Cameras as Rays: Pose Estimation via Ray Diffusion
<|reference_start|>Cameras as Rays: Pose Estimation via Ray Diffusion: Estimating camera poses is a fundamental task for 3D reconstruction and remains challenging given sparsely sampled views (<10). In contrast to existing approaches that pursue top-down prediction of global parametrizations of camera extrinsics, we propose a distributed representation of camera pose that treats a camera as a bundle of rays. This representation allows for a tight coupling with spatial image features improving pose precision. We observe that this representation is naturally suited for set-level transformers and develop a regression-based approach that maps image patches to corresponding rays. To capture the inherent uncertainties in sparse-view pose inference, we adapt this approach to learn a denoising diffusion model which allows us to sample plausible modes while improving performance. Our proposed methods, both regression- and diffusion-based, demonstrate state-of-the-art performance on camera pose estimation on CO3D while generalizing to unseen object categories and in-the-wild captures.<|reference_end|>
@article{zhang2024cameras,
    title={Cameras as Rays: Pose Estimation via Ray Diffusion},
    author={Jason Y. Zhang, Amy Lin, Moneish Kumar, Tzu-Hsuan Yang, Deva Ramanan,
  Shubham Tulsiani},
    journal={arXiv preprint arXiv:2402.14817},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14817},
    primaryClass={cs.CV cs.LG}
}