  Extracting detailed 3D information of objects from video data is an important
goal for holistic scene understanding. While recent methods have shown
impressive results when reconstructing meshes of objects from a single image,
results often remain ambiguous as part of the object is unobserved. Moreover,
existing image-based datasets for mesh reconstruction don't permit to study
models which integrate temporal information. To alleviate both concerns we
present SAIL-VOS 3D: a synthetic video dataset with frame-by-frame mesh
annotations which extends SAIL-VOS. We also develop first baselines for
reconstruction of 3D meshes from video data via temporal models. We demonstrate
efficacy of the proposed baseline on SAIL-VOS 3D and Pix3D, showing that
temporal information improves reconstruction quality. Resources and additional
information are available at http://sailvos.web.illinois.edu.
