一种自适应剪枝的室内场景三维高斯SLAM实时高保真建模方法

doi:10.11947/j.AGCS.2026.20260050

测绘学报 ›› 2026, Vol. 55 ›› Issue (7): 1266-1277.doi: 10.11947/j.AGCS.2026.20260050

• 摄影测量学与遥感 • 上一篇

一种自适应剪枝的室内场景三维高斯SLAM实时高保真建模方法

龚俊¹(), 罗建军¹, 柯胜男¹, 黎施彬², 陈蔚聪², 秦乐¹, 汤圣君²()

^1.江西师范大学人工智能学院，江西　南昌　330022
^2.深圳大学建筑与城市规划学院，广东　深圳　518060

收稿日期:2026-02-02 修回日期:2026-06-23 发布日期:2026-08-18
通讯作者: 汤圣君 E-mail:gongjunbox@163.com;shengjuntang@szu.edu.cn
作者简介:龚俊（1978—），男，博士，教授，研究方向为三维城市建模。 E-mail：gongjunbox@163.com
基金资助:
国家自然科学基金(42471442; 62261029; 12563011);广东省自然科学基金项目(2024A1515030061);深圳市科技创新委员会项目(KJZD20230923115508017);亚热带建筑与城市科学全国重点实验室科研项目(2023ZB18);广东省科学技术协会青年科技人才培育计划项目(SKXRC2026858);中国国家铁路集团有限公司科技研究开发计划项目(N2024S008)

An adaptive-pruning 3D Gaussian SLAM method for real-time high-fidelity indoor scene modeling

Jun Gong¹(), Jianjun Luo¹, Shengnan Ke¹, Shibin Li², Weicong Chen², Le Qin¹, Shengjun Tang²()

^1.School of Artificial Intelligence, Jiangxi Normal University, Nanchang 330022, China
^2.School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China

Received:2026-02-02 Revised:2026-06-23 Published:2026-08-18
Contact: Shengjun Tang E-mail:gongjunbox@163.com;shengjuntang@szu.edu.cn
About author:Gong Jun (1978—), male, PhD, professor, majors in 3D city modeling. E-mail: gongjunbox@163.com
Supported by:
The National Natural Science Foundation of China(42471442; 62261029; 12563011);Research Project of Natural Science Foundation of Guangdong Province(2024A1515030061);Research Project of Shenzhen Science and Technology Innovation Committee(KJZD20230923115508017);Research Project of State Key Laboratory of Subtropical Building and Urban Science(2023ZB18);Youth Science and Technology Talent Support Programme of Guangdong Provincial Association for Science and Technology (GDSTA)(SKXRC2026858);Research and Development Project of China Railway(N2024S008)

摘要/Abstract

摘要：

近年来，机器人与增强现实等应用对稠密SLAM的实时性与可解释性提出了更高要求。三维高斯泼溅（3DGS）作为显式三维表示，兼具快速渲染与结构可解释性，正成为高质量建图的重要方向。然而，现有3DGS-SLAM在在线建图中难以同时兼顾重建精度与实时性，其关键瓶颈在于高斯基元数量缺乏自适应的规模控制。为此，本文提出一种自适应剪枝的3DGS-SLAM实时高保真建模方法，将“地图规模/高斯预算”显式纳入建图优化目标，并设计评分驱动的高斯管理机制：在优化过程中融合多视图光度一致性残差、遮挡感知与视角覆盖等信息，在线评估高斯点对几何一致性与外观保真度的贡献度，据此联合执行自适应致密化与优先级剪枝，以实现模型复杂度的动态调节。同时，引入可学习掩膜并构建稀疏化正则项，促使有效高斯集合自适应收缩，降低显存占用与渲染开销。在公开基准数据集和自制数据集的评测中，本文方法即使在最终高斯点数量减少65.0%的情况下，仍能保持稳定的重建质量，显著节省计算与存储资源。结果表明，本文方法能够在不牺牲地图可视化质量的前提下提升在线建图效率，为3DGS-SLAM的实时高保真建图提供了一种有效的规模自适应控制方案。

关键词: 3DGS, SLAM, 实时建图, 自适应剪枝, 可视化

Abstract:

