Monocular object 6D pose estimation is a fundamental yet challenging task in computer vision. Recently, deep learning has been proven to be capable of predicting remarkable results in this task. Existing works often adopt a two-stage pipeline with establishing 2D-3D correspondences and utilizing a PnP/RANSAC or differentiable PnP algorithm to recover 6 degrees-of-freedom (6DoF) pose parameters. However, most of them hardly consider the geometric features in 3D space, and ignore the topological cues when performing differentiable PnP algorithms. To this end, we present an improved end-to-end monocular 6D pose estimation method (DGECN++) that incorporates depth estimation and a geometric-aware learnable PnP network. Our method is based on keypoints. First we detect the 2D keypoints that correspond to the 3D model. We then integrate differentiable PnP/RANSAC algorithm to create an end-to-end pipeline for 6D pose estimation. We focuses on the following three key aspects: 1) We utilize the estimated depth information to guide the process of extracting 2D-3D correspondences and refine the results using a cascaded differentiable PnP/RANSAC algorithm that incorporates geometric information. 2) We leverage the uncertainty of the estimated depth map to enhance the accuracy and robustness of the predicted 6D pose. 3) We propose a differentiable Perspective-n-Point (PnP) algorithm based on edge convolution and self-attention to explore the topological relationships between 2D-3D correspondences. Experimental results demonstrate that our proposed network surpasses existing methods in terms of both effectiveness and efficiency.

Monocular 6D pose estimation is a fundamental task in computer vision. Existing works often adopt a two-stage pipeline by establishing correspondences and utilizing a RANSAC algorithm to calculate 6 degrees-of-freedom (6DoF) pose. Recent works try to integrate differentiable RANSAC algorithms to achieve an end-to-end 6D pose estimation. However, most of them hardly consider the geometric features in 3D space, and ignore the topology cues when performing differentiable RANSAC algorithms. To this end, we proposed a Depth-Guided Edge Convolutional Network (DGECN) for 6D pose estimation task. We have made efforts from the following three aspects: 1) We take advantages of estimated depth information to guide both the correspondences-extraction process and the cascaded differentiable RANSAC algorithm with geometric information. 2) We leverage the uncertainty of the estimated depth map to improve accuracy and robustness of the output 6D pose. 3) We propose a differentiable Perspective-n-Point(PnP) algorithm via edge convolution to explore the topology relations between 2D-3D correspondences. Experiments demonstrate that our proposed network outperforms current works on both effectiveness and efficiency.