局优化	0.80	97	615	12

图8 不同布局下渲染效果图

Fig. 8 Renderings in different layouts ((a) GT; (b) DDGI(113); (c) Ours(113); (d) DDGI(223))

表3 不同布局下测试结果

Table 3 Test results under different layouts

布局	SSIM	帧率	数据量	资源数
DDGI	0.73	120	480 K	3
Ours(11³)	0.80	97	615 K	48
DDGI(22³)	0.80	70	3.3 M	3

图9 运行效率对比图

Fig. 9 Render efficiency comparison result

图10 渲染质量对比图

Fig. 10 Render quality comparison result

表4 传输数据量对比

Table 4 Transmission data volume comparison

场景	布局 (长×宽×高)		数据量 (Our system)	数据量 (Cloud baking)/M
Sponza	11³	DDGI	480 K	5.6
	11³	Ours	615 K
	22³	DDGI	3.3 M
Kindergarten	10³	DDGI	679 K	4.8
	10³	Ours	1 096 K
	20³	DDGI	4.0 M
San_miguel	11³	DDGI	807 K	7.4
	11³	Ours	1 249 K
	22³	DDGI	4.9 M

表5 系统延时表/ms

Table 5 System delay table/ms

场景	云端计算时间	Web端计算时间	传输时间
Kindergarten	7	6	159
Sponza	10	7	164
San_miguel	19	46	168

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基于改进式DDGI的Web3D场景云渲染

Web3D global illumination cloud rendering based on advanced DDGI

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