In recent years, robotics and augmented reality (AR) applications have imposed increasingly stringent requirements on the real-time performance and interpretability of dense SLAM. As an explicit 3D scene representation, 3D Gaussian splatting (3DGS) combines fast rendering with structural interpretability and has emerged as a promising direction for high-quality mapping. However, existing 3DGS-SLAM systems still struggle to simultaneously achieve high reconstruction accuracy and real-time efficiency in online mapping, mainly due to the lack of adaptive control over the number of Gaussian primitives. To address this issue, we propose an adaptive-pruning 3D Gaussian SLAM method for real-time, high-fidelity modeling. The proposed approach explicitly incorporates a “map scale/Gaussian budget” term into the mapping optimization objective and introduces a score-driven Gaussian management mechanism. Specifically, during optimization, multi-view photometric consistency residuals, occlusion awareness, and viewpoint coverage are jointly leveraged to online estimate the contribution of each Gaussian to geometric consistency and appearance fidelity. Based on these scores, adaptive densification and priority-based pruning are performed in a unified manner to dynamically regulate model complexity. In addition, a learnable mask coupled with a sparsity regularization term is introduced to promote the adaptive contraction of the effective Gaussian set, thereby reducing GPU memory usage and rendering overhead. Experiments on public benchmark datasets demonstrate that the proposed method maintains stable reconstruction quality even at an approximately 65% pruning ratio, while substantially reducing computational and storage costs. These results indicate that the proposed approach improves online mapping efficiency without compromising map visualization quality, providing an effective scale-adaptive control scheme for real-time, high-fidelity 3DGS-SLAM mapping.

Key words: 3DGS, SLAM, real-time mapping, adaptive pruning, visualization

中图分类号:

P237

龚俊, 罗建军, 柯胜男, 黎施彬, 陈蔚聪, 秦乐, 汤圣君. 一种自适应剪枝的室内场景三维高斯SLAM实时高保真建模方法[J]. 测绘学报, 2026, 55(7): 1266-1277.

Jun Gong, Jianjun Luo, Shengnan Ke, Shibin Li, Weicong Chen, Le Qin, Shengjun Tang. An adaptive-pruning 3D Gaussian SLAM method for real-time high-fidelity indoor scene modeling[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(7): 1266-1277.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2026.20260050

http://xb.chinasmp.com/CN/Y2026/V55/I7/1266

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参考文献 30

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方法	fr1/desk	fr2/xyz	fr3/Office	Avg
SplaTAM	3.38	1.29	5.14	3.27
Photo-SLAM	1.52	0.34	1.23	1.03
MonoGS-SLAM	1.47	1.54	1.41	1.47
GS-ICP SLAM	2.54	1.78	3.32	2.55
本文方法	1.57	0.43	1.21	1.07

方法	Room0	Room1	Room2	Office0	Office1	Office2	Office3	Office4	Avg
SplaTAM	0.31	0.39	0.29	0.48	0.25	0.28	0.32	0.56	0.36
Photo-SLAM	0.59	0.51	0.32	0.45	0.46	0.48	0.61	0.87	0.54
MonoGS SLAM	0.48	0.34	0.29	0.46	0.49	0.22	0.46	2.43	0.64
GS-ICP SLAM	0.15	0.16	0.11	0.18	0.12	0.17	0.16	0.21	0.16
本文方法	0.14	0.16	0.11	0.18	0.13	0.17	0.16	0.21	0.16

方法	指标	fr1/desk	fr2/xyz	fr3/Office	Avg
SplaTAM	PSNR/dB↑	21.03	23.03	20.63	21.56
	SSIM↑	0.75	0.79	0.73	0.76
	LPIPS↓	0.23	0.20	0.21	0.21
Photo-SLAM	PSNR/dB↑	19.45	21.45	22.63	21.18
	SSIM↑	0.70	0.73	0.76	0.73
	LPIPS↓	0.25	0.16	0.15	0.19
MonoGS-SLAM	PSNR/dB↑	18.66	15.81	19.11	17.86
	SSIM↑	0.70	0.72	0.97	0.80
	LPIPS↓	0.33	0.25	0.32	0.30
GS-ICP SLAM（限制帧率25帧/s）	PSNR/dB↑	17.42	18.43	20.01	18.62
	SSIM↑	0.64	0.68	0.73	0.68
	LPIPS↓	0.37	0.30	0.27	0.31
本文方法	PSNR/dB↑	20.54	22.71	21.95	21.73
	SSIM↑	0.72	0.76	0.74	0.74
	LPIPS↓	0.27	0.19	0.18	0.21

方法	指标	Room0	Room1	Room2	Office0	Office1	Office2	Office3	Office4	Avg
SplaTAM	PSNR/dB↑	32.81	33.55	34.83	38.09	39.02	31.95	29.53	31.55	33.89
	SSIM↑	0.975	0.969	0.982	0.982	0.982	0.966	0.949	0.951	0.970
	LPIPS↓	0.070	0.097	0.074	0.088	0.093	0.098	0.119	0.150	0.099
Photo-SLAM	PSNR/dB↑	33.38	34.10	36.32	38.72	39.31	34.22	34.10	34.82	35.62
	SSIM↑	0.979	0.977	0.985	0.985	0.987	0.962	0.963	0.981	0.977
	LPIPS↓	0.097	0.115	0.101	0.089	0.110	0.152	0.119	0.131	0.114
MonoGS-SLAM	PSNR/dB↑	31.56	32.86	32.59	38.70	41.17	32.36	32.03	32.92	34.27
	SSIM↑	0.968	0.973	0.971	0.986	0.993	0.978	0.970	0.968	0.975
	LPIPS↓	0.094	0.075	0.093	0.050	0.033	0.094	0.110	0.112	0.082
GS-ICP SLAM	PSNR/dB↑	35.43	37.46	38.33	43.00	43.32	36.90	36.94	38.71	38.76
	SSIM↑	0.962	0.969	0.973	0.985	0.984	0.973	0.969	0.972	0.973
	LPIPS↓	0.049	0.047	0.050	0.027	0.031	0.044	0.042	0.046	0.042
本文方法	PSNR/dB↑	36.12	38.10	39.45	43.52	44.13	37.58	37.34	39.19	39.44
	SSIM↑	0.967	0.974	0.978	0.9870	0.987	0.977	0.972	0.975	0.977
	LPIPS↓	0.042	0.040	0.041	0.022	0.021	0.037	0.037	0.040	0.034

方法	指标	InSTaR0	InSTaR1	InSTaR2	Avg
SplaTAM	PSNR/dB↑	19.69	21.54	19.82	20.35
	SSIM↑	0.70	0.72	0.76	0.73
	LPIPS↓	0.37	0.29	0.33	0.33
Photo-SLAM	PSNR/dB↑	23.62	22.47	22.27	22.79
	SSIM↑	0.85	0.81	0.78	0.81
	LPIPS↓	0.18	0.21	0.28	0.22
MonoGS-SLAM	PSNR/dB↑	20.04	19.21	18.23	19.16
	SSIM↑	0.78	0.76	0.72	0.75
	LPIPS↓	0.42	0.25	0.52	0.40
GS-ICP SLAM	PSNR/dB↑	21.06	20.21	19.71	20.33
	SSIM↑	0.89	0.81	0.82	0.84
	LPIPS↓	0.19	0.31	0.39	0.30
本文方法	PSNR↑	24.31	22.31	23.43	23.35
	SSIM↑	0.82	0.79	0.83	0.81
	LPIPS↓	0.17	0.25	0.32	0.25

一种自适应剪枝的室内场景三维高斯SLAM实时高保真建模方法

An adaptive-pruning 3D Gaussian SLAM method for real-time high-fidelity indoor scene modeling

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摘要/Abstract

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图/表 16

参考文献 30

相关文章 15

编辑推荐

Metrics

本文评价

方法	Track/Frame/s↓	Map/Frame/s↓	Render FPS↑
SplaTAM	2.045	1.461	189.77
MonoGS-SLAM	1.433	1.764	339.68
Photo-SLAM	0.046	－	364.51
GS-ICP SLAM	0.040	－	354.62
本文方法	0.039	－	392.74

关键帧选择策略	PSNR/dB↑	SSIM↑	LPIPS↓
顺序选择	27.02	0.881	0.212
动态选择	27.35	0.884	0.132
随机选择	36.12	0.967	0.042

方法	PSNR/dB↑	SSIM↑	LPIPS↓	Points↓
随机剪枝	34.43	0.952	0.074	991 732
单属性评分	35.48	0.959	0.066	1 402 103
无可视性评分	35.97	0.966	0.043	1 216 370
无图像结构评分	35.82	0.966	0.055	1 202 214
本文方法	36.08	0.964	0.041	1 032 312

λ取值	PSNR/dB↑	SSIM↑	LPIPS↓	Points↓
0	36.14	0.968	0.041	1 511 241
0.05	35.90	0.964	0.081	780 392
0.1	35.59	0.963	0.054	528 946
0.2	34.54	0.956	0.072	336 440
1.0	32.30	0.941	0.116	163 330

场景	PSNR/dB↑	SSIM↑	LPIPS↓	Points	帧率/（帧/s）↑
large_with_loop	19.5	0.78	0.33	993 494	25.34
large_no_loop	19.28	0.73	0.38	536 032	24.21
fr3/Office	21.95	0.74	0.18	1 869 276	26.98

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致密化	剪枝	PSNR/dB↑	SSIM↑	LPIPS↓	Points↓
×	×	35.43	0.962	0.049	1 511 241
×	√	31.26	0.925	0.149	161 370
√	×	36.14	0.968	0.041	1 602 214
√	√	36.12	0.967	0.042	1 101 801