  We propose a novel method for 3D object reconstruction from a sparse set of
views captured from a 360-degree calibrated camera rig. We represent the object
surface through a hybrid model that uses both an MLP-based neural
representation and a triangle mesh. A key contribution in our work is a novel
object-centric sampling scheme of the neural representation, where rays are
shared among all views. This efficiently concentrates and reduces the number of
samples used to update the neural model at each iteration. This sampling scheme
relies on the mesh representation to ensure also that samples are
well-distributed along its normals. The rendering is then performed efficiently
by a differentiable renderer. We demonstrate that this sampling scheme results
in a more effective training of the neural representation, does not require the
additional supervision of segmentation masks, yields state of the art 3D
reconstructions, and works with sparse views on the Google's Scanned Objects,
Tank and Temples and MVMC Car datasets. Code available at:
https://github.com/llukmancerkezi/ROSTER
