Estimating the 6D pose of objects accurately, quickly, and robustly remains a
difficult task. However, recent methods for directly regressing poses from RGB
images using dense features have achieved state-of-the-art results. Stereo
vision, which provides an additional perspective on the object, can help reduce
pose ambiguity and occlusion. Moreover, stereo can directly infer the distance
of an object, while mono-vision requires internalized knowledge of the object's
size. To extend the state-of-the-art in 6D object pose estimation to stereo, we
created a BOP compatible stereo version of the YCB-V dataset. Our method
outperforms state-of-the-art 6D pose estimation algorithms by utilizing stereo
vision and can easily be adopted for other dense feature-based algorithms.

使用稠密特征从 RGB 图像中直接回归物体的 6D 姿态是一个困难的任务，但最近的方法通过利用稠密特征在直接回归姿态方面取得了最先进的结果。使用立体视觉可以减少姿态的模糊性和遮挡，并且可以直接推断物体的距离。为了扩展立体的 6D 物体姿态估计到最新技术，我们创建了与 YCB-V 数据集兼容的立体版本。我们的方法利用立体视觉优于最先进的 6D 姿态估计算法，并且可以容易地用于其他基于稠密特征的算法。