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6D pose estimation of textureless objects is a valuable but challenging taskfor many robotic applications. In this work, we propose a framework to addressthis challenge using only RGB images acquired from multiple viewpoints. Thecore idea of our approach is to decouple 6D pose estimation into a sequentialtwo-step process, first estimating the 3D translation and then the 3D rotationof each object. This decoupled formulation first resolves the scale and depthambiguities in single RGB images, and uses these estimates to accuratelyidentify the object orientation in the second stage, which is greatlysimplified with an accurate scale estimate. Moreover, to accommodate themulti-modal distribution present in rotation space, we develop an optimizationscheme that explicitly handles object symmetries and counteracts measurementuncertainties. In comparison to the state-of-the-art multi-view approach, wedemonstrate that the proposed approach achieves substantial improvements on achallenging 6D pose estimation dataset for textureless objects.

6D Pose Estimation for Textureless Objects on RGB Frames using Multi-View Optimization