One-shot view planning and online optimization-based replanning for unknown object reconstruction

Patino Minan, Jose, Hernandez Vega, Juan ORCID: https://orcid.org/0000-0002-9593-6789, Romero Cano, Victor ORCID: https://orcid.org/0000-0003-2910-5116, Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 and Kingston, Zachary 2026. One-shot view planning and online optimization-based replanning for unknown object reconstruction. Presented at: IEEE International Conference on Robotics and Automation (ICRA) 2026, Vienna, Austria, 01-05 June 2026. Item availability restricted.

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Abstract

Robotic inspection tasks often require constructing high-quality 3D models of objects from a minimal number of views. Traditional next-best view planning (NBVP) approaches incrementally select view poses but fail to account for global optimality of the inspection trajectory, thus leading to inefficient inspection paths. Recent one-shot view planning (OSVP) methods address this challenge by predicting informative view poses from an initial observation. While subsequent improvements on the pioneering OSVP approach attempt to improve prediction accuracy, they can still fail when faced with out of distribution(OoD) examples. With recent advances in generative modeling, OSVP methods can infer a plausible object shape from one observation and then derive the corresponding solution set of view poses. However, because the predicted shape may deviate from the true geometry, these methods can still generate infeasible views. To overcome these limitations, we propose a novel OSVP framework that leverages RGB-D data to generate geometric priors and incorporates online video-based reconstruction. Our method formulates viewpoint selection and path optimization, so that both the calculated poses and the connecting trajectories satisfy visibility constraints, maintain smoothness, and can be locally replanned to compensate for discrepancies between predicted and real object geometries. We validate our OSVP approach through simulation benchmarks against state-of-the-art OSVP techniques and demonstrate its effectiveness on a real Franka Emika manipulator.

Item Type:	Conference or Workshop Item - published (Paper)
Status:	In Press
Schools:	Schools > Computer Science & Informatics
Date of First Compliant Deposit:	11 February 2026
Date of Acceptance:	31 January 2026
Last Modified:	12 Feb 2026 11:27
URI:	https://orca.cardiff.ac.uk/id/eprint/184709

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