Segment-based Disparity Refinement with Occlusion Handling for Stereo Matching翻譯和理解
摘要 摘要:本文提出了一種視差細化方法，通過對視差統計意義的探討，直接細化贏者通吃視差圖。根據基於分割的立體匹配的主要步驟，將參考圖像過度分割爲超像素，並通過改進的隨機樣本對每個超像素進行視差面擬合共識(RANSAC)。我們設計了一個兩層優化來細化視差平面。在全局優化中，利用馬爾可夫隨機域(MRF)推理估計超像素點的平均視差，然後根據平均視差推導出遮擋處理的三維鄰域系統。在局部優化中，採用貝葉斯推
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相關文章
- 1. Self-Supervised Learning for Stereo Matching with Self-Improving Ability
- 2. CV Stereo Vision
- 3. 【翻譯】Computing the Stereo Matching Cost with a Convolutional Neural Network
- 4. 【翻譯學習筆記】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. 【論文筆記-Stereo】Pyramid Stereo Matching Network
- 10. 論文閱讀筆記《PatchMatch Stereo - Stereo Matching with Slanted Support Windows》