Skip to content

Author

Siyuan Zou

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Aug 2026

Controlled Accuracy Degradation of Photogrammetric 3D City Models

Photogrammetric 3D city models contain detailed planimetric and elevation information that supports urban visualization and low-altitude applications. However, the direct dissemination of high-accuracy models may expose sensitive geometric measurements. Existing protection methods mainly focus on conventional encryption, coordinate scrambling, or two-dimensional data perturbation and do not adequately balance geometric accuracy degradation with the visual usability of textured 3D meshes. This study proposes a controlled geometric deformation method that processes the planimetric and elevation components independently. In the horizontal domain, a normalized Sigmoid function generates smooth, bounded, and spatially varying coordinate displacements. In the vertical domain, a normalized deformation function combines global elevation stretching with amplitude-constrained sine-wave superposition. The sine-wave parameters are generated using a seed-sensitive hybrid cascaded chaotic system, producing reproducible but model-dependent nonlinear deformation patterns. During processing, the mesh connectivity, face indices, texture coordinates, texture images, and material relationships remain unchanged. The method was evaluated using low-rise and high-rise photogrammetric 3D scenes with different horizontal extents and elevation characteristics. Under the selected 10 m planimetric and 5% elevation settings, the mean planimetric displacements were 10.474 and 10.045 m, while the relative elevation deformations were 5.01% and 5.30%, respectively. Both datasets maintained monotonic elevation relationships and achieved 100% direction consistency. Their spatial-shape coefficients deviated from the corresponding reference values by only 0.02% and 1.33%. The results demonstrate that the proposed method provides controllable and spatially continuous geometric deformation while maintaining mesh connectivity, overall morphology, and visual interpretability. It can therefore serve as a practical pre-processing approach for the risk-reduced dissemination and non-measurement-oriented visualization of photogrammetric 3D city models.

Siyuan Zou, Zibo Xu, Yiwen Wang et al. · 0 citations
Open access Aug 2026

GSSA: Gaussian Surfels with Spatial Awareness for Surface Reconstruction

3D Gaussian Splatting is effective in multi-view surface reconstruction tasks, fundamental to photogrammetry applications. Mainstream pipelines focus on optimizing for explicit Gaussians to enhance volumetric rendering quality, typically relying on rendering-based Truncated Signed Distance Function (TSDF) methods for reconstruction, while the training and meshing often suffer from missing details and geometric distortions under occlusions or restricted viewpoints. In this paper, we focus on a new problem definition: directly formulating implicit surface representation exploiting geometric features of trained Gaussian Surfels. For this problem, we introduce Gaussian Surfels with Spatial Awareness (GSSA) for reconstruction, a spatially aware reconstruction framework that optimizes for Surfel distributions from geometric primitives via a 3D refiner, and constructs SDF by integrating signed distances from voxel vertices to nearby Surfels. Compared with state-of-the-art GS-based, especially Surfel-based, reconstruction methods, GSSA achieves both competitive geometric accuracy and efficiency. On terrestrial (indoor object) and airborne (photogrammetry) reconstruction benchmarks, GSSA maintains a competitive balance between reconstruction time and accuracy. Further migration experiments demonstrate that GSSA can also be integrated as a plug-and-play module into general Surfel-based pipelines.

Hao Tang, Siyuan Zou, Hongbo Pan et al. · 0 citations