LiFMCR: Dataset and Benchmark for Light Field Multi-Camera Registration
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915556144185344 |
|---|---|
| author | Fleith, Aymeric Zirbel, Julian Cremers, Daniel Zeller, Niclas |
| author_facet | Fleith, Aymeric Zirbel, Julian Cremers, Daniel Zeller, Niclas |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13729 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LiFMCR: Dataset and Benchmark for Light Field Multi-Camera Registration Fleith, Aymeric Zirbel, Julian Cremers, Daniel Zeller, Niclas Computer Vision and Pattern Recognition 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/ |
| title | LiFMCR: Dataset and Benchmark for Light Field Multi-Camera Registration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.13729 |