Self-supervised monocular depth prediction is a widely researched field that aims to provide a better scene understanding. However, most existing methods prioritize prediction accuracy over computation cost, which can hinder the deployment of these methods in real-world applications. Our objective is to propose a solution that efficiently compresses the depth map while maintaining a high level of accuracy for navigation purpose. The proposed method is an expansion of the work presented in N-QGN, which utilizes a quadtree representation for compression. This approach has already shown promising results, but we aim to improve it further by making it more accurate, faster, and easier to train. Therefore, we introduce a new method that directly predicts the quadtree structure, resulting in a more consistent prediction, and we revise the network architecture to be lighter and produce state-of-the-art accuracy results, depending on the data compression rate. The new implementation is also faster, making it more suitable for real-time applications. Experiments have been conducted on various scene configuration highlighting the capability of the method to efficiently predicting a reliable quadtree depth representation of the scene at low computation cost and high accuracy.

Monocular depth estimation has been a popu-lar area of research for several years, especially since self-supervised networks have shown increasingly good results in bridging the gap with supervised and stereo methods. However, these approaches focus their interest on dense 3D reconstruction and sometimes on tiny details that are superfluous for autonomous navigation. In this paper, we propose to address this issue by estimating the navigation map under a quad tree representation. The objective is to create an adaptive depth map prediction that only extract details that are essential for the obstacle avoidance. Other 3D space which leaves large room for navigation will be provided with approximate distance. Experiment on KITTI dataset shows that our method can significantly reduce the number of output information without major loss of accuracy.

Monocular depth estimation is a recurring subject in the field of computer vision. Its ability to describe scenes via a depth map while reducing the constraints related to the formulation of perspective geometry tends to favor its use. However, despite the constant improvement of algorithms, most methods exploit only colorimetric information. Consequently, robustness to events to which the modality is not sensitive to, like specularity or transparency, is neglected. In response to this phenomenon, we propose using polarimetry as an input for a self-supervised monodepth network. Therefore, we propose exploiting polarization cues to encourage accurate reconstruction of scenes. Furthermore, we include a term of polarimetric regularization to state-of-the-art method to take specific advantage of the data. Our method is evaluated both qualitatively and quantitatively demonstrating that the contribution of this new information as well as an enhanced loss function improves depth estimation results, especially for specular areas.