DeepMatching: Hierarchical Deformable Dense Matching

Jerome Revaud (1) , Philippe Weinzaepfel (1) , Zaid Harchaoui (1) , Cordelia Schmid (1)

1 LEAR - Learning and recognition in vision

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.

Mots clés: deep convolutional neural networks (CNN); Non-rigid matching; optical flow

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

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