{"ID":2855306,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.13729","arxiv_id":"2510.13729","title":"LiFMCR: Dataset and Benchmark for Light Field Multi-Camera Registration","abstract":"We present LiFMCR, a novel dataset for the registration of multiple micro lens array (MLA)-based light field cameras. While existing light field datasets are limited to single-camera setups and typically lack external ground truth, LiFMCR provides synchronized image sequences from two high-resolution Raytrix R32 plenoptic cameras, together with high-precision 6-degrees of freedom (DoF) poses recorded by a Vicon motion capture system. This unique combination enables rigorous evaluation of multi-camera light field registration methods. As a baseline, we provide two complementary registration approaches: a robust 3D transformation estimation via a RANSAC-based method using cross-view point clouds, and a plenoptic PnP algorithm estimating extrinsic 6-DoF poses from single light field images. Both explicitly integrate the plenoptic camera model, enabling accurate and scalable multi-camera registration. Experiments show strong alignment with the ground truth, supporting reliable multi-view light field processing. Project page: https://lifmcr.github.io/","short_abstract":"We present LiFMCR, a novel dataset for the registration of multiple micro lens array (MLA)-based light field cameras. While existing light field datasets are limited to single-camera setups and typically lack external ground truth, LiFMCR provides synchronized image sequences from two high-resolution Raytrix R32 plenop...","url_abs":"https://arxiv.org/abs/2510.13729","url_pdf":"https://arxiv.org/pdf/2510.13729v1","authors":"[\"Aymeric Fleith\",\"Julian Zirbel\",\"Daniel Cremers\",\"Niclas Zeller\"]","published":"2025-10-15T16:32:27Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}