Web3D global illumination cloud rendering based on advanced DDGI

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

Abstract

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

CLC Number:

TP391

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.

Figures/Tables 15

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

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

Fig. 3 The effect of irradiance mapping on the scene

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

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)

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

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

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

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

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

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

Fig. 9 Render efficiency comparison result

Fig. 10 Render quality comparison result

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

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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