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Prior-Driven Enhancements in 3D Gaussian Splatting: Normals and Depths Regularization — arXiv:2609.36969

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.36969
License (as archived): CC0-1.0
Archived: 2026-10-07T18:38:42.443Z
SHA-256 of the archived text: 9d046ead2eaf22c0f976d01910c504f9d26299de52102744b141dbbbfc9dd4cd
Stable URL of this page: https://metraipolis.com/documento/dc_5aff28568fe6

How to cite

Prior-Driven Enhancements in 3D Gaussian Splatting: Normals and Depths Regularization — arXiv:2609.36969 — arXiv (en), https://arxiv.org/abs/2609.36969 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_5aff28568fe6 (Metraipolis archive dc_5aff28568fe6, sha256 9d046ead2eaf22c0f976d01910c504f9d26299de52102744b141dbbbfc9dd4cd)

BibTeX

@misc{dc5aff28568fe6,
  title = {Prior-Driven Enhancements in 3D Gaussian Splatting: Normals and Depths Regularization — arXiv:2609.36969},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.36969},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_5aff28568fe6; sha256 9d046ead2eaf22c0f976d01910c504f9d26299de52102744b141dbbbfc9dd4cd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.36969. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

3D Gaussian Splatting (3DGS) is a state-of-the-art technique for 3D scene rendering, offering high efficiency and excellent visual quality. However, because 3DGS relies on an initial sparse point set from Structure-from-Motion (SfM) and view-dependent properties, it can suffer from geometric inaccuracies and visual artifacts, particularly in complex scenes. To address these challenges, we propose an improved 3DGS approach that regularizes the optimization process by integrating geometric priors, including surface normals and dense depth information. Surface normal regularization improves geometric consistency by aligning Gaussian covariance with local surface structures, while dense depth priors combined with an initial points from SfM enhance per-pixel depth estimation, increasing accuracy and reducing ambiguities. These enhancements enable robust handling of diverse and complex real-world scenarios, minimizing visual distortions and improving reconstruction quality across various environments. To validate our method, we evaluate it on challenging datasets, including street-view scenes and highly reflective environments, while testing it across multiple SfM pipelines. Our results demonstrate compatibility across diverse environments and highlight the robustness of our approach. Experimental findings further show that our method enhances geometric accuracy and visual quality, establishing a reliable solution for real-time 3D scene rendering in complex environments.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-30 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.36969 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T18:04:24.788Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).