论文标题
使用一些混乱的图像进行6D姿势估算的神经对象学习
Neural Object Learning for 6D Pose Estimation Using a Few Cluttered Images
论文作者
论文摘要
6D姿势估计对象的最新方法假设构成纹理的3D模型或涵盖整个目标姿势范围的真实图像。但是，很难获得纹理的3D模型并在实际场景中注释对象的姿势。本文提出了一种方法，即神经对象学习（NOL），该方法通过仅结合杂物图像中的几个观察结果来创建对象的合成图像。提出了一个新颖的改进步骤，以使源图像中对象的不准确姿势对齐，从而导致质量更好的图像。在两个公共数据集上执行的评估表明，与使用真实图像数量的13倍的方法相比，NOL创建的渲染图像导致最先进的性能。我们的新数据集中的评估显示，可以使用固定场景的序列同时训练和识别多个对象。
Recent methods for 6D pose estimation of objects assume either textured 3D models or real images that cover the entire range of target poses. However, it is difficult to obtain textured 3D models and annotate the poses of objects in real scenarios. This paper proposes a method, Neural Object Learning (NOL), that creates synthetic images of objects in arbitrary poses by combining only a few observations from cluttered images. A novel refinement step is proposed to align inaccurate poses of objects in source images, which results in better quality images. Evaluations performed on two public datasets show that the rendered images created by NOL lead to state-of-the-art performance in comparison to methods that use 13 times the number of real images. Evaluations on our new dataset show multiple objects can be trained and recognized simultaneously using a sequence of a fixed scene.
