A Neural Network Architecture for Accurate 4D Vehicle Pose Estimation from Monocular Images with Uncertainty Assessment

This paper proposes a new neural network architecture for estimating the four degrees of freedom poses of vehicles from monocular images in an uncontrolled environment. The neural network learns how to reconstruct 3D characteristic points of vehicles from image crops and coordinates of 2D keypoints estimated from these images. The 3D and 2D points are used to compute the vehicle pose solving the Perspective-n-Point problem, while the uncertainty is propagated by applying the Unscented Transform. Our network is trained and tested on the ApolloCar3D dataset, and we introduce a novel method to automatically obtain approximate labels for 3D points in this dataset. Our system outperforms state-of-the-art pose estimation methods on the ApolloCar3D dataset, and unlike competitors, it implements a full pipeline of uncertainty propagation.