EfficientPose: An efficient, accurate and scalable end-to-end 6D multi object pose estimation approach
Yannick Bukschat, Marcus Vetter

TL;DR
EfficientPose is a fast, accurate, and scalable end-to-end method for 6D object pose estimation that detects multiple objects and their full poses simultaneously, outperforming previous approaches in accuracy and speed.
Contribution
The paper introduces EfficientPose, a novel single-shot approach for multi-object 6D pose estimation that combines detection and pose estimation efficiently, with a new augmentation technique for improved generalization.
Findings
Achieves 97.35% accuracy on Linemod dataset
Runs at over 27 FPS for single object detection
Handles multiple objects simultaneously with over 26 FPS
Abstract
In this paper we introduce EfficientPose, a new approach for 6D object pose estimation. Our method is highly accurate, efficient and scalable over a wide range of computational resources. Moreover, it can detect the 2D bounding box of multiple objects and instances as well as estimate their full 6D poses in a single shot. This eliminates the significant increase in runtime when dealing with multiple objects other approaches suffer from. These approaches aim to first detect 2D targets, e.g. keypoints, and solve a Perspective-n-Point problem for their 6D pose for each object afterwards. We also propose a novel augmentation method for direct 6D pose estimation approaches to improve performance and generalization, called 6D augmentation. Our approach achieves a new state-of-the-art accuracy of 97.35% in terms of the ADD(-S) metric on the widely-used 6D pose estimation benchmark dataset…
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Taxonomy
TopicsAdvanced Neural Network Applications · Robot Manipulation and Learning · Robotics and Sensor-Based Localization
