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Modern deep learning techniques that regress the relative camera pose betweentwo images have difficulty dealing with challenging scenarios, such as largecamera motions resulting in occlusions and significant changes in perspectivethat leave little overlap between images. These models continue to struggleeven with the benefit of large supervised training datasets. To address thelimitations of these models, we take inspiration from techniques that showregressing keypoint locations in 2D and 3D can be improved by estimating adiscrete distribution over keypoint locations. Analogously, in this paper weexplore improving camera pose regression by instead predicting a discretedistribution over camera poses. To realize this idea, we introduceDirectionNet, which estimates discrete distributions over the 5D relative posespace using a novel parameterization to make the estimation problem tractable.Specifically, DirectionNet factorizes relative camera pose, specified by a 3Drotation and a translation direction, into a set of 3D direction vectors. Since3D directions can be identified with points on the sphere, DirectionNetestimates discrete distributions on the sphere as its output. We evaluate ourmodel on challenging synthetic and real pose estimation datasets constructedfrom Matterport3D and InteriorNet. Promising results show a near 50% reductionin error over direct regression methods.

Wide-Baseline Relative Camera Pose Estimation with Directional Learning