  We propose a single-shot approach to determining 6-DoF pose of an object with
available 3D computer-aided design (CAD) model from a single RGB image. Our
method, dubbed MRC-Net, comprises two stages. The first performs pose
classification and renders the 3D object in the classified pose. The second
stage performs regression to predict fine-grained residual pose within class.
Connecting the two stages is a novel multi-scale residual correlation (MRC)
layer that captures high-and-low level correspondences between the input image
and rendering from first stage. MRC-Net employs a Siamese network with shared
weights between both stages to learn embeddings for input and rendered images.
To mitigate ambiguity when predicting discrete pose class labels on symmetric
objects, we use soft probabilistic labels to define pose class in the first
stage. We demonstrate state-of-the-art accuracy, outperforming all competing
RGB-based methods on four challenging BOP benchmark datasets: T-LESS, LM-O,
YCB-V, and ITODD. Our method is non-iterative and requires no complex
post-processing.
