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

Image-Based Rendering (IBR) allows good-quality free-viewpoint navigation in urban scenes, but suffers from artifacts 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/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.

@InProceedings{ODMAD16,
  author       = "Ortiz-Cayon, Rodrigo and Djelouah, Abdelaziz and Massa, Francisco and Aubry, Mathieu and Drettakis, George",
  title        = "Automatic 3D Car Model Alignment for Mixed Image-Based Rendering",
  booktitle    = "International Conference on 3D Vision (3DV)",
  year         = "2016",
  url          = "http://www-sop.inria.fr/reves/Basilic/2016/ODMAD16"
}