KRONC: Keypoint-based Robust Camera Optimization for 3D Car Reconstruction
<|reference_start|>KRONC: Keypoint-based Robust Camera Optimization for 3D Car Reconstruction: The three-dimensional representation of objects or scenes starting from a set of images has been a widely discussed topic for years and has gained additional attention after the diffusion of NeRF-based approaches. However, an underestimated prerequisite is the knowledge of camera poses or, more specifically, the estimation of the extrinsic calibration parameters. Although excellent general-purpose Structure-from-Motion methods are available as a pre-processing step, their computational load is high and they require a lot of frames to guarantee sufficient overlapping among the views. This paper introduces KRONC, a novel approach aimed at inferring view poses by leveraging prior knowledge about the object to reconstruct and its representation through semantic keypoints. With a focus on vehicle scenes, KRONC is able to estimate the position of the views as a solution to a light optimization problem targeting the convergence of keypoints' back-projections to a singular point. To validate the method, a specific dataset of real-world car scenes has been collected. Experiments confirm KRONC's ability to generate excellent estimates of camera poses starting from very coarse initialization. Results are comparable with Structure-from-Motion methods with huge savings in computation. Code and data will be made publicly available.<|reference_end|>
@article{di nucci2024kronc:,
    title={KRONC: Keypoint-based Robust Camera Optimization for 3D Car
  Reconstruction},
    author={Davide Di Nucci, Alessandro Simoni, Matteo Tomei, Luca Ciuffreda,
  Roberto Vezzani, Rita Cucchiara},
    journal={arXiv preprint arXiv:2409.05407},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05407},
    primaryClass={cs.CV}
}