  Learning-based 3D object reconstruction enables single- or few-shot
estimation of 3D object models. For robotics, this holds the potential to allow
model-based methods to rapidly adapt to novel objects and scenes. Existing 3D
reconstruction techniques optimize for visual reconstruction fidelity,
typically measured by chamfer distance or voxel IOU. We find that when applied
to realistic, cluttered robotics environments, these systems produce
reconstructions with low physical realism, resulting in poor task performance
when used for model-based control. We propose ARM, an amodal 3D reconstruction
system that introduces (1) a stability prior over object shapes, (2) a
connectivity prior, and (3) a multi-channel input representation that allows
for reasoning over relationships between groups of objects. By using these
priors over the physical properties of objects, our system improves
reconstruction quality not just by standard visual metrics, but also
performance of model-based control on a variety of robotics manipulation tasks
in challenging, cluttered environments. Code is available at
github.com/wagnew3/ARM.
