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arXiv · 2011.12912

DRACO: Weakly Supervised Dense Reconstruction And Canonicalization of Objects

Rahul Sajnani·AadilMehdi Sanchawala·Krishna Murthy Jatavallabhula·Srinath Sridhar·K. Madhava Krishna
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Abstract

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.

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Rahul Sajnani, AadilMehdi Sanchawala, Krishna Murthy Jatavallabhula, Srinath Sridhar, K. Madhava Krishna. 2020-11-25. DRACO: Weakly Supervised Dense Reconstruction And Canonicalization of Objects. https://arxiv.org/abs/2011.12912

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