Segment-based Disparity Refinement with Occlusion Handling for Stereo Matching翻译和理解
摘要 摘要:本文提出了一种视差细化方法，通过对视差统计意义的探讨，直接细化赢者通吃视差图。根据基于分割的立体匹配的主要步骤，将参考图像过度分割为超像素，并通过改进的随机样本对每个超像素进行视差面拟合共识(RANSAC)。我们设计了一个两层优化来细化视差平面。在全局优化中，利用马尔可夫随机域(MRF)推理估计超像素点的平均视差，然后根据平均视差推导出遮挡处理的三维邻域系统。在局部优化中，采用贝叶斯推
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相关文章
- 1. Self-Supervised Learning for Stereo Matching with Self-Improving Ability
- 2. CV Stereo Vision
- 3. 【翻译】Computing the Stereo Matching Cost with a Convolutional Neural Network
- 4. 【翻译学习笔记】Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
- 5. Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture
- 6. Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning
- 7. Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching(CVPR 2020)
- 8. Unsupervised Learning of Stereo Matching
- 9. 【论文笔记-Stereo】Pyramid Stereo Matching Network
- 10. 论文阅读笔记《PatchMatch Stereo - Stereo Matching with Slanted Support Windows》