  We consider the problem of simultaneously estimating a dense depth map and
camera pose for a large set of images of an indoor scene. While classical SfM
pipelines rely on a two-step approach where cameras are first estimated using a
bundle adjustment in order to ground the ensuing multi-view stereo stage, both
our poses and dense reconstructions are a direct output of an altered bundle
adjuster. To this end, we parametrize each depth map with a linear combination
of a limited number of basis "depth-planes" predicted in a monocular fashion by
a deep net. Using a set of high-quality sparse keypoint matches, we optimize
over the per-frame linear combinations of depth planes and camera poses to form
a geometrically consistent cloud of keypoints. Although our bundle adjustment
only considers sparse keypoints, the inferred linear coefficients of the basis
planes immediately give us dense depth maps. RidgeSfM is able to collectively
align hundreds of frames, which is its main advantage over recent memory-heavy
deep alternatives that can align at most 10 frames. Quantitative comparisons
reveal performance superior to a state-of-the-art large-scale SfM pipeline.
