在3D 資訊重建中，立體視覺影像密匹配為關鍵步驟之一，其在攝影測量和計算機視覺領域方面，仍然是一項艱鉅的任務。除了基於罩窗的匹配之外，在機器學習的最新研究中，其通過使用深度卷積神經網絡（DCNN），在密匹配