SparsePose: Sparse-View Camera Pose Regression and Refinement
Samarth Sinha, Jason Y. Zhang, Andrea Tagliasacchi, Igor, Gilitschenski, David B. Lindell

TL;DR
SparsePose is a novel method that accurately estimates camera poses from fewer than 10 wide-baseline images, enabling high-quality 3D reconstruction with minimal views.
Contribution
It introduces a learning-based approach for initial pose regression and iterative refinement tailored for sparse-view scenarios, outperforming existing methods.
Findings
Outperforms conventional and learning-based baselines in pose accuracy
Enables high-fidelity 3D reconstruction with only 5-9 images
Demonstrates effectiveness on large-scale object dataset Co3D
Abstract
Camera pose estimation is a key step in standard 3D reconstruction pipelines that operate on a dense set of images of a single object or scene. However, methods for pose estimation often fail when only a few images are available because they rely on the ability to robustly identify and match visual features between image pairs. While these methods can work robustly with dense camera views, capturing a large set of images can be time-consuming or impractical. We propose SparsePose for recovering accurate camera poses given a sparse set of wide-baseline images (fewer than 10). The method learns to regress initial camera poses and then iteratively refine them after training on a large-scale dataset of objects (Co3D: Common Objects in 3D). SparsePose significantly outperforms conventional and learning-based baselines in recovering accurate camera rotations and translations. We also…
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Taxonomy
TopicsAdvanced Vision and Imaging · Robotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques
Methodsfail
