Cameras as Rays: Pose Estimation via Ray Diffusion
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Cited by 26 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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.
Key facts. Cited by 129 papers in the map (data through 7 Oct 2026). Builds on 36 of its 55 references that are in the map. First posted on arXiv 22 Feb 2024.
Builds on
36 of 55 references.
References · 36
- 2017Attention Is All You Needcited by 59,932
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- 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,437
- 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,313
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- 2016ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Camerascited by 801
- 2018MegaDepth: Learning Single-View Depth Prediction from Internet Photoscited by 731
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- 2021Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstructioncited by 646
- 2018From Coarse to Fine: Robust Hierarchical Localization at Large Scalecited by 556
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- 2021Pixel-Perfect Structure-from-Motion with Featuremetric Refinementcited by 199
- 2022SPARF: Neural Radiance Fields from Sparse and Noisy Posescited by 195
- 2022SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Viewscited by 175
- 2018BA-Net: Dense Bundle Adjustment Networkcited by 173
- 2023PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Predictioncited by 165
- 2023PoseDiffusion: Solving Pose Estimation via Diffusion-aided Bundle Adjustmentcited by 146
- 2021NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wildcited by 136
- 2022RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wildcited by 102
- 2023RelPose++: Recovering 6D Poses from Sparse-view Observationscited by 87
- 2022SparsePose: Sparse-View Camera Pose Regression and Refinementcited by 53
- 2022Few-View Object Reconstruction with Unknown Categories and Camera Posescited by 53
- 2021Wide-Baseline Relative Camera Pose Estimation with Directional Learningcited by 49
- 2020RANSAC-Flow: generic two-stage image alignmentcited by 48
- 2019Why Having 10,000 Parameters in Your Camera Model is Better Than Twelvecited by 43
- 2021Extreme Rotation Estimation using Dense Correlation Volumescited by 42
- 2022The 8-Point Algorithm as an Inductive Bias for Relative Pose Prediction by ViTscited by 38
- 2021Differentiable Stereopsis: Meshes from multiple views using differentiable renderingcited by 26
- 2020Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motioncited by 20
- 2023Sparse 3D Reconstruction via Object-Centric Ray Samplingcited by 4
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Citing papers per year: 2024: 35; 2025: 60; 2026: 34.
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