IFFNeRF: Initialisation Free and Fast 6DoF pose estimation from a single image and a NeRF model

Bortolon, Matteo; Tsesmelis, Theodore; James, Stuart; Poiesi, Fabio; Bue, Alessio Del

Abstract

We introduce IFFNeRF to estimate the six degrees-of-freedom (6DoF) camera pose of a given image, building on the Neural Radiance Fields (NeRF) formulation. IFFNeRF is specifically designed to operate in real-time and eliminates the need for an initial pose guess that is proximate to the sought solution. IFFNeRF utilizes the Metropolis-Hasting algorithm to sample surface points from within the NeRF model. From these sampled points, we cast rays and deduce the color for each ray through pixel-level view synthesis. The camera pose can then be estimated as the solution to a Least Squares problem by selecting correspondences between the query image and the resulting bundle. We facilitate this process through a learned attention mechanism, bridging the query image embedding with the embedding of parameterized rays, thereby matching rays pertinent to the image. Through synthetic and real evaluation settings, we show that our method can improve the angular and translation error accuracy by 80.1% and 67.3%, respectively, compared to iNeRF while performing at 34fps on consumer hardware and not requiring the initial pose guess. Project page: https://mbortolon97.github.io/frenerf/

Citation

Bortolon, M., Tsesmelis, T., James, S., Poiesi, F., & Bue, A. D. (2024, May). IFFNeRF: Initialisation Free and Fast 6DoF pose estimation from a single image and a NeRF model. Presented at 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 IEEE International Conference on Robotics and Automation (ICRA)
• Start Date: May 13, 2024
• End Date: May 17, 2024
• Acceptance Date: Jan 29, 2024
• Publication Date: May 13, 2024
• Deposit Date: Sep 27, 2024
• Publicly Available Date: Oct 1, 2024
• Peer Reviewed: Peer Reviewed
• Volume: 21
• Pages: 1985-1991
• Book Title: 2024 IEEE International Conference on Robotics and Automation (ICRA)
• ISBN: 9798350384581
• DOI: https://doi.org/10.1109/icra57147.2024.10610425
• Public URL: https://durham-repository.worktribe.com/output/2880027