6D object pose estimation is a prerequisite for many applications. In recent
years, monocular pose estimation has attracted much research interest because
it does not need depth measurements. In this work, we introduce ConvPoseCNN, a
fully convolutional architecture that avoids cutting out individual objects.
Instead we propose pixel-wise, dense prediction of both translation and
orientation components of the object pose, where the dense orientation is
represented in Quaternion form. We present different approaches for aggregation
of the dense orientation predictions, including averaging and clustering
schemes. We evaluate ConvPoseCNN on the challenging YCB-Video Dataset, where we
show that the approach has far fewer parameters and trains faster than
comparable methods without sacrificing accuracy. Furthermore, our results
indicate that the dense orientation prediction implicitly learns to attend to
trustworthy, occlusion-free, and feature-rich object regions.

该研究介绍了 ConvPoseCNN，它是一种全卷积架构，能够实现对象位姿的像素级密集预测，包括翻译和方向组件，其中密集方向以四元数形式表示，并使用不同方法对密集方向预测进行聚合，该方法与比较方法相比具有更少的参数和更快的训练速度，而且能够自动学习信任、无遮挡和特征丰富的对象区域。