Week texture objects pose estimation based on 3D model

Yang Chen; Hanmo Zhang; Shaoxiong Tian; Changxin Gao

doi:10.1117/12.2284972

9 April 2018 Week texture objects pose estimation based on 3D model

Yang Chen, Hanmo Zhang, Shaoxiong Tian, Changxin Gao

Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106090U (2018) https://doi.org/10.1117/12.2284972
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

This paper proposes a 3D pose estimation method for week texture objects, by performing point matching of a test image to a matched rendering image of an object rather than its 3D model. Give a 3D model of an object, we use an exemplar based 2D-3D matching method to estimate the coarse pose of the object. We first obtain the 2D rendering images of each view of the object using its 3D model, and build an exemplar based model using all the rendering images. For a test image, we then perform 2D-3D matching using the proposed model, and the rendering image with the highest score is the best match to the test image. The coarse pose can be obtained using the view parameters of the rending images. Finally, we perform point matching between the matched rendering image and the test image to estimate pose more accurately. The proposed coarse-to- fine pose estimation method can provide stronger constraint, which makes pose estimation more accurate. The experimental results demonstrate the effectiveness of the proposed method.

Citation Download Citation

Yang Chen, Hanmo Zhang, Shaoxiong Tian, and Changxin Gao "Week texture objects pose estimation based on 3D model", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106090U (9 April 2018); https://doi.org/10.1117/12.2284972

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