Joint Depth and Surface Normals Prediction in Autonomous Driving

In this paper, we introduce a multitask learning architecture for simultaneous depth estimation and surface normals prediction, built upon a shared encoder framework. Our approach integrates well-established depth estimation models to reduce computational complexity while enhancing feature representation for both tasks. Through extensive experiments on benchmark datasets, we provide both quantitative and qualitative analyses of the results. Our evaluations demonstrate significant improvements in depth estimation accuracy and efficiency compared to other state-of-the-art methods, along with competitive results in surface normals prediction. Additionally, the proposed architecture offers a lightweight design for multitask learning, presenting a robust and versatile framework for future research and practical applications. These findings emphasize the potential of multitask learning to enhance the efficiency and performance of related computer vision tasks.