  Object pose estimation has multiple important applications, such as robotic
grasping and augmented reality. We present a new method to estimate the 6D pose
of objects that improves upon the accuracy of current proposals and can still
be used in real-time. Our method uses RGB-D data as input to segment objects
and estimate their pose. It uses a neural network with multiple heads to
identify the objects in the scene, generate the appropriate masks and estimate
the values of the translation vectors and the quaternion that represents the
objects' rotation. These heads leverage a pyramid architecture used during
feature extraction and feature fusion. We conduct an empirical evaluation using
the two most common datasets in the area, and compare against state-of-the-art
approaches, illustrating the capabilities of MPF6D. Our method can be used in
real-time with its low inference time and high accuracy.
