论文标题
Simugan：无监督的前向建模和LIDAR相机的最佳设计
SimuGAN: Unsupervised forward modeling and optimal design of a LIDAR Camera
论文作者
论文摘要
短距离的节能LIDAR相机使用时间强度编码的激光脉冲估算对象的距离，并计算与后散射脉冲的最大相关性。 尽管在低功率上，背面散落的脉搏嘈杂且不稳定，这导致了不准确和不可靠的深度估计。 为了解决这个问题，我们使用gans（生成对抗网络），这是两个神经网络，可以通过对抗过程学习复杂的类分布。我们学习了LiDAR相机的隐藏属性和行为，创建了一种模拟摄像机的新颖，完全无监督的远期模型。然后，我们使用模型的不同性来探索相机参数空间并根据深度，准确性和稳定性优化这些参数。为了实现这一目标，我们还提出了一个新的自定义损失函数，该功能指定为背面代码分布的弱点及其循环行为。结果在综合数据和实际数据上都得到了证明。
Energy-saving LIDAR camera for short distances estimates an object's distance using temporally intensity-coded laser light pulses and calculates the maximum correlation with the back-scattered pulse. Though on low power, the backs-scattered pulse is noisy and unstable, which leads to inaccurate and unreliable depth estimation. To address this problem, we use GANs (Generative Adversarial Networks), which are two neural networks that can learn complicated class distributions through an adversarial process. We learn the LIDAR camera's hidden properties and behavior, creating a novel, fully unsupervised forward model that simulates the camera. Then, we use the model's differentiability to explore the camera parameter space and optimize those parameters in terms of depth, accuracy, and stability. To achieve this goal, we also propose a new custom loss function designated to the back-scattered code distribution's weaknesses and its circular behavior. The results are demonstrated on both synthetic and real data.
