  MaskedFusion is a framework to estimate the 6D pose of objects using RGB-D
data, with an architecture that leverages multiple sub-tasks in a pipeline to
achieve accurate 6D poses. 6D pose estimation is an open challenge due to
complex world objects and many possible problems when capturing data from the
real world, e.g., occlusions, truncations, and noise in the data. Achieving
accurate 6D poses will improve results in other open problems like robot
grasping or positioning objects in augmented reality. MaskedFusion improves the
state-of-the-art by using object masks to eliminate non-relevant data. With the
inclusion of the masks on the neural network that estimates the 6D pose of an
object we also have features that represent the object shape. MaskedFusion is a
modular pipeline where each sub-task can have different methods that achieve
the objective. MaskedFusion achieved 97.3% on average using the ADD metric on
the LineMOD dataset and 93.3% using the ADD-S AUC metric on YCB-Video Dataset,
which is an improvement, compared to the state-of-the-art methods. The code is
available on GitHub (https://github.com/kroglice/MaskedFusion).
