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Conference Papers Year : 2016

Accurate 3D car pose estimation


We propose a new approach for accurate car pose estimation in images using only a dataset of 3D untextured models. Our algorithm detects both a car and its 3D pose. It is based on the matching of 3D models with the car in the image. With a part detector based on Convolutional Neural Networks, interest points corresponding to predefined 3D parts are extracted from the image. Then, we use the car geometry to find which parts are relevant across viewpoints. Finally, a 2D/3D pose estimator is used to recover the 3D pose of the car. The main contribution is to learn appearance and geometry models from 3D models dataset only. Experiments show that the method is very competitive for car detection and coarse viewpoint classification and improves the 3D pose estimation over the state-of-the-art methods.
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cea-01841166 , version 1 (17-07-2018)



F. Chabot, M. Chaouch, J. Rabarisoa, C. Teuliere, T. Chateau. Accurate 3D car pose estimation. 2016 IEEE International Conference on Image Processing (ICIP), Sep 2016, Phoenix, AZ, United States. pp.3807-3811, ⟨10.1109/ICIP.2016.7533072⟩. ⟨cea-01841166⟩
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