Week texture objects pose estimation based on 3D model

Autor: Shaoxiong Tian, Yang Chen, Hanmo Zhang, Changxin Gao
Rok vydání: 2018
Předmět:
Zdroj: MIPPR 2017: Pattern Recognition and Computer Vision.
DOI: 10.1117/12.2284972
Popis: 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.
Databáze: OpenAIRE