We present a novel approach for model-based 6D pose refinement in color data.
Building on the established idea of contour-based pose tracking, we teach a
deep neural network to predict a translational and rotational update. At the
core, we propose a new visual loss that drives the pose update by aligning
object contours, thus avoiding the definition of any explicit appearance model.
In contrast to previous work our method is correspondence-free,
segmentation-free, can handle occlusion and is agnostic to geometrical symmetry
as well as visual ambiguities. Additionally, we observe a strong robustness
towards rough initialization. The approach can run in real-time and produces
pose accuracies that come close to 3D ICP without the need for depth data.
Furthermore, our networks are trained from purely synthetic data and will be
published together with the refinement code to ensure reproducibility.

本研究提出了一种基于深度神经网络的视觉损失函数，通过对凸形物体外轮廓的对齐来驱动位姿更新进行模型为 6D 姿态精修的处理，方法无需确定外观模型、免除了手动图像分割、同时能够处理遮挡问题和几何不对称性与视觉模糊问题，并且能够在没有深度数据的情况下准确地估算物体位姿。