PrimA6D: Rotational Primitive Reconstruction for Enhanced and Robust 6D Pose Estimation

• URL: http://arxiv.org/abs/2006.07789v2
• Date: Fri, 3 Jul 2020 10:39:13 GMT
• Title: PrimA6D: Rotational Primitive Reconstruction for Enhanced and Robust 6D Pose Estimation
• Authors: Myung-Hwan Jeon and Ayoung Kim
• Abstract summary: We introduce a rotational primitive prediction based 6D object pose estimation using a single image as an input. We leverage a Variational AutoEncoder (VAE) to learn this underlying primitive and its associated keypoints. When evaluated over public datasets, our method yields a notable improvement over LINEMOD, Occlusion LINEMOD, and the Y-induced dataset.
• Score: 11.873744190924599
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce a rotational primitive prediction based 6D object pose estimation using a single image as an input. We solve for the 6D object pose of a known object relative to the camera using a single image with occlusion. Many recent state-of-the-art (SOTA) two-step approaches have exploited image keypoints extraction followed by PnP regression for pose estimation. Instead of relying on bounding box or keypoints on the object, we propose to learn orientation-induced primitive so as to achieve the pose estimation accuracy regardless of the object size. We leverage a Variational AutoEncoder (VAE) to learn this underlying primitive and its associated keypoints. The keypoints inferred from the reconstructed primitive image are then used to regress the rotation using PnP. Lastly, we compute the translation in a separate localization module to complete the entire 6D pose estimation. When evaluated over public datasets, the proposed method yields a notable improvement over the LINEMOD, the Occlusion LINEMOD, and the YCB-Video dataset. We further provide a synthetic-only trained case presenting comparable performance to the existing methods which require real images in the training phase.

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