A visual delivery method for underground space digital models integrating 4D Gaussian splatting and augmented reality

doi:10.11996/JG.j.2095-302X.2026040895

Abstract

Abstract:

As underground space engineering expands in scale and complexity, conventional static and discrete model delivery methods based on BIM, point clouds, or meshes suffer from slow update cycles, excessive data volumes, and visual distortions. Consequently, they are inadequate for the growing demands for dynamic visualization and precise control throughout the construction process. To address this industry challenge, a digital twin model visualization delivery framework integrating 4D Gaussian Splatting (4DGS) with Augmented Reality (AR) technology was proposed, aiming to achieve high-fidelity dynamic reconstruction and immersive delivery of underground construction models. This approach established a comprehensive technical framework encompassing temporal data acquisition, 4DGS dynamic modeling, and AR-based interactive visualization. First, by leveraging multi-view temporal imagery and 3D Gaussian Splatting (3DGS) technology, temporal attributes were integrated into the modeling of key construction nodes to construct a 4D Gaussian splatting representation of the on-site construction progress. Second, the 3DGS models were subjected to lightweight compression for deployment on mobile AR terminals. This supported virtual-real registration and overlay, temporal playback, multi-axial sectioning, and interactive real-time analysis of the digital twin across various construction phases. Experimental validation across multiple typical underground engineering scenarios demonstrated that the compressed 3DGS models achieved substantial reductions in storage footprint compared to traditional point cloud and mesh models, while significantly improving visual fidelity metrics, including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). Furthermore, this method enabled high-fidelity, dynamic, and clear reconstruction of complex construction processes on AR devices, effectively mitigating the visual distortion issues inherent in traditional models. By integrating 4DGS and AR, the conventional static and discrete model delivery paradigm was transformed into a dynamic, continuous, and interactive immersive experience, establishing a closed-loop system encompassing perception, visualization, interaction, and feedback. This provided a robust digital foundation for construction technical briefing, process control, and full-lifecycle operation and maintenance management of underground space engineering projects.

Key words: 4D Gaussian splatting, augmented reality, underground space construction, digital twin, construction progress visualization

CLC Number:

TP391
TU91

YANG Jihao, CHEN Penglu, LU Baihua, ZHAO Yu, ZHAO Chunjie, ZHANG Jian, ZHOU Yihui, CHEN Lei, CHEN Xiangsheng, TAN Yi. A visual delivery method for underground space digital models integrating 4D Gaussian splatting and augmented reality[J]. Journal of Graphics, 2026, 47(4): 895-903.

Figures/Tables 14

References 21

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场景	阶段	分辨率	视频长度/s	图片数量
场景1	阶段1	3 480×2 160	76	467
	阶段2	1 920×1 080	96	582
	阶段3	1 920×1 080	112	673
场景2	阶段1	3 840×2 160	327	985
场景2	阶段2	3 840×2 160	414	1 243

场景	阶段	分辨率	视频长度/s	图片数量
场景1	阶段1	3 480×2 160	76	467
	阶段2	1 920×1 080	96	582
	阶段3	1 920×1 080	112	673
场景2	阶段1	3 840×2 160	327	985
场景2	阶段2	3 840×2 160	414	1 243

场景	阶段	3DGS/MB	点云/G	网格/MB
场景1	阶段1	39.5	1.55	815
	阶段2	16.5	2.73	810
	阶段3	23.4	3.80	1 966
场景2	阶段1	23.9	1.97	878
场景2	阶段2	18.4	3.24	336

场景	阶段	3DGS/MB	点云/G	网格/MB
场景1	阶段1	39.5	1.55	815
	阶段2	16.5	2.73	810
	阶段3	23.4	3.80	1 966
场景2	阶段1	23.9	1.97	878
场景2	阶段2	18.4	3.24	336

场景	阶段	评价指标	3DGS	点云	网格
场景1	阶段1	PSNR	17.500 5	9.336 6	12.912 4
		SSIM	0.672 0	0.414 4	0.510 3
		LPIPS	0.410 6	1.054 7	0.677 3
	阶段2	PSNR	14.179 8	10.671 1	11.936 3
		SSIM	0.376 3	0.264 8	0.294 8
		LPIPS	0.376 7	0.940 4	0.620 2
	阶段3	PSNR	18.086 0	14.740 4	14.424 7
		SSIM	0.499 5	0.396 3	0.432 4
		LPIPS	0.306 3	0.948 4	0.584 3
场景2	阶段1	PSNR	20.267 1	12.596 5	17.139 5
		SSIM	0.884 4	0.409 0	0.750 9
		LPIPS	0.359 2	0.790 4	0.509 9
	阶段2	PSNR	19.086 3	12.619 2	17.311 5
		SSIM	0.876 7	0.440 9	0.748 0
		LPIPS	0.410 4	0.743 7	0.549 1