A Decomposition Model for Stereo Matching

• URL: http://arxiv.org/abs/2104.07516v1
• Date: Thu, 15 Apr 2021 15:16:23 GMT
• Title: A Decomposition Model for Stereo Matching
• Authors: Chengtang Yao, Yunde Jia, Huijun Di, Pengxiang Li, Yuwei Wu
• Abstract summary: We present a decomposition model for stereo matching to solve the problem of excessive growth in computational cost as the resolution increases. Our model only runs dense matching at a very low resolution and uses sparse matching at different higher resolutions to recover the disparity of lost details scale-by-scale.
• Score: 43.04003114948216
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present a decomposition model for stereo matching to solve the problem of excessive growth in computational cost (time and memory cost) as the resolution increases. In order to reduce the huge cost of stereo matching at the original resolution, our model only runs dense matching at a very low resolution and uses sparse matching at different higher resolutions to recover the disparity of lost details scale-by-scale. After the decomposition of stereo matching, our model iteratively fuses the sparse and dense disparity maps from adjacent scales with an occlusion-aware mask. A refinement network is also applied to improving the fusion result. Compared with high-performance methods like PSMNet and GANet, our method achieves $10-100\times$ speed increase while obtaining comparable disparity estimation results.

Related papers

• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• Stereo Matching with Cost Volume based Sparse Disparity Propagation [27.74131924190943]
  We propose a simple yet novel scheme to improve general stereo matching based on matching cost volume and sparse matching feature points. Our scheme achieves promising performance comparable to state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-01-28T05:20:41Z)
• Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-Resolution [48.093500219958834]
  We propose an Accelerated Multi-Scale Aggregation network (AMSA) for Reference-based Super-Resolution. The proposed AMSA achieves superior performance over state-of-the-art approaches on both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2022-01-12T08:40:23Z)
• Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching [13.76996108304056]
  This paper presents an iterative multi-scale coarse-to-fine refinement (iCFR) framework to bridge this gap. We use multi-scale warped features to estimate disparity residuals and push the disparity search range in the cost volume to a minimum limit. Finally, we apply a refinement network to recover the loss of precision which is inherent in multi-scale approaches.
  arXiv  Detail & Related papers  (2021-10-25T09:54:17Z)
• PixelPyramids: Exact Inference Models from Lossless Image Pyramids [58.949070311990916]
  Pixel-Pyramids is a block-autoregressive approach with scale-specific representations to encode the joint distribution of image pixels. It yields state-of-the-art results for density estimation on various image datasets, especially for high-resolution data. For CelebA-HQ 1024 x 1024, we observe that the density estimates are improved to 44% of the baseline despite sampling speeds superior even to easily parallelizable flow-based models.
  arXiv  Detail & Related papers  (2021-10-17T10:47:29Z)
• Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation [65.83008812026635]
  We construct Guided Cost volume Excitation (GCE) and show that simple channel excitation of cost volume guided by image can improve performance considerably. We present an end-to-end network that we call Correlate-and-Excite (CoEx)
  arXiv  Detail & Related papers  (2021-08-12T14:32:26Z)
• CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching [27.313740022587442]
  We propose CFNet, a Cascade and Fused cost volume based network to improve the robustness of the stereo matching network. We employ a variance-based uncertainty estimation to adaptively adjust the next stage disparity search space. Our proposed method achieves the state-of-the-art overall performance and obtains the 1st place on the stereo task of Robust Vision Challenge 2020.
  arXiv  Detail & Related papers  (2021-04-09T11:38:59Z)
• SMD-Nets: Stereo Mixture Density Networks [68.56947049719936]
  We propose Stereo Mixture Density Networks (SMD-Nets), a simple yet effective learning framework compatible with a wide class of 2D and 3D architectures. Specifically, we exploit bimodal mixture densities as output representation and show that this allows for sharp and precise disparity estimates near discontinuities. We carry out comprehensive experiments on a new high-resolution and highly realistic synthetic stereo dataset, consisting of stereo pairs at 8Mpx resolution, as well as on real-world stereo datasets.
  arXiv  Detail & Related papers  (2021-04-08T16:15:46Z)
• Fusion of Range and Stereo Data for High-Resolution Scene-Modeling [20.824550995195057]
  This paper addresses the problem of range-stereo fusion, for the construction of high-resolution depth maps. We combine low-resolution depth data with high-resolution stereo data, in a maximum a posteriori (MAP) formulation. The accuracy of the method is not compromised, owing to three properties of the data-term in the energy function.
  arXiv  Detail & Related papers  (2020-12-12T09:37:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.