What did Tongji and Ali study in the CVPR 2022 best student thesis award? This is an interpretation of yizuo

In this article, we get CVPR 2022 Work for the best student thesis award 《EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation》. The problem studied in this paper is to estimate the location of objects based on a single image 3D Pose in space . Among the existing methods , be based on PnP Geometric optimization of pose estimation methods are often extracted through the depth network 2D-3D Association point , However, there is a non differentiable problem in the back-propagation of the optimal position and orientation solution , It is difficult to realize the stable end-to-end training of the network with the pose error as the loss , here 2D-3D The correlation point relies on the monitoring of other agents' losses , This is not the best training target for pose estimation . To solve this problem , We start from the theory , Put forward EPro-PnP modular , The probability density distribution of the output pose is not a single optimal solution , Thus, the non derivable optimal pose is replaced by the derivable probability density , Stable end-to-end training .EPro-PnP Strong commonality , Applicable to various specific tasks and data , It can be used to improve the existing PnP The method of pose estimation , You can also use its flexibility to train new networks . In a more general sense ,EPro-PnP The essence is to classify the common softmax Into the continuous field , In theory, it can be extended to training general models with nested optimization layers .

• Thesis link ：https://arxiv.org/abs/2203.13254
• Code link ：https://github.com/tjiiv-cprg/EPro-PnP