YOLOPose: Transformer-based Multi-Object 6D Pose Estimation using Keypoint Regression. (arXiv:2205.02536v1 [cs.CV])

Web: http://arxiv.org/abs/2205.02536

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 …

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