4 citations · 4 across the 1 of their papers we have counts for
6 papers
PDC-Net+: Enhanced Probabilistic Dense Correspondence Network
Prune Truong, Martin Danelljan, Radu Timofte +1
Establishing robust and accurate correspondences between a pair of images is a long-standing computer vision problem with numerous applications. While classically dominated by spar…
Warp Consistency for Unsupervised Learning of Dense Correspondences
Prune Truong, Martin Danelljan, Fisher Yu +1
The key challenge in learning dense correspondences lies in the lack of ground-truth matches for real image pairs. While photometric consistency losses provide unsupervised alterna…
Learning Accurate Dense Correspondences and When to Trust Them
Prune Truong, Martin Danelljan, Luc Van Gool +1
Establishing dense correspondences between a pair of images is an important and general problem. However, dense flow estimation is often inaccurate in the case of large displacemen…
GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network
Prune Truong, Martin Danelljan, Luc Van Gool +1
The feature correlation layer serves as a key neural network module in numerous computer vision problems that involve dense correspondences between image pairs. It predicts a corre…
GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences
Prune Truong, Martin Danelljan, Radu Timofte
Establishing dense correspondences between a pair of images is an important and general problem, covering geometric matching, optical flow and semantic correspondences. While these…
GLAMpoints: Greedily Learned Accurate Match points
Prune Truong, Stefanos Apostolopoulos, Agata Mosinska +3
We introduce a novel CNN-based feature point detector - GLAMpoints - learned in a semi-supervised manner. Our detector extracts repeatable, stable interest points with a dense cove…