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Conference papers

Automatic 3D Car Model Alignment for Mixed Image-Based Rendering

Abstract : Image-Based Rendering (IBR) allows good-quality free-viewpoint navigation in urban scenes, but suffers from arti-facts on poorly reconstructed objects, e.g., reflective surfaces such as cars. To alleviate this problem, we propose a method that automatically identifies stock 3D models , aligns them in the 3D scene and performs morphing to better capture image contours. We do this by first adapting learning-based methods to detect and identify an object class and pose in images. We then propose a method which exploits all available information, namely partial and inaccurate 3D reconstruction, multi-view calibration, image contours and the 3D model to achieve accurate object alignment suitable for subsequent morphing. These steps provide models which are well-aligned in 3D and to contours in all the images of the multi-view dataset, allowing us to use the resulting model in our mixed IBR algorithm. Our results show significant improvement in image quality for free-viewpoint IBR, especially when moving far from the captured viewpoints.
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Submitted on : Monday, September 19, 2016 - 2:09:20 PM
Last modification on : Tuesday, December 8, 2020 - 9:43:28 AM


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  • HAL Id : hal-01368355, version 1



Rodrigo Ortiz-Cayon, Abdelaziz Djelouah, Francisco Massa, Mathieu Aubry, George Drettakis. Automatic 3D Car Model Alignment for Mixed Image-Based Rendering. 2016 International Conference on 3D Vision (3DV), Oct 2016, Stanford, United States. ⟨hal-01368355⟩



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