Patch Match Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation

Lu, Jiangbo; Yang, Hongsheng; Min, Dongbo; Minh, N.

doi:10.1109/cvpr.2013.242

Cited by 119 publications

(93 citation statements)

References 13 publications

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“…There are also approaches that try to obtain correspondence fields tailored to optical flow. Lu et al [23] used superpixels to gain edge aware correspondence fields. Bao et al [3] used an edge aware bilateral data term instead.…”

Section: Related Workmentioning

confidence: 99%

Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation

Bailer

Taetz

Stricker

2015

2015 IEEE International Conference on Computer Vision (ICCV)

208

221

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Modern large displacement optical flow algorithms usually use an initialization by either sparse descriptor matching techniques or dense approximate nearest neighbor fields. While the latter have the advantage of being dense, they have the major disadvantage of being very outlier prone as they are not designed to find the optical flow, but the visually most similar correspondence. In this paper we present a dense correspondence field approach that is much less outlier prone and thus much better suited for optical flow estimation than approximate nearest neighbor fields. Our approach is conceptually novel as it does not require explicit regularization, smoothing (like median filtering) or a new data term, but solely our novel purely data based search strategy that finds most inliers (even for small objects), while it effectively avoids finding outliers. Moreover, we present novel enhancements for outlier filtering. We show that our approach is better suited for large displacement optical flow estimation than state-of-the-art descriptor matching techniques. We do so by initializing EpicFlow (so far the best method on MPI-Sintel) with our Flow Fields instead of their originally used state-of-the-art descriptor matching technique. We significantly outperform the original EpicFlow on MPI-Sintel, KITTI and Middlebury.

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Section: Related Workmentioning

confidence: 99%

Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation

Bailer

Taetz

Stricker

2015

2015 IEEE International Conference on Computer Vision (ICCV)

208

221

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“…Recently, a new trend in stereo vision is to solve the correspondence problem in continuous plane parameter space rather than in discrete disparity label space [1,13,32]. These methods can handle slant planes very well and one probable future direction is to investigate the scale space behavior of these methods.…”

Section: Discussionmentioning

confidence: 99%

Cross-Scale Cost Aggregation for Stereo Matching

Zhang

Fang

Min

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

Self Cite

126

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Human beings process stereoscopic correspondence across multiple scales. However, this bio-inspiration is ignored by state-of-the-art cost aggregation methods for dense stereo correspondence. In this paper, a generic cross-scale cost aggregation framework is proposed to allow multi-scale interaction in cost aggregation. We firstly reformulate cost aggregation from a unified optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Then, an inter-scale regularizer is introduced into optimization and solving this new optimization problem leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation methods can be integrated into the proposed general framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets.

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“…Bleyer et al [25] showed that better correspondences can be found between stereo image pairs by adding additional degrees of freedom to patches so they can tilt in 3D out of the camera plane. The PatchMatch filter work [26] showed that edge-aware filters on cost volumes can be used in combination with PatchMatch to solve labeling problems such as optical flow and stereo matching. Results for stereo matching are shown in Fig.…”

Section: Matching Algorithmsmentioning

confidence: 99%

“…In this section, we discuss some [26]. The algorithm accepts a stereo image pair as input, and estimates stereo disparity maps.…”

Section: Imagesmentioning

confidence: 99%

A survey of the state-of-the-art in patch-based synthesis

Barnes

Zhang

2017

Comp. Visual Media

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This paper surveys the state-of-the-art of research in patch-based synthesis. Patch-based methods synthesize output images by copying small regions from exemplar imagery. This line of research originated from an area called "texture synthesis", which focused on creating regular or semi-regular textures from small exemplars. However, more recently, much research has focused on synthesis of larger and more diverse imagery, such as photos, photo collections, videos, and light fields. Additionally, recent research has focused on customizing the synthesis process for particular problem domains, such as synthesizing artistic or decorative brushes, synthesis of rich materials, and synthesis for 3D fabrication. This report investigates recent papers that follow these themes, with a particular emphasis on papers published since 2009, when the last survey in this area was published. This survey can serve as a tutorial for readers who are not yet familiar with these topics, as well as provide comparisons between these papers, and highlight some open problems in this area.

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Patch Match Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation

Cited by 119 publications

References 13 publications

Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation

Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation

Cross-Scale Cost Aggregation for Stereo Matching

A survey of the state-of-the-art in patch-based synthesis

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