  We present DRACO, a method for Dense Reconstruction And Canonicalization of
Object shape from one or more RGB images. Canonical shape reconstruction,
estimating 3D object shape in a coordinate space canonicalized for scale,
rotation, and translation parameters, is an emerging paradigm that holds
promise for a multitude of robotic applications. Prior approaches either rely
on painstakingly gathered dense 3D supervision, or produce only sparse
canonical representations, limiting real-world applicability. DRACO performs
dense canonicalization using only weak supervision in the form of camera poses
and semantic keypoints at train time. During inference, DRACO predicts dense
object-centric depth maps in a canonical coordinate-space, solely using one or
more RGB images of an object. Extensive experiments on canonical shape
reconstruction and pose estimation show that DRACO is competitive or superior
to fully-supervised methods.
