  Standard RGB-D trackers treat the target as an inherently 2D structure, which
makes modelling appearance changes related even to simple out-of-plane rotation
highly challenging. We address this limitation by proposing a novel long-term
RGB-D tracker - Object Tracking by Reconstruction (OTR). The tracker performs
online 3D target reconstruction to facilitate robust learning of a set of
view-specific discriminative correlation filters (DCFs). The 3D reconstruction
supports two performance-enhancing features: (i) generation of accurate spatial
support for constrained DCF learning from its 2D projection and (ii) point
cloud based estimation of 3D pose change for selection and storage of
view-specific DCFs which are used to robustly localize the target after
out-of-view rotation or heavy occlusion. Extensive evaluation of OTR on the
challenging Princeton RGB-D tracking and STC Benchmarks shows it outperforms
the state-of-the-art by a large margin.
