论文标题
标准元素相机中的基线和三角剖分几何形状
Baseline and Triangulation Geometry in a Standard Plenoptic Camera
论文作者
论文摘要
在本文中,我们演示了光场三角剖分,以确定深度距离和基准在全体摄像机中。微镜头和图像传感器的进步使元素摄像头能够从具有足够空间分辨率的不同观点捕获场景。虽然可以使用三角剖分在立体声观点对中从差异中推断出对象距离,但在元素摄像机的情况下应用时,该概念仍然模棱两可。我们提出了一个几何光场模型,允许将三角剖分应用于元素摄像头,以预测对象距离或根据需要指定基准。结果表明,我们新颖方法的距离估计值与放置在相机前面的真实对象的估计值相匹配。使用光学设计软件进行的其他基准测试进一步验证了该模型的精度,而几种主要镜头类型和重点设置的偏差小于-0.33%。汽车和机器人技术领域中的各种应用可以从此估计模型中受益。
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.