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.
翻译:对象的估测是一项非三重任务,它能够使机器人操作、垃圾选取、增强现实和场景理解成为几个使用案例。单体物体的估测随着高效深层次学习解决方案的兴起而获得相当大的动力,对于社区来说特别有趣,因为传感器价格低廉,推推推很快。先前的工作为各种估测问题建立了全面的先进水平。其范围广泛,难以确定有希望的未来方向。我们缩小了单发单筒单筒单筒6D物体的估测范围,机器人通常使用这种估计,因此能够辨别这种趋势。我们通过审查机器人和计算机视觉的最新出版物,在这两个领域的联合中确立了艺术状态。随后,我们确定了有希望的研究方向,以帮助研究人员制定相关的研究构想,有效推进艺术状态。研究结果包括方法十分复杂,足以克服域变,隔离处理是一项基本挑战。我们还着重指出了诸如新式物体作为推进机器人的核心挑战而构成估测和质疑材料处理的问题。