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Journal of System Simulation

Abstract

Abstract: To enhance the geometry reconstruction quality of the GS algorithm in large-scale scene reconstruction, an optimization method constrained by multi-view geometry reconstruction results was proposed. 2D Gaussian planes were used as geometric primitives to overcome depth anisotropy, and dense depth maps generated by DUSt3R and aligned by sparse point clouds were introduced as constraints. By designing a multi-stage optimization strategy that decouples geometry and rendering, the gradient conflict problem in multi-objective training was solved. Experiments on the MatrixCity dataset indicate that the method surpasses comparison methods in related indicators of geometry reconstruction quality and rendering quality in large-scale scenes. The multi-view reconstruction results demonstrate the effectiveness in improving the geometry reconstruction quality of the GS algorithm.

First Page

2353

Last Page

2363

CLC

TP391.9

Recommended Citation

Cui Haohao, Di Yanqiang, Liu Qing, et al. Three-dimensional Gaussian Reconstruction of Large-scale Scenes Under Multi-view Geometry Constraints[J]. Journal of System Simulation, 2026, 38(8): 2353-2363.

Corresponding Author

Di Yanqiang

DOI

10.16182/j.issn1004731x.joss.25-0925

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