Open Challenges for Monocular Single-shot 6D Object Pose Estimation

TL;DR

This paper reviews recent advances in monocular single-shot 6D object pose estimation, highlighting current challenges like occlusion and domain shift, and suggests promising future research directions to further the field.

Contribution

It narrows the focus to single-shot monocular 6D pose estimation in robotics, reviews recent progress, and identifies key challenges and future research directions.

Findings

Methods can now overcome domain shift issues.

Occlusion handling remains a fundamental challenge.

Addressing novel objects and challenging materials is crucial for robotics.

Abstract

Object pose estimation is a non-trivial task that enables robotic manipulation, bin picking, augmented reality, and scene understanding, to name a few use cases. Monocular object pose estimation gained considerable momentum with the rise of high-performing deep learning-based solutions and is particularly interesting for the community since sensors are inexpensive and inference is fast. Prior works establish the comprehensive state of the art for diverse pose estimation problems. Their broad scopes make it difficult to identify promising future directions. We narrow down the scope to the problem of single-shot monocular 6D object pose estimation, which is commonly used in robotics, and thus are able to identify such trends. By reviewing recent publications in robotics and computer vision, the state of the art is established at the union of both fields. Following that, we identify promising research directions in order to help researchers to formulate relevant research ideas and effectively advance the state of the art. Findings include that methods are sophisticated enough to overcome the domain shift and that occlusion handling is a fundamental challenge. We also highlight problems such as novel object pose estimation and challenging materials handling as central challenges to advance robotics.