Year: 2025
Type: article
Abstract: This paper proposes an end-to-end method for estimating camera poses using ray regression, a diffusion model-based ray inference approach. The conventional ray regression model outputs moments and directions, which are then converted into the final pose through traditional methods; however, this conversion process can introduce errors. In this work, we replace the conversion process with a deep learning network to achieve more stable pose estimation performance. Furthermore, the proposed model incorporates an additional rendering network for image reconstruction, demonstrating not only camera pose estimation but also the scalability to scene reconstruction. Leveraging the learned features, the model enables image rendering from novel viewpoints. Experimental results demonstrate that the proposed end-to-end method outperforms the conventional ray regression approach under the same training conditions, achieving approximately a 16% improvement in camera pose estimation and a nearly 30% gain in translation accuracy.
Source: Electronics
Authors: Jin-Woo Kim, Jong-Eun Ha
Institution: Seoul National University of Science and Technology
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G5860474638
OpenAlex ID: W4416672875