融合几何先验与重要性采样的室内场景高精度重建方法

doi:10.16182/j.issn1004731x.joss.25-1055

系统仿真学报 ›› 2026, Vol. 38 ›› Issue (3): 584-594.doi: 10.16182/j.issn1004731x.joss.25-1055

• 专栏 • 上一篇

融合几何先验与重要性采样的室内场景高精度重建方法

杨涛¹, 石敏¹, 赵熙钢¹, 王素琴¹, 王祺¹, 朱登明²^,³

^1.华北电力大学控制与计算机工程学院，北京 102206
^2.中国科学院计算技术研究所，北京 100190
^3.太仓中科信息技术研究院江苏太仓 215400

收稿日期:2025-10-30 修回日期:2026-01-05 出版日期:2026-03-18 发布日期:2026-03-27
通讯作者: 石敏
第一作者简介:杨涛(2001-)，男，硕士生，研究方向为三维场景重建。
基金资助:
苏州市科技计划前沿技术研究项目(SYG202327)

Integrating Geometric Priors and Importance Sampling for High-fidelity Indoor Scene Reconstruction

Yang Tao¹, Shi Min¹, Zhao Xigang¹, Wang Suqin¹, Wang Qi¹, Zhu Dengming²^,³

^1.School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
^2.Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
^3.Taicang-CAS Institute of Information and Technology, Taicang 215400, China

Received:2025-10-30 Revised:2026-01-05 Online:2026-03-18 Published:2026-03-27
Contact: Shi Min

摘要/Abstract

摘要：

针对3D高斯溅射在几何重建，尤其是弱纹理室内场景中几何结构容易失真的问题，提出一种融合几何先验与重要性采样的室内场景高精度重建方法。在设计上充分考虑初始化对重建质量的影响，利用先进的前馈模型生成高质量几何初始化，提升整体重建稳定性与精度。引入重要性采样策略以减轻模糊图像的不利影响，并设计一种基于几何先验模型的几何监督机制，通过约束场景结构进一步提升重建的几何一致性与精度。实验结果表明：该方法提升了重建质量，有效缓解了室内场景重建出现的几何结构失真问题。

关键词: 三维重建, 高斯溅射, 室内场景, 前馈模型, 几何先验, 表面重建

Abstract:

Gaussian splatting suffers from geometric distortion during scene reconstruction, particularly in weakly textured indoor scenes. To address this issue, this paper proposes a high-precision indoor scene reconstruction method that integrates geometric priors and importance sampling. The proposed method fully considers the effect of the initialization process on reconstruction quality. An advanced feed-forward model is employed to generate high-quality geometric initialization, thus improving overall reconstruction stability and accuracy. An importance sampling strategy is introduced to mitigate the adverse effects of blurry images. Furthermore, a supervision mechanism based on a geometric prior model is designed to constrain the scene structure, further enhancing geometric consistency and reconstruction accuracy. Experimental results show that the proposed method improves reconstruction quality and effectively alleviates geometric structural distortion in indoor scene reconstruction.

Key words: 3D reconstruction, Gaussian splatting, indoor scene, feed-forward model, geometric prior, surface reconstruction

中图分类号:

TP37

杨涛,石敏,赵熙钢等 . 融合几何先验与重要性采样的室内场景高精度重建方法[J]. 系统仿真学报, 2026, 38(3): 584-594.

Yang Tao,Shi Min,Zhao Xigang,et al . Integrating Geometric Priors and Importance Sampling for High-fidelity Indoor Scene Reconstruction[J]. Journal of System Simulation, 2026, 38(3): 584-594.

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方法	准确率	完整率	精确率	召回率	F1分数	训练时间/min
SuGaR	0.081 2	0.101 8	0.591 4	0.507 0	0.515 6	120.67
RaDe-GS	0.063 8	0.076 1	0.618 2	0.631 1	0.623 8	13.65
2DGS	0.076 1	0.108 6	0.675 1	0.577 1	0.622 2	17.96
本文算法	0.021 7	0.050 6	0.936 8	0.793 7	0.859 2	30.43

方法	准确率	完整率	精确率	召回率	F1分数	训练时间/min
SuGaR	0.115 4	0.115 9	0.419 0	0.400 6	0.409 0	86.51
RaDe-GS	0.125 7	0.152 0	0.380 7	0.410 1	0.392 6	7.43
2DGS	0.149 8	0.123 4	0.427 1	0.433 4	0.428 9	8.86
本文算法	0.063 8	0.046 8	0.733 8	0.693 3	0.711 4	15.12

方法	准确率	完整率	精确率	召回率	F1分数
基线模型	0.076 1/0.149 8	0.108 6/0.123 4	0.675 1/0.427 1	0.577 1/0.433 4	0.622 2/0.428 9
+前馈模型初始化	0.024 7/0.081 1	0.062 3/0.063 3	0.896 6/0.634 3	0.749 2/0.627 9	0.816 2/0.629 4
+重要图片采样	0.024 3/0.076 8	0.061 6/0.058 0	0.918 1/0.677 9	0.771 8/0.659 4	0.838 5/0.666 8
+几何先验监督(完整模型)	0.021 7/0.063 8	0.050 6/0.046 8	0.938 6/0.733 8	0.793 7/0.693 3	0.859 2/0.711 4

融合几何先验与重要性采样的室内场景高精度重建方法

Integrating Geometric Priors and Importance Sampling for High-fidelity Indoor Scene Reconstruction

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融合几何先验与重要性采样的室内场景高精度重建方法

​​Integrating Geometric Priors and Importance Sampling for High-fidelity Indoor Scene Reconstruction

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Integrating Geometric Priors and Importance Sampling for High-fidelity Indoor Scene Reconstruction