Cameras as Rays: Pose Estimation via Ray Diffusion
CoRR(2024)
摘要
Estimating camera poses is a fundamental task for 3D reconstruction and
remains challenging given sparse 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 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.
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