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.

基于 Transformer 的 YOLOPose 模型通过关键点回归和改进的方式实现多目标 6D 姿态估计，适用于实时应用，并达到与最先进方法相媲美的效果。

YOLOPose V2：基于 Transformer 的 6D 姿态估计的理解与改进

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

The typical bottom-up human pose estimation framework includes two stages,
keypoint detection and grouping. Most existing works focus on developing
grouping algorithms, e.g., associative embedding, and pixel-wise keypoint
regression that we adopt in our approach. We present several schemes that are
rarely or unthoroughly studied before for improving keypoint detection and
grouping (keypoint regression) performance. First, we exploit the keypoint
heatmaps for pixel-wise keypoint regression instead of separating them for
improving keypoint regression. Second, we adopt a pixel-wise spatial
transformer network to learn adaptive representations for handling the scale
and orientation variance to further improve keypoint regression quality. Last,
we present a joint shape and heatvalue scoring scheme to promote the estimated
poses that are more likely to be true poses. Together with the tradeoff heatmap
estimation loss for balancing the background and keypoint pixels and thus
improving heatmap estimation quality, we get the state-of-the-art bottom-up
human pose estimation result. Code is available at
this https URL

本文介绍了一种基于向下拼接的人体姿态估计框架，其中通过关键点检测和聚类两个阶段进行处理。在该框架下，本文提出了多种方案，包括使用关键点热图进行像素级别的关键点回归、采用像素级别空间变换网络学习适应性表示处理尺度和方向差异并提高关键点回归质量，以及使用联合形状和热值评分方案提升预测姿态的可靠性，最终获得了最先进的人体底部向上姿态估计结果。