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AGILE-GS: Anchor-Guided Fast Next-Best-View Selection for Active 3D Gaussian Splatting — arXiv:2609.34176

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.34176
License (as archived): CC0-1.0
Archived: 2026-10-07T23:51:55.450Z
SHA-256 of the archived text: f32300937122b7bcf188db2aa16054af58d1d662a7299072388dc5299ed2cb06
Stable URL of this page: https://metraipolis.com/documento/dc_4efbe48eaebd

How to cite

AGILE-GS: Anchor-Guided Fast Next-Best-View Selection for Active 3D Gaussian Splatting — arXiv:2609.34176 — arXiv (en), https://arxiv.org/abs/2609.34176 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4efbe48eaebd (Metraipolis archive dc_4efbe48eaebd, sha256 f32300937122b7bcf188db2aa16054af58d1d662a7299072388dc5299ed2cb06)

BibTeX

@misc{dc4efbe48eaebd,
  title = {AGILE-GS: Anchor-Guided Fast Next-Best-View Selection for Active 3D Gaussian Splatting — arXiv:2609.34176},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.34176},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4efbe48eaebd; sha256 f32300937122b7bcf188db2aa16054af58d1d662a7299072388dc5299ed2cb06},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.34176. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Radiance fields need hundreds of views, and their placement matters as much as their number. Next-best-view (NBV) selection for 3D Gaussian Splatting (3DGS) usually scores every candidate in the pool and keeps one. Searching for information and choosing a camera, however, are separable problems. We present AGILE-GS, an anchor-guided NBV method that separates the two. A virtual anchor pose is optimized on SE(3) by Riemannian gradient ascent on expected information gain. It need not be reachable or in the pool; it marks where the model is most uncertain. Candidates are scored against the anchor's viewing geometry, and a greedy ridge-leverage step distills the pool into a small, non-redundant shortlist without rendering any candidate. The shortlist can be used in two ways. AGILE-GS takes the first view on it as the next view, so no Fisher information is computed for any candidate. AGILE-GS+ computes the Fisher information gain of each shortlisted view and picks the best, so the expensive evaluation runs on a handful of views rather than the whole pool. On standard benchmarks and in closed-loop embodied acquisition, both match or exceed existing baselines while cutting selection latency by one to two orders of magnitude.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-29 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.34176 — 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 2026-10-07T23:29:27.306Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).