ROCA: Robust CAD Model Retrieval and Alignment from a Single Image

• URL: http://arxiv.org/abs/2112.01988v1
• Date: Fri, 3 Dec 2021 16:02:32 GMT
• Title: ROCA: Robust CAD Model Retrieval and Alignment from a Single Image
• Authors: Can G\"umeli, Angela Dai, Matthias Nie{\ss}ner
• Abstract summary: We present ROCA, a novel end-to-end approach that retrieves and aligns 3D CAD models from a shape database to a single input image. experiments on challenging, real-world imagery from ScanNet show that ROCA significantly improves on state of the art, from 9.5% to 17.6% in retrieval-aware CAD alignment accuracy.
• Score: 22.03752392397363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present ROCA, a novel end-to-end approach that retrieves and aligns 3D CAD models from a shape database to a single input image. This enables 3D perception of an observed scene from a 2D RGB observation, characterized as a lightweight, compact, clean CAD representation. Core to our approach is our differentiable alignment optimization based on dense 2D-3D object correspondences and Procrustes alignment. ROCA can thus provide a robust CAD alignment while simultaneously informing CAD retrieval by leveraging the 2D-3D correspondences to learn geometrically similar CAD models. Experiments on challenging, real-world imagery from ScanNet show that ROCA significantly improves on state of the art, from 9.5% to 17.6% in retrieval-aware CAD alignment accuracy.

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