The primary focus of this paper is the development of a framework for pose
and size estimation of unseen objects given a single RGB image - all in
real-time. In 2019, the first category-level pose and size estimation framework
was proposed alongside two novel datasets called CAMERA and REAL. However,
current methodologies are restricted from practical use because of its long
inference time (2-4 fps). Their approach's inference had significant delays
because they used the computationally expensive MaskedRCNN framework and
Umeyama algorithm. To optimize our method and yield real-time results, our
framework uses the efficient ResNet-FPN framework alongside decoupling the
translation, rotation, and size regression problem by using distinct decoders.
Moreover, our methodology performs pose and size estimation in a global context
- i.e., estimating the involved parameters of all captured objects in the image
all at once. We perform extensive testing to fully compare the performance in
terms of precision and speed to demonstrate the capability of our method.

该研究论文的主要目标是开发一个在实时中给出单一 RGB 图像的看不见物体的姿态和尺寸估计框架。