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Found 10 papers, 6 papers with code
S2DNet: Learning Image Features for Accurate Sparse-to-Dense Matching
Establishing robust and accurate correspondences is a fundamental backbone to many computer vision algorithms.
Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
We present a foundation model for zero-shot metric monocular depth estimation.
Are Semi-Dense Detector-Free Methods Good at Matching Local Features?
We then propose to limit the computation of the matching accuracy to textured regions, and show that in this case SAM often surpasses SDF methods.
Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation
This paper presents a framework that combines traditional keypoint-based camera pose optimization with an invertible neural rendering mechanism.
Visual Correspondence Hallucination
Given a pair of partially overlapping source and target images and a keypoint in the source image, the keypoint's correspondent in the target image can be either visible, occluded or outside the field of view.
Back to the Feature: Learning Robust Camera Localization from Pixels to Pose
In this paper, we go Back to the Feature: we argue that deep networks should focus on learning robust and invariant visual features, while the geometric estimation should be left to principled algorithms.
Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation
Absolute camera pose estimation is usually addressed by sequentially solving two distinct subproblems: First a feature matching problem that seeks to establish putative 2D-3D correspondences, and then a Perspective-n-Point problem that minimizes, with respect to the camera pose, the sum of so-called Reprojection Errors (RE).
S2DNet: Learning Accurate Correspondences for Sparse-to-Dense Feature Matching
Establishing robust and accurate correspondences is a fundamental backbone to many computer vision algorithms.
Sparse-to-Dense Hypercolumn Matching for Long-Term Visual Localization
Given a query image, we first match it against a database of registered reference images, using recent retrieval techniques.
Improving Nighttime Retrieval-Based Localization
Outdoor visual localization is a crucial component to many computer vision systems.
