We propose a three-stage 6 DoF object detection method called DPODv2 (Dense
Pose Object Detector) that relies on dense correspondences. We combine a 2D
object detector with a dense correspondence estimation network and a multi-view
pose refinement method to estimate a full 6 DoF pose. Unlike other deep
learning methods that are typically restricted to monocular RGB images, we
propose a unified deep learning network allowing different imaging modalities
to be used (RGB or Depth). Moreover, we propose a novel pose refinement method,
that is based on differentiable rendering. The main concept is to compare
predicted and rendered correspondences in multiple views to obtain a pose which
is consistent with predicted correspondences in all views. Our proposed method
is evaluated rigorously on different data modalities and types of training data
in a controlled setup. The main conclusions is that RGB excels in
correspondence estimation, while depth contributes to the pose accuracy if good
3D-3D correspondences are available. Naturally, their combination achieves the
overall best performance. We perform an extensive evaluation and an ablation
study to analyze and validate the results on several challenging datasets.
DPODv2 achieves excellent results on all of them while still remaining fast and
scalable independent of the used data modality and the type of training data

本文提出了一种基于密集对应的三阶段六自由度物体检测方法，称为 DPODv2（密集姿势物体检测器），结合了 2D 物体检测与密集对应估计网络以及多视图姿态细化方法来估计完整的 6 自由度姿态，其使用了不同成像模式（RGB 或 Depth）作为输入。实验结果表明，RGB 成像在对应估计方面表现优异，而如果存在良好的 3D-3D 对应，则深度成像可提高姿势精度。