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• W2951594310 abstract "We present a novel, end-to-end approach to align CAD models to an 3D scan of a scene, enabling transformation of a noisy, incomplete 3D scan to a compact, CAD reconstruction with clean, complete object geometry. Our main contribution lies in formulating a differentiable Procrustes alignment that is paired with a symmetry-aware dense object correspondence prediction. To simultaneously align CAD models to all the objects of a scanned scene, our approach detects object locations, then predicts symmetry-aware dense object correspondences between scan and CAD geometry in a unified object space, as well as a nearest neighbor CAD model, both of which are then used to inform a differentiable Procrustes alignment. Our approach operates in a fully-convolutional fashion, enabling alignment of CAD models to the objects of a scan in a single forward pass. This enables our method to outperform state-of-the-art approaches by $19.04%$ for CAD model alignment to scans, with $approx 250times$ faster runtime than previous data-driven approaches." @default.
• W2951594310 created "2019-06-27" @default.
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• W2951594310 date "2019-06-10" @default.
• W2951594310 modified "2023-09-27" @default.
• W2951594310 title "End-to-End CAD Model Retrieval and 9DoF Alignment in 3D Scans" @default.
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