A major obstacle to the development of effective monocular depth estimation algorithms is the difficulty in obtaining high-quality depth data that corresponds to collected RGB images. Collecting this data is time-consuming and costly, and even data collected by modern sensors has limited range or resolution, and is subject to inconsistencies and noise. To combat this, we propose a method of data generation in simulation using 3D synthetic environments and CycleGAN domain transfer. We compare this method of data generation to the popular NYUDepth V2 dataset by training a depth estimation model based on the DenseDepth structure using different training sets of real and simulated data. We evaluate the performance of the models on newly collected images and LiDAR depth data from a Husky robot to verify the generalizability of the approach and show that GAN-transformed data can serve as an effective alternative to real-world data, particularly in depth estimation.

翻译：开发高效单目深度估计算法的主要障碍在于难以获取与采集的RGB图像相对应的高质量深度数据。收集此类数据既耗时又昂贵，即便使用现代传感器采集的数据也存在范围或分辨率限制，并受噪声与不一致性的影响。为解决这一问题，我们提出了一种利用三维合成环境与CycleGAN域迁移的仿真数据生成方法。通过基于DenseDepth结构的深度估计模型，对比使用不同真实与模拟数据训练集，将该数据生成方法与流行的NYUDepth V2数据集进行比较。我们基于Husky机器人新采集的图像与LiDAR深度数据评估模型性能，以验证方法的泛化能力，并证明经GAN转换的数据可作为真实数据的有效替代方案，尤其在深度估计任务中表现突出。