Deep Convolutional Neural Network for 6-DOF Image Localization
Daoyuan Jia, Yongchi Su, Chunping Li

TL;DR
This paper introduces a deep learning approach for precise 6-DOF image localization using CNN regression, leveraging synthesized data for improved accuracy in outdoor environments.
Contribution
It presents a novel method combining automatic photo synthesis from point clouds with CNN-based pose regression for 6-DOF localization.
Findings
Achieved within 1 meter and 1 degree accuracy on outdoor campus dataset.
Demonstrated robustness and accuracy of the CNN-based localization approach.
Utilized synthesized images to enhance training data quality.
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Neural Network Applications · Robotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques
