DeepMatching: Hierarchical Deformable Dense Matching
This addresses the challenge of robust image matching and optical flow estimation for computer vision applications, with incremental improvements in handling large displacements and complex motion.
The authors tackled the problem of dense image matching under non-rigid deformations and repetitive textures, introducing DeepMatching, which outperformed state-of-the-art algorithms on datasets like Mikolajczyk, MPI-Sintel, and Kitti, and integrated it into DeepFlow for optical flow estimation, achieving competitive results on benchmarks.
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.