  6D pose estimation is crucial for augmented reality, virtual reality, robotic
manipulation and visual navigation. However, the problem is challenging due to
the variety of objects in the real world. They have varying 3D shape and their
appearances in captured images are affected by sensor noise, changing lighting
conditions and occlusions between objects. Different pose estimation methods
have different strengths and weaknesses, depending on feature representations
and scene contents. At the same time, existing 3D datasets that are used for
data-driven methods to estimate 6D poses have limited view angles and low
resolution. To address these issues, we organize the Shape Retrieval Challenge
benchmark on 6D pose estimation and create a physically accurate simulator that
is able to generate photo-realistic color-and-depth image pairs with
corresponding ground truth 6D poses. From captured color and depth images, we
use this simulator to generate a 3D dataset which has 400 photo-realistic
synthesized color-and-depth image pairs with various view angles for training,
and another 100 captured and synthetic images for testing. Five research groups
register in this track and two of them submitted their results. Data-driven
methods are the current trend in 6D object pose estimation and our evaluation
results show that approaches which fully exploit the color and geometric
features are more robust for 6D pose estimation of reflective and texture-less
objects and occlusion. This benchmark and comparative evaluation results have
the potential to further enrich and boost the research of 6D object pose
estimation and its applications.
