3D Wireframe Modeling and Viewpoint Estimation for Multi-Class Objects Combining Deep Neural Network and Deformable Model Matching

The accuracy of 3D viewpoint and shape estimation from 2D images has been greatly improved by machine learning, especially deep learning technology such as the convolution neural network (CNN). However, current methods are always valid only for one specific category and have exhibited poor performance when generalized to other categories, which means that multiple detectors or networks are needed for multi-class object image cases. In this paper, we propose a method with strong generalization ability, which incorporates only one CNN with deformable model matching processing for the 3D viewpoint and the shape estimation of multi-class object image cases. The CNN is utilized to detect keypoints of the potential object from the image, while a deformable model matching stage is designed to conduct 3D wireframe modeling and viewpoint estimation simultaneously with the support of the detected keypoints. Besides, parameter estimation by deformable model matching processing has robust fault-tolerance to the keypoint detection results containing mistaken keypoints. The proposed method is evaluated on Pascal3D+ dataset. Experiments show that the proposed method performs well in both parameter estimation accuracy and the multi-class objects generalization. This research is a useful exploration to extend the generalization of deep learning in specific tasks.

Abstract licence: CC BY

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