Towards Camera Parameters Invariant Monocular Depth Estimation in Autonomous Driving

Abstract

Monocular depth estimation is an effective approach to environment perception due to simplicity of the sensor setup and absence of multisensor calibration. Deep learning has enabled accurate depth estimation from a single image by exploiting semantic cues such as the sizes of known objects and positions on the ground plane thereof. However, learning-based methods frequently fail to generalize on images collected with different vehicle-camera setups due to the induced perspective geometry bias. In this work, we propose an approach for camera parameters invariant depth estimation in autonomous driving scenarios. We propose a novel joint parametrization of camera intrinsic and extrinsic parameters specifically designed for autonomous driving. In order to supplement the neural network with information about the camera parameters, we fuse the proposed parametrization and image features via the novel module based on a self-attention mechanism. After thorough experimentation on the effects of camera parameter variation, we show that our approach effectively provides the neural network with useful information, thus increasing accuracy and generalization performance.