Description: FS6D: Few-Shot 6D Pose Estimation of Novel Objects.
few-shot learning (3) 6d object pose estimation (1) synthesis dataset (1)
Given a few RGBD views of a novel objects with pose labels, the few-shot pose estimation network aims to estimate 6D pose of that object in a novel query scene without extra training. No precise CAD models are required as well.
6D object pose estimation networks are limited in their capability to scale to large numbers of object instances due to the close-set assumption and their reliance on high-fidelity object CAD models. In this work, we study a new open set problem; the few-shot 6D object poses estimation: estimating the 6D pose of an unknown object by a few support views without extra training. To tackle the problem, we point out the importance of fully exploring the appearance and geometric relationship between the given sup
We introduce a large-scale photorealistic dataset (ShapeNet6D) to empower the network's generalizability to novel objects. Rendered RGBD images are labeled with instance semantic segmentation and 6D pose parameters.