Parallax Attention for Unsupervised Stereo Correspondence Learning

• URL: http://arxiv.org/abs/2009.08250v2
• Date: Tue, 12 Oct 2021 16:24:19 GMT
• Title: Parallax Attention for Unsupervised Stereo Correspondence Learning
• Authors: Longguang Wang and Yulan Guo and Yingqian Wang and Zhengfa Liang and Zaiping Lin and Jungang Yang and Wei An
• Abstract summary: Stereo image pairs encode 3D scene cues into stereo correspondences between the left and right images. Recent CNN based methods commonly use cost volume techniques to capture stereo correspondence over large disparities. We propose a generic parallax-attention mechanism (PAM) to capture stereo correspondence regardless of disparity variations.
• Score: 46.035892564279564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stereo image pairs encode 3D scene cues into stereo correspondences between the left and right images. To exploit 3D cues within stereo images, recent CNN based methods commonly use cost volume techniques to capture stereo correspondence over large disparities. However, since disparities can vary significantly for stereo cameras with different baselines, focal lengths and resolutions, the fixed maximum disparity used in cost volume techniques hinders them to handle different stereo image pairs with large disparity variations. In this paper, we propose a generic parallax-attention mechanism (PAM) to capture stereo correspondence regardless of disparity variations. Our PAM integrates epipolar constraints with attention mechanism to calculate feature similarities along the epipolar line to capture stereo correspondence. Based on our PAM, we propose a parallax-attention stereo matching network (PASMnet) and a parallax-attention stereo image super-resolution network (PASSRnet) for stereo matching and stereo image super-resolution tasks. Moreover, we introduce a new and large-scale dataset named Flickr1024 for stereo image super-resolution. Experimental results show that our PAM is generic and can effectively learn stereo correspondence under large disparity variations in an unsupervised manner. Comparative results show that our PASMnet and PASSRnet achieve the state-of-the-art performance.

Related papers

• Stereo Risk: A Continuous Modeling Approach to Stereo Matching [110.22344879336043]
  We introduce Stereo Risk, a new deep-learning approach to solve the classical stereo-matching problem in computer vision. We demonstrate that Stereo Risk enhances stereo-matching performance for deep networks, particularly for disparities with multi-modal probability distributions. A comprehensive analysis demonstrates our method's theoretical soundness and superior performance over the state-of-the-art methods across various benchmark datasets.
  arXiv  Detail & Related papers  (2024-07-03T14:30:47Z)
• Modeling Stereo-Confidence Out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep [31.261772846687297]
  The proposed stereo-confidence method is built upon the idea that any shift in a stereo-image pair should be updated in a corresponding amount shift in the disparity map. By comparing the desirable and predicted disparity profiles, we can quantify the level of matching ambiguity between left and right images for confidence measurement.
  arXiv  Detail & Related papers  (2024-01-22T14:52:08Z)
• Active-Passive SimStereo -- Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo Methods [26.662129158141763]
  Self-similar or bland regions can make it difficult to match patches between two images. Active stereo-based methods mitigate this problem by projecting a pseudo-random pattern on the scene. If this pattern acts as a form of adversarial noise, it could negatively impact the performance of deep learning-based methods.
  arXiv  Detail & Related papers  (2022-09-17T10:30:32Z)
• Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective [65.37571681370096]
  We propose a simple pixel-wise contrastive learning across the viewpoints. A stereo selective whitening loss is introduced to better preserve the stereo feature consistency across domains. Our method achieves superior performance over several state-of-the-art networks.
  arXiv  Detail & Related papers  (2022-03-21T11:21:41Z)
• Neural Disparity Refinement for Arbitrary Resolution Stereo [67.55946402652778]
  We introduce a novel architecture for neural disparity refinement aimed at facilitating deployment of 3D computer vision on cheap and widespread consumer devices. Our approach relies on a continuous formulation that enables to estimate a refined disparity map at any arbitrary output resolution.
  arXiv  Detail & Related papers  (2021-10-28T18:00:00Z)
• 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)
• Symmetric Parallax Attention for Stereo Image Super-Resolution [46.20494593243566]
  We improve the performance of stereo image SR by exploiting symmetry cues in stereo image pairs. We design a Siamese network equipped with a biPAM to super-resolve both sides of views. Experiments on four public datasets demonstrate the superior performance of our method.
  arXiv  Detail & Related papers  (2020-11-07T16:28:35Z)
• Expanding Sparse Guidance for Stereo Matching [24.74333370941674]
  We propose a novel sparsity expansion technique to expand the sparse cues concerning RGB images for local feature enhancement. Our approach significantly boosts the existing state-of-the-art stereo algorithms with extremely sparse cues.
  arXiv  Detail & Related papers  (2020-04-24T06:41:11Z)
• AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching [50.06646151004375]
  A novel domain-adaptive pipeline called AdaStereo aims to align multi-level representations for deep stereo matching networks. Our AdaStereo models achieve state-of-the-art cross-domain performance on multiple stereo benchmarks, including KITTI, Middlebury, ETH3D, and DrivingStereo.
  arXiv  Detail & Related papers  (2020-04-09T16:15:13Z)

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.