  We present GTT-Net, a supervised learning framework for the reconstruction of
sparse dynamic 3D geometry. We build on a graph-theoretic formulation of the
generalized trajectory triangulation problem, where non-concurrent multi-view
imaging geometry is known but global image sequencing is not provided. GTT-Net
learns pairwise affinities modeling the spatio-temporal relationships among our
input observations and leverages them to determine 3D geometry estimates.
Experiments reconstructing 3D motion-capture sequences show GTT-Net outperforms
the state of the art in terms of accuracy and robustness. Within the context of
articulated motion reconstruction, our proposed architecture is 1) able to
learn and enforce semantic 3D motion priors for shared training and test
domains, while being 2) able to generalize its performance across different
training and test domains. Moreover, GTT-Net provides a computationally
streamlined framework for trajectory triangulation with applications to
multi-instance reconstruction and event segmentation.
