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Baseline and Triangulation Geometry in a Standard Plenoptic Camera
International Journal of Computer Vision ( IF 19.5 ) Pub Date : 2017-08-20 , DOI: 10.1007/s11263-017-1036-4
Christopher Hahne , Amar Aggoun , Vladan Velisavljevic , Susanne Fiebig , Matthias Pesch

In this paper, we demonstrate light field triangulation to determine depth distances and baselines in a plenoptic camera. Advances in micro lenses and image sensors have enabled plenoptic cameras to capture a scene from different viewpoints with sufficient spatial resolution. While object distances can be inferred from disparities in a stereo viewpoint pair using triangulation, this concept remains ambiguous when applied in the case of plenoptic cameras. We present a geometrical light field model allowing the triangulation to be applied to a plenoptic camera in order to predict object distances or specify baselines as desired. It is shown that distance estimates from our novel method match those of real objects placed in front of the camera. Additional benchmark tests with an optical design software further validate the model’s accuracy with deviations of less than ±0.33% for several main lens types and focus settings. A variety of applications in the automotive and robotics field can benefit from this estimation model.

中文翻译:

标准全光相机中的基线和三角测量几何

在本文中,我们演示了光场三角测量以确定全光相机中的深度距离和基线。微透镜和图像传感器的进步使全光相机能够以足够的空间分辨率从不同的视点捕捉场景。虽然可以使用三角测量从立体视点对中的视差推断出物距,但当应用于全光相机时,这个概念仍然不明确。我们提出了一个几何光场模型,允许将三角测量应用于全光相机,以便预测物体距离或根据需要指定基线。结果表明,我们的新方法的距离估计与放置在相机前面的真实物体的距离估计相匹配。使用光学设计软件进行的其他基准测试进一步验证了模型的准确性,几种主要镜头类型和对焦设置的偏差小于 ±0.33%。汽车和机器人领域的各种应用都可以从这种估计模型中受益。
更新日期:2017-08-20
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