  In this paper, we introduce a novel RGB-D based relative pose estimation
approach that is suitable for small-overlapping or non-overlapping scans and
can output multiple relative poses. Our method performs scene completion and
matches the completed scans. However, instead of using a fixed representation
for completion, the key idea is to utilize hybrid representations that combine
360-image, 2D image-based layout, and planar patches. This approach offers
adaptively feature representations for relative pose estimation. Besides, we
introduce a global-2-local matching procedure, which utilizes initial relative
poses obtained during the global phase to detect and then integrate geometric
relations for pose refinement. Experimental results justify the potential of
this approach across a wide range of benchmark datasets. For example, on
ScanNet, the rotation translation errors of the top-1/top-5 predictions of our
approach are 28.6/0.90m and 16.8/0.76m, respectively. Our approach also
considerably boosts the performance of multi-scan reconstruction in few-view
reconstruction settings.
