YOLOPose V2: Understanding and Improving Transformer-based 6D Pose Estimation

• URL: http://arxiv.org/abs/2307.11550v1
• Date: Fri, 21 Jul 2023 12:53:54 GMT
• Title: YOLOPose V2: Understanding and Improving Transformer-based 6D Pose Estimation
• Authors: Arul Selvam Periyasamy, Arash Amini, Vladimir Tsaturyan, and Sven Behnke
• Abstract summary: YOLOPose is a Transformer-based multi-object 6D pose estimation method. We employ a learnable orientation estimation module to predict the orientation from the keypoints. Our method is suitable for real-time applications and achieves results comparable to state-of-the-art methods.
• Score: 36.067414358144816
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications. The state-of-the-art models for pose estimation are convolutional neural network (CNN)-based. Lately, Transformers, an architecture originally proposed for natural language processing, is achieving state-of-the-art results in many computer vision tasks as well. Equipped with the multi-head self-attention mechanism, Transformers enable simple single-stage end-to-end architectures for learning object detection and 6D object pose estimation jointly. In this work, we propose YOLOPose (short form for You Only Look Once Pose estimation), a Transformer-based multi-object 6D pose estimation method based on keypoint regression and an improved variant of the YOLOPose model. In contrast to the standard heatmaps for predicting keypoints in an image, we directly regress the keypoints. Additionally, we employ a learnable orientation estimation module to predict the orientation from the keypoints. Along with a separate translation estimation module, our model is end-to-end differentiable. Our method is suitable for real-time applications and achieves results comparable to state-of-the-art methods. We analyze the role of object queries in our architecture and reveal that the object queries specialize in detecting objects in specific image regions. Furthermore, we quantify the accuracy trade-off of using datasets of smaller sizes to train our model.

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