Context-aware 6D Pose Estimation of Known Objects using RGB-D data

• URL: http://arxiv.org/abs/2212.05560v1
• Date: Sun, 11 Dec 2022 18:01:01 GMT
• Title: Context-aware 6D Pose Estimation of Known Objects using RGB-D data
• Authors: Ankit Kumar, Priya Shukla, Vandana Kushwaha and G.C. Nandi
• Abstract summary: 6D object pose estimation has been a research topic in the field of computer vision and robotics. We present an architecture that, unlike prior work, is context-aware. Our experiments show an enhancement in the accuracy of about 3.2% over the LineMOD dataset.
• Score: 3.48122098223937
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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