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

TL;DR

This paper introduces a context-aware architecture for 6D object pose estimation from RGB-D data, improving accuracy in cluttered scenes with occlusion, suitable for real-time applications.

Contribution

It proposes a novel architecture that leverages object context and differentiates between symmetric and non-symmetric objects for improved pose estimation accuracy.

Findings

Achieved 3.2% higher accuracy on LineMOD dataset compared to DenseFusion.

Demonstrated real-time inference capability.

Effective handling of occlusion and clutter in pose estimation.

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.