构建首个多相机光场注册数据集，支持高精度多视角光场处理。
LiFMCR: Dataset and Benchmark for Light Field Multi-Camera Registration
- 基于双Raytrix R32相机同步采集，融合Vicon高精度姿态数据
- 提出两种互补注册方法，实现6自由度精确定位
- 适合光场三维重建、多相机系统校准的研究者使用
我们提出了LiFMCR，一个针对基于微透镜阵列（MLA）的多相机光场系统注册的新数据集。现有光场数据集通常仅支持单相机设置且缺乏外部真值，而LiFMCR提供了来自两台高分辨率Raytrix R32全向相机的同步图像序列，并结合了由Vicon运动捕捉系统记录的高精度6自由度（DoF）姿态。该组合使得对多相机光场注册方法进行严格评估成为可能。作为基准，我们提供两种互补的注册方法：一种基于跨视图点云的RANSAC方法实现鲁棒的3D变换估计；另一种为从单个光场图像中估计外参6-DoF姿态的全向摄影PnP算法。两者均显式整合了全向相机模型，支持精确且可扩展的多相机注册。实验表明结果与真值高度一致，支持可靠的多视角光场处理。项目页面：https://lifmcr.github.io/
原文摘要 · Abstract (English)
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/
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