Inference Analysis of Lightweight CNNs for Monocular Depth Estimation

Abstract

Monocular Depth Estimation (MDE) from a single image is a challenging problem in computer vision, which has been intensively investigated in the last decade using deep learning approaches. It is essential in developing cutting-edge applications like self-driving cars and augmented reality. The ability to perceive depth is essential for various tasks, including navigation and perception. Monocular depth estimation has attracted much attention. Their popularity is driven by ease of use, lower cost, ubiquitous, and denser imaging compared to other methods such as LiDAR scanners. Traditional MDE approaches heavily rely on depth cues for depth estimation and are subject to strict constraints, such as shape-from-focus and defocus algorithms, which require a low depth of field of scenes and images. MDE without some particular environmental assumptions is an ill-posed problem due to the ambiguity of mapping between the depth and intensity of color measurements. Recently, Convolutional Neural Networks (CNN) approaches have demonstrated encouraging outcomes in addressing this challenge. CNN can learn an implicit relationship between color pixels and depth. However, the mechanism and process behind the depth inference of a CNN from a single image are relatively unknown. In many applications, interpretability is very important. To address this problem, this paper tries to visualize a lightweight CNN (Fast-depth) inference in monocular depth estimation. The proposed method is based on [1], with some modifications and more analyses of the results on outdoor scenes. This method detects the smallest number of image pixels (mask) critical to infer the depth from a single image through an optimization problem. This small subset of image pixels can be used to find patterns and features that can help us to better formulate the behavior of the CNN for any future monocular depth estimation tasks.