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We present DRACO, a method for Dense Reconstruction And Canonicalization ofObject 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 holdspromise for a multitude of robotic applications. Prior approaches either relyon painstakingly gathered dense 3D supervision, or produce only sparsecanonical representations, limiting real-world applicability. DRACO performsdense canonicalization using only weak supervision in the form of camera posesand semantic keypoints at train time. During inference, DRACO predicts denseobject-centric depth maps in a canonical coordinate-space, solely using one ormore RGB images of an object. Extensive experiments on canonical shapereconstruction and pose estimation show that DRACO is competitive or superiorto fully-supervised methods.

DRACO: Weakly Supervised Dense Reconstruction And Canonicalization of Objects