Depth Estimation for Indoor Panoramas through Merging Multiple Perspective Monocular Depth Predictions
We propose a method to estimate depths for indoor panoramas by merging multiple perspective depth maps produced by modern monocular depth estimation methods such as LeReS. The challenge is that the perspective depth maps, each covering a different subset of the panorama, tend to have different scales and shifts of the predicted depth values. Simply stitching them together led to inconsistent depth values for the whole panorama with visible seams. To address the challenge, we propose a novel approach to solve a single depth map for the whole panorama with information taken from each perspective depth estimations and a common panoramic depth map served as the reference for merging.
