{"ID":5552831,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-03T20:14:26.82372516Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00176","arxiv_id":"2607.00176","title":"PRISM-VO: Scale-Aware Visual Odometry Using Photometric Plenoptic Bundle Adjustment","abstract":"We introduce PRISM-VO, a novel pure optimization-based sparse photometric visual odometry framework for focused plenoptic cameras. The core of PRISM-VO is a novel photometric plenoptic bundle adjustment which jointly optimizes camera poses and inverse depth values of points in a sliding window. By combining geometric depth from a single plenoptic image with temporal multi-view constraints, PRISM-VO achieves accurate and drift-resilient motion estimation. Through explicit modeling of the plenoptic projection, PRISM-VO provides reliable metric-scale reconstructions, overcoming the scale ambiguity of monocular SLAM algorithms. Importantly, our approach relies solely on a single plenoptic sensor and avoids complex initialization, as depth priors are computed directly from plenoptic imaging. Experiments show that PRISM-VO outperforms the current state-of-the-art plenoptic visual odometry method on indoor and outdoor scenes. The proposed approach rivals other optimization- and learning-based methods while accurately and reliably recovering a metric scale of the scene. Project page: https://prism-vo.github.io/","short_abstract":"We introduce PRISM-VO, a novel pure optimization-based sparse photometric visual odometry framework for focused plenoptic cameras. The core of PRISM-VO is a novel photometric plenoptic bundle adjustment which jointly optimizes camera poses and inverse depth values of points in a sliding window. By combining geometric d...","url_abs":"https://arxiv.org/abs/2607.00176","url_pdf":"https://arxiv.org/pdf/2607.00176v1","authors":"[\"Aymeric Fleith\",\"Julian Zirbel\",\"Daniel Cremers\",\"Niclas Zeller\"]","published":"2026-06-30T20:50:11Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}