FAR: Flexible, Accurate and Robust 6DoF Relative Camera Pose Estimation
Chris Rockwell Nilesh Kulkarni Linyi Jin Jeong Joon Park Justin Johnson David F. Fouhey
Estimating relative camera poses between images has been a central problem in
computer vision. Methods that find correspondences and solve for the
fundamental matrix offer high precision in most cases. Conversely, methods
predicting pose directly using neural networks are more robust to limited
overlap and can infer absolute translation scale, but at the expense of reduced
precision. We show how to combine the best of both methods; our approach yields
results that are both precise and robust, while also accurately inferring
translation scales. At the heart of our model lies a Transformer that (1)
learns to balance between solved and learned pose estimations, and (2) provides
a prior to guide a solver. A comprehensive analysis supports our design choices
and demonstrates that our method adapts flexibly to various feature extractors
and correspondence estimators, showing state-of-the-art performance in 6DoF
pose estimation on Matterport3D, InteriorNet, StreetLearn, and Map-free
Relocalization.