Aug 2026· Remote Sensing· 0 citations· 45 references
TL;DR
This paper introduces 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.
Abstract
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.
These results show that, in the controlled direct illumination setting, compact beta surfels combined with transport-based optimization can recover surfaces without relying on the tens to hundreds of thousands of primitives used by the evaluated baselines.
M. K. Gjerde, J. B. Haurum, J. Frisvad et al.· 0 citations
This work proposes an Iterative Spatial Decomposition framework that bridges dense geometric priors from Multi-View Stereo (MVS) with Gaussian Splatting and introduces Hierarchical Geometric Prior Sampling (HGPS), which substantially reduce redundancy in MVS point clouds while preserving critical details, thereby providing a more robust geometric foundation for reconstruction.
Zonghua Yu, Junhuai Li, Huaijun Wang et al.· ACM Transactions on Multimed...· 0 citations
Experiments show that CoMVS-GS remains competitive on object-level reconstruction and improves geometric accuracy and mesh compactness in outdoor scenes while maintaining high rendering quality.
Shihan Chen, Junjing Zhang, Q. Yan et al.· 0 citations
A semantic-guided 3D Gaussian splatting (3DGS) framework tailored to sparse-view industrial reconstruction was introduced, enabling robust reconstruction from limited viewpoints and offers a practical geometric foundation for automated inspection and remote equipment monitoring.
Boyang Li, Tian-Han Gao, Zuan Gu et al.· Visual Computing for Industr...· 0 citations
A 3D-GS-based framework that attaches a simplified Disney Bidirectional Reflectance Distribution Function (BRDF) and differentiable physically based rendering (PBR) shader to each surface Gaussian for interpretable material attributes delivers higher geometric accuracy, improved material realism, and real-time rendering performance suitable for applications such as Simultaneous Localization and Mapping (SLAM) and relighting.
Wei-Chen Xu, W. Wan, M. Peng· International Conference on...· 0 citations
Abstract. The proliferation of continuous Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) has shifted the paradigm of 3D aerial reconstruction from relying solely on geometric stereo matching to inverse rendering optimization. However, while these emerging rendering-based frameworks excel in synthesizing photo-realistic novel views, their capability to extract accurate surfaces in complex aerial scenarios remains ambiguous compared to traditional methods. To establish a clearer understanding, this study presents a comprehensive evaluation of five representative frameworks spanning traditional Structure from Motion (SfM), purely Signed Distance Field (SDF) representations, unstructured 3D Gaussians, hybrid voxel-Gaussians, and strictly explicit sparse voxels. By systematically standardizing identical computational environments, inputs, and unified mesh-extraction pipelines on both real-world airborne LiDAR datasets and synthetic cityscapes, we assess their performance regarding visual fidelity, geometric accuracy, and resource efficiency. The experimental results reveal that while traditional MVS produces the highest overall geometric precision by strictly enforcing multi-view rigid geometry, it is prone to failures in texture-less regions. Among rendering-based representations, a fundamental trade-off exists: highly flexible, unstructured 3DGS achieve highest visual scores but degrade the underlying geometric surfaces; conversely, explicitly structured techniques demonstrate distinct superiority in regularizing topological coherence and floating artifact suppression. Furthermore, we observe that integrating structured voxels avoids the severe memory bottlenecks associated with extracting geometries from chaotic unorganized primitives. These empirical findings emphasize that for large-scale aerial photogrammetry, integrating explicit spatial structuralization into differentiable rendering pipelines is imperative for achieving scalable operations and bridging the geometric accuracy gap with traditional methods.
Shihan Chen, Zhaojin Li, Q. Yan et al.· The International Archives o...· 0 citations
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