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We present a data-driven method for building dense 3D reconstructions
using a combination of recognition and multi-view cues. Our approach
is based on the idea that there are image patches that are so
distinctive that we can accurately estimate their latent 3D shapes
solely using recognition. We call these patches shape anchors, and we
use them as the basis of a multi-view reconstruction system that
transfers dense, complex geometry between scenes. We "anchor" our 3D
interpretation from these patches, using them to predict geometry for
parts of the scene that are relatively ambiguous. The resulting
algorithm produces dense reconstructions from stereo point clouds that
are sparse and noisy, and we demonstrate it on a challenging dataset
of real-world, indoor scenes.
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