SynerFill: A Synergistic RGB-D Image Inpainting Network via Fast Fourier Convolutions

Map inpainting is an important technology in the production of maps for autonomous driving vehicles. In recent years, scholars have often used methods such as point cloud inpainting, RGB image inpainting, and depth inpainting to repair maps. However, these methods require high computational power and result in longer algorithmic processing times. To address this issue, we propose SynerFill, a synergistic RGB-D images inpainting method that can simultaneously inpaint RGB and depth images. We design its network architecture and loss functions, which include a generator, an RGB image discriminator, a depth image discriminator, and an edge image discriminator. Second, we collect real-world data and build a large-scale, multi-scene, multi-weather dataset called the Synthetic City RGB-D (SCRGB-D) Dataset based on 3ds Max, CARLA, and Unreal Engine 4. To verify SynerFill, we conduct experiments on the SCRGB-D dataset, DynaFill dataset, and SceneNet dataset. The experimental results show that SynerFill achieves state-of-the-art (SOTA) performance.