Recent years have seen the emergence of very effective ConvNet-based object detectors that have reconfigured the computer vision landscape. As a consequence, new approaches that propose object-based reasoning to solve traditional problems, such as camera pose estimation, have appeared. In particular, these methods have shown that modelling 3D objects by ellipsoids and 2D detections by ellipses offers a convenient manner to link 2D and 3D data. Following that promising direction, we propose here a novel object-based pose estimation algorithm that does not require any sensor but a RGB camera. Our method operates from at least two object detections, and is based on a new paradigm that enables to decrease the Degrees of Freedom (DoF) of the pose estimation problem from six to three, while two simplifying yet realistic assumptions reduce the remaining DoF to only one. Exhaustive search is performed over the unique unknown parameter to recover the full camera pose. Robust algorithms designed to deal with any number of objects as well as a refinement step are introduced. Effectiveness of the method has been assessed on the challenging T-LESS and Freiburg datasets.

中文翻译：

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Perspective-2-Ellipsoid：弥合物体检测和 6-DoF 相机姿势之间的差距

近年来，出现了非常有效的基于 ConvNet 的对象检测器，它们重新配置了计算机视觉领域。因此，出现了提出基于对象推理来解决传统问题（例如相机姿态估计）的新方法。特别是，这些方法已经表明，通过椭球对 3D 对象建模和通过椭圆进行 2D 检测提供了一种方便的方式来链接 2D 和 3D 数据。遵循这个有希望的方向，我们在这里提出了一种新颖的基于对象的姿态估计算法，它不需要任何传感器，只需要一个 RGB 相机。我们的方法从至少两个对象检测开始，并且基于一种新范式，该范式能够将姿势估计问题的自由度 (DoF) 从六个减少到三个，而两个简化但现实的假设将剩余的自由度减少到只有一个。对唯一的未知参数执行穷举搜索以恢复完整的相机姿势。引入了旨在处理任意数量对象以及细化步骤的稳健算法。已经在具有挑战性的 T-LESS 和弗莱堡数据集上评估了该方法的有效性。