Estimating the 6D pose of textureless objects from RBG images is an important problem in robotics. Due to appearance ambiguities, rotational symmetries, and severe occlusions, single-view based 6D pose estimators are still unable to handle a wide range of objects, motivating research towards multi-view pose estimation and next-best-view prediction that addresses these limitations. In this work, we propose a comprehensive active perception framework for estimating the 6D poses of textureless objects using only RGB images. Our approach is built upon a key idea: decoupling the 6D pose estimation into a sequential two-step process can greatly improve both accuracy and efficiency. First, we estimate the 3D translation of each object, resolving scale and depth ambiguities inherent to RGB images. These estimates are then used to simplify the subsequent task of determining the 3D orientation, which we achieve through canonical scale template matching. Building on this formulation, we then introduce an active perception strategy that predicts the next best camera viewpoint to capture an RGB image, effectively reducing object pose uncertainty and enhancing pose accuracy. We evaluate our method on the public ROBI dataset as well as on a transparent object dataset that we created. When evaluated using the same camera viewpoints, our multi-view pose estimation significantly outperforms state-of-the-art approaches. Furthermore, by leveraging our next-best-view strategy, our method achieves high object pose accuracy with substantially fewer viewpoints than heuristic-based policies.

6D pose estimation of textureless objects is a valuable but challenging task for many robotic applications. In this work, we propose a framework to address this challenge using only RGB images acquired from multiple viewpoints. The core idea of our approach is to decouple 6D pose estimation into a sequential two-step process, first estimating the 3D translation and then the 3D rotation of each object. This decoupled formulation first resolves the scale and depth ambiguities in single RGB images, and uses these estimates to accurately identify the object orientation in the second stage, which is greatly simplified with an accurate scale estimate. Moreover, to accommodate the multi-modal distribution present in rotation space, we develop an optimization scheme that explicitly handles object symmetries and counteracts measurement uncertainties. In comparison to the state-of-the-art multi-view approach, we demonstrate that the proposed approach achieves substantial improvements on a challenging 6D pose estimation dataset for textureless objects.