Determining camera poses accurately from sparse images presents a significant challenge for 3D representation. Traditional structure-from-motion methods often struggle in limited view situations. This has led to a shift towards learning-based strategies intended to improve the accuracy of camera pose predictions from sparse image sets. These new approaches are exploring various learning techniques, including regression and denoising diffusion. However, the optimal representation of camera poses for neural learning is a question of significant importance. Traditional methods rely on extrinsic camera matrices, made up of a rotation and translation component, but more granular, distributed representations might be more effective for learning-based methods.
Researchers have proposed a patch-wise ray prediction model as a novel approach to camera parametrization. Rather than using the traditional global rotation and translation prediction, this model predicts individual rays for each image patch. This methodology is ideal for transformer-based models that process sets of features extracted from the patches, thus providing a detailed and nuanced understanding of camera poses.
The method’s appeal comes from its ability to convert a collection of predicted rays into classical extrinsic and intrinsic camera configurations, allowing for non-perspective camera accommodation. Initial experiments with a patch-based transformer model show substantial improvements over existing pose prediction methods. The incorporation of a denoising diffusion-based probabilistic model to handle inherent ambiguities in ray prediction enhances the model’s performance further.
An extensive evaluation of the method was performed on the CO3D dataset to examine its performance across familiar and novel categories, and its ability to generalize to entirely unseen data sets. The results confirm that this ray diffusion approach has superior performance in estimating camera poses, especially in challenging sparse-view situations.
This research presents a new method for camera parametrization and sets a new benchmark for pose estimation accuracy. Its success highlights the potential of adopting more complex, distributed representations for neural learning, and paves the way for future advances in this domain. The shift from a global pose representation to a more detailed ray-based model opens up new possibilities for exploration in the field of 3D representation and pose estimation.
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