{"ID":2836821,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.20050","arxiv_id":"2511.20050","title":"Active3D: Active High-Fidelity 3D Reconstruction via Hierarchical Uncertainty Quantification","abstract":"In this paper, we present an active exploration framework for high-fidelity 3D reconstruction that incrementally builds a multi-level uncertainty space and selects next-best-views through an uncertainty-driven motion planner. We introduce a hybrid implicit-explicit representation that fuses neural fields with Gaussian primitives to jointly capture global structural priors and locally observed details. Based on this hybrid state, we derive a hierarchical uncertainty volume that quantifies both implicit global structure quality and explicit local surface confidence. To focus optimization on the most informative regions, we propose an uncertainty-driven keyframe selection strategy that anchors high-entropy viewpoints as sparse attention nodes, coupled with a viewpoint-space sliding window for uncertainty-aware local refinement. The planning module formulates next-best-view selection as an Expected Hybrid Information Gain problem and incorporates a risk-sensitive path planner to ensure efficient and safe exploration. Extensive experiments on challenging benchmarks demonstrate that our approach consistently achieves state-of-the-art accuracy, completeness, and rendering quality, highlighting its effectiveness for real-world active reconstruction and robotic perception tasks.","short_abstract":"In this paper, we present an active exploration framework for high-fidelity 3D reconstruction that incrementally builds a multi-level uncertainty space and selects next-best-views through an uncertainty-driven motion planner. We introduce a hybrid implicit-explicit representation that fuses neural fields with Gaussian...","url_abs":"https://arxiv.org/abs/2511.20050","url_pdf":"https://arxiv.org/pdf/2511.20050v1","authors":"[\"Yan Li\",\"Yingzhao Li\",\"Gim Hee Lee\"]","published":"2025-11-25T08:17:32Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"LoRA\"]","has_code":false}