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Uncertainty-Aware RL-Controlled Adaptive 3D Mapping — arXiv:2610.00188

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.00188
License (as archived): CC0-1.0
Archived: 2026-10-06T19:16:05.095Z
SHA-256 of the archived text: cb5806017b083570829104a832433737d43c20cc8dec271a928bbc29b88f4c84
Stable URL of this page: https://metraipolis.com/documento/dc_802ee7cd07da

How to cite

Uncertainty-Aware RL-Controlled Adaptive 3D Mapping — arXiv:2610.00188 — arXiv (en), https://arxiv.org/abs/2610.00188 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_802ee7cd07da (Metraipolis archive dc_802ee7cd07da, sha256 cb5806017b083570829104a832433737d43c20cc8dec271a928bbc29b88f4c84)

BibTeX

@misc{dc802ee7cd07da,
  title = {Uncertainty-Aware RL-Controlled Adaptive 3D Mapping — arXiv:2610.00188},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.00188},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_802ee7cd07da; sha256 cb5806017b083570829104a832433737d43c20cc8dec271a928bbc29b88f4c84},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.00188. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Voxel-based volumetric mapping is fundamental to 3D reconstruction, yet fixed-resolution grids remain inherently inefficient - wasting memory in uniform regions and losing detail in complex ones. Existing adaptive methods, such as MAP-ADAPT, partially address this by varying resolution based on geometry and user-defined semantic class lists, but these heuristics require expert tuning, lack generalization to unseen objects, and provide no explicit mechanism to control memory usage. We propose an adaptive framework that refines voxels based on semantic entropy, which captures label uncertainty, together with geometric curvature and texture richness as scene complexity cues, yielding principled resolution allocation without reliance on semantic taxonomies. To make the accuracy-memory trade-off explicit and user-controlled, we further introduce a reinforcement learning agent that learns voxel subdivision policies under a user-specified target memory budget, replacing hand-tuned thresholds with a single intuitive control parameter. The resulting multi-resolution TSDF achieves higher geometric accuracy, better semantic consistency, and improved memory-accuracy trade-offs compared to MAP-ADAPT and fixed-resolution baselines on both synthetic and real-world datasets. Our code and models are available at this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-10-02 in cs.CV).
Categorie arXiv: Machine Learning (cs.LG) ; Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Image and Video Processing (eess.IV).
Pagina del lavoro: https://arxiv.org/abs/2610.00188 — 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 2026-10-06T17:37:36.910Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-10-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).