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Found 8 papers, 5 papers with code
BodyMap: Learning Full-Body Dense Correspondence Map
Dense correspondence between humans carries powerful semantic information that can be utilized to solve fundamental problems for full-body understanding such as in-the-wild surface matching, tracking and reconstruction.
Reconstructing and grounding narrated instructional videos in 3D
Contrary to the standard scenario of instance-level 3D reconstruction, where identical objects or scenes are present in all views, objects in different instructional videos may have large appearance variations given varying conditions and versions of the same product.
Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions
In this work we target the problem of estimating accurately localised correspondences between a pair of images.
Is This The Right Place? Geometric-Semantic Pose Verification for Indoor Visual Localization
The pose with the largest geometric consistency with the query image, e. g., in the form of an inlier count, is then selected in a second stage.
D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions.
Ranked #10 on
Image Matching
on IMC PhotoTourism
Neighbourhood Consensus Networks
Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences.
Ranked #2 on
Semantic correspondence
on PF-PASCAL
(PCK (weak) metric)
End-to-end weakly-supervised semantic alignment
We tackle the task of semantic alignment where the goal is to compute dense semantic correspondence aligning two images depicting objects of the same category.
Convolutional neural network architecture for geometric matching
We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating its parameters.
