Self-supervised Monocular Depth Estimation on Unseen Synthetic Cameras

Cecilia Diana-Albelda, Juan Ignacio Bravo Pérez-Villar, Javier Montalvo, Álvaro García-Martín, Jesús Bescós Cano

Abstract: Monocular depth estimation is a critical task in computer vision, and self-supervised deep learning methods have achieved remarkable results in recent years. However, these models often struggle on camera generalization, i.e. at sequences captured by unseen cameras. To address this challenge, we present a new public custom dataset created using the CARLA simulator [4], consisting of three video sequences recorded by five different cameras with varying focal distances. This dataset has been created due to the absence of public datasets containing identical sequences captured by different cameras. Additionally, it is proposed in this paper the use of adversarial training to improve the models’ robustness to intrinsic camera parameter changes, enabling accurate depth estimation regardless of the recording camera. The results of our proposed architecture are compared with a baseline model, hence being evaluated the effectiveness of adversarial training and demonstrating its potential benefits both on our synthetic dataset and on the KITTI benchmark [8] as the reference dataset to evaluate depth estimation.

External IDs:dblp:conf/ciarp/DianaAlbeldaPMGC23