DeepMatching: Hierarchical Deformable Dense Matching

Jerome Revaud 1, * Philippe Weinzaepfel 1 Zaid Harchaoui 1 Cordelia Schmid 1
* Corresponding author
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : We introduce a novel matching algorithm, called DeepMatching, to compute dense correspondences between images. DeepMatching relies on a hierarchical, multi-layer, correlational architecture designed for matching images and was inspired by deep convolutional approaches. The proposed matching algorithm can handle non-rigid deformations and repetitive textures and efficiently determines dense correspondences in the presence of significant changes between images. We evaluate the performance of DeepMatching, in comparison with state-of-the-art matching algorithms, on the Mikolajczyk (Mikolajczyk et al 2005), the MPI-Sintel (Butler et al 2012) and the Kitti (Geiger et al 2013) datasets. DeepMatching outperforms the state-of-the-art algorithms and shows excellent results in particular for repetitive textures. We also propose a method for estimating optical flow, called DeepFlow, by integrating DeepMatching in the large displacement optical flow (LDOF) approach of Brox and Malik (2011). Compared to existing matching algorithms, additional robustness to large displacements and complex motion is obtained thanks to our matching approach. DeepFlow obtains competitive performance on public benchmarks for optical flow estimation.
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Submitted on : Thursday, October 8, 2015 - 11:46:13 AM
Last modification on : Monday, December 17, 2018 - 11:22:02 AM
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  • HAL Id : hal-01148432, version 2
  • ARXIV : 1506.07656



Jerome Revaud, Philippe Weinzaepfel, Zaid Harchaoui, Cordelia Schmid. DeepMatching: Hierarchical Deformable Dense Matching. 2015. ⟨hal-01148432v2⟩



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