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D3GS: Depth, DINO, and RGB Diffusion Co-Guided 3D Gaussian Splatting for Sparse-View Reconstruction — arXiv:2609.22941

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.22941
License (as archived): CC0-1.0
Archived: 2026-10-09T21:59:21.160Z
SHA-256 of the archived text: 41ef14a0fbfa19bdb0587c24ec8d378bc3db0b34143b2ca2d797319326a707f3
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How to cite

D3GS: Depth, DINO, and RGB Diffusion Co-Guided 3D Gaussian Splatting for Sparse-View Reconstruction — arXiv:2609.22941 — arXiv (en), https://arxiv.org/abs/2609.22941 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_481a3a809ab0 (Metraipolis archive dc_481a3a809ab0, sha256 41ef14a0fbfa19bdb0587c24ec8d378bc3db0b34143b2ca2d797319326a707f3)

BibTeX

@misc{dc481a3a809ab0,
  title = {D3GS: Depth, DINO, and RGB Diffusion Co-Guided 3D Gaussian Splatting for Sparse-View Reconstruction — arXiv:2609.22941},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.22941},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_481a3a809ab0; sha256 41ef14a0fbfa19bdb0587c24ec8d378bc3db0b34143b2ca2d797319326a707f3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.22941. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Novel view synthesis from sparse inputs remains challenging for 3D Gaussian Splatting (3DGS) due to ambiguous geometry, cross-view inconsistency, and missing details in under-constrained regions, resulting in degraded reconstruction and unstable rendering. To tackle these issues, we propose D$^{3}$GS, a Depth-DINO-Diffusion guided sparse-view Gaussian reconstruction framework that jointly enhances geometry and appearance. D$^{3}$GS first recovers a high-resolution, metric depth map via diffusion-based completion and DPT (Dense Prediction Transformer) refinement, providing robust Gaussian initialization and geometric constraints. Then, a DINO-guided view-consistent learning is introduced to augment Gaussian attributes with structural features, improving multi-view consistency. Finally, a diffusion-based Gaussian refinement module injects generative priors into an iterative optimization strategy, enhancing high-frequency geometric and appearance details within the Gaussian representation. Experiments on DTU, LLFF, and Mip-NeRF 360 show that D$^{3}$GS achieves consistent and substantial improvements over strong baselines, with ablation studies validating the effectiveness and complementary roles of each component.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-22 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.22941 — 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 2026-10-09T21:45:20.830Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).