  6D object pose estimation has been a research topic in the field of computer
vision and robotics. Many modern world applications like robot grasping,
manipulation, autonomous navigation etc, require the correct pose of objects
present in a scene to perform their specific task. It becomes even harder when
the objects are placed in a cluttered scene and the level of occlusion is high.
Prior works have tried to overcome this problem but could not achieve accuracy
that can be considered reliable in real-world applications. In this paper, we
present an architecture that, unlike prior work, is context-aware. It utilizes
the context information available to us about the objects. Our proposed
architecture treats the objects separately according to their types i.e;
symmetric and non-symmetric. A deeper estimator and refiner network pair is
used for non-symmetric objects as compared to symmetric due to their intrinsic
differences. Our experiments show an enhancement in the accuracy of about 3.2%
over the LineMOD dataset, which is considered a benchmark for pose estimation
in the occluded and cluttered scenes, against the prior state-of-the-art
DenseFusion. Our results also show that the inference time we got is sufficient
for real-time usage.
