Structure from Duplicates: Neural Inverse Graphics from a Pile of
  Objects
Tianhang Cheng Wei-Chiu Ma Kaiyu Guan Antonio Torralba Shenlong Wang
Our world is full of identical objects (\emphe.g., cans of coke, cars of same
model). These duplicates, when seen together, provide additional and strong
cues for us to effectively reason about 3D. Inspired by this observation, we
introduce Structure from Duplicates (SfD), a novel inverse graphics framework
that reconstructs geometry, material, and illumination from a single image
containing multiple identical objects. SfD begins by identifying multiple
instances of an object within an image, and then jointly estimates the 6DoF
pose for all instances.An inverse graphics pipeline is subsequently employed to
jointly reason about the shape, material of the object, and the environment
light, while adhering to the shared geometry and material constraint across
instances. Our primary contributions involve utilizing object duplicates as a
robust prior for single-image inverse graphics and proposing an in-plane
rotation-robust Structure from Motion (SfM) formulation for joint 6-DoF object
pose estimation. By leveraging multi-view cues from a single image, SfD
generates more realistic and detailed 3D reconstructions, significantly
outperforming existing single image reconstruction models and multi-view
reconstruction approaches with a similar or greater number of observations.