Deep Convolutional Neural Network for 6-DOF Image Localization
This addresses the problem of precise image localization for applications like robotics or augmented reality, but it appears incremental as it builds on existing deep learning methods for pose estimation.
The paper tackles 6-DOF image localization by using deep convolutional neural networks to regress camera poses from images, achieving an accuracy within 1 meter and 1 degree on an outdoor dataset covering about 2 acres.
We present an accurate and robust method for six degree of freedom image localization. There are two key-points of our method, 1. automatic immense photo synthesis and labeling from point cloud model and, 2. pose estimation with deep convolutional neural networks regression. Our model can directly regresses 6-DOF camera poses from images, accurately describing where and how it was captured. We achieved an accuracy within 1 meters and 1 degree on our out-door dataset, which covers about 2 acres on our school campus.