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/
Cite
@article{arxiv.2510.13729,
title = {LiFMCR: Dataset and Benchmark for Light Field Multi-Camera Registration},
author = {Aymeric Fleith and Julian Zirbel and Daniel Cremers and Niclas Zeller},
journal= {arXiv preprint arXiv:2510.13729},
year = {2025}
}
Comments
Accepted at the International Symposium on Visual Computing (ISVC) 2025