论文标题
适应的项目：适应噪音和稀疏传感器数据深度完成的域
Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data
论文作者
论文摘要
深度完成旨在从稀疏深度输入中预测密集的深度图。对深度完成设置的密集地面真相注释的获取可能很困难，同时，实际发光剂测量和合成数据之间存在显着的域间隙，这阻止了在虚拟设置中成功培训模型的成功培训。我们提出了一种稀疏深度完成的域适应方法，该方法是根据合成数据训练的，而无需在真实域或其他传感器中进行注释。我们的方法模拟了RGB+激光雷达设置中的真实传感器噪声，并由三个模块组成：通过投影模拟合成域中的真实激光雷达输入，通过使用循环gan方法来过滤实际的噪声激光镜头以进行监督和调整合成RGB图像。我们对Kitti深度完成基准中最先进的最先进的模块进行了广泛的评估，显示了显着改进。
Depth completion aims to predict a dense depth map from a sparse depth input. The acquisition of dense ground truth annotations for depth completion settings can be difficult and, at the same time, a significant domain gap between real LiDAR measurements and synthetic data has prevented from successful training of models in virtual settings. We propose a domain adaptation approach for sparse-to-dense depth completion that is trained from synthetic data, without annotations in the real domain or additional sensors. Our approach simulates the real sensor noise in an RGB+LiDAR set-up, and consists of three modules: simulating the real LiDAR input in the synthetic domain via projections, filtering the real noisy LiDAR for supervision and adapting the synthetic RGB image using a CycleGAN approach. We extensively evaluate these modules against the state-of-the-art in the KITTI depth completion benchmark, showing significant improvements.
