论文标题
机器人模拟的摄像机模拟：各种相机模型组件的重要性有多？
Camera simulation for robot simulation: how important are various camera model components?
论文作者
论文摘要
建模用于模拟自主机器人技术的摄像机对于生成具有适当现实主义的合成图像至关重要，以有效地评估模拟中的感知算法。但是，在许多情况下，模拟图像是由传统的渲染技术产生的，这些技术排除或表面处理了实际摄像机管道中遇到的处理步骤和方面。此贡献的目的是量化各种图像生成步骤或方面的相机模型中排除的程度会影响机器人技术中的SIM到现实差距。我们研究如果忽略从物理摄像机内部的过程（例如，镜头失真，噪声和信号处理）绑定的方面，会发生什么；场景效果，例如照明和反射；和渲染质量。该研究的结果表明，对颜色，场景和位置的大规模变化远比与局部特征级文物有关的模型方面的影响要大得多。此外，我们表明这些场景级别的方面可能源于镜头的失真和信号处理，尤其是在考虑白平衡和自动暴露建模时。
Modeling cameras for the simulation of autonomous robotics is critical for generating synthetic images with appropriate realism to effectively evaluate a perception algorithm in simulation. In many cases though, simulated images are produced by traditional rendering techniques that exclude or superficially handle processing steps and aspects encountered in the actual camera pipeline. The purpose of this contribution is to quantify the degree to which the exclusion from the camera model of various image generation steps or aspects affect the sim-to-real gap in robotics. We investigate what happens if one ignores aspects tied to processes from within the physical camera, e.g., lens distortion, noise, and signal processing; scene effects, e.g., lighting and reflection; and rendering quality. The results of the study demonstrate, quantitatively, that large-scale changes to color, scene, and location have far greater impact than model aspects concerned with local, feature-level artifacts. Moreover, we show that these scene-level aspects can stem from lens distortion and signal processing, particularly when considering white-balance and auto-exposure modeling.
