ViPNet: An End-to-End 6D Visual Camera Pose Regression Network

Abstract

In this work, we present a visual pose regression network: ViPNet. It is robust and real-time capable on mobile platforms such as self-driving vehicles. We train a convolutional neural network to estimate the six degrees of freedom camera pose from a single monocular image in an end-to-end manner. In order to estimate camera poses with uncertainty, we use a Bayesian version of the ResNet-50 as our basic network. SEBlocks are applied in residual units to increase our model’s sensitivity to informative features. Our ViPNet is trained using a geometric loss function with trainable parameters, which can simplify the fine-tuning process significantly. We evaluate our ViPNet on the Cambridge Landmarks dataset and also on our Karl-Wilhelm-Plaza dataset, which is recorded with an experimental vehicle. As evaluation results, our ViPNet outperforms other end-to-end monocular camera pose estimation methods. Our ViPNet requires only 9-15ms to predict one camera pose, which allows us to run it with a very high frequency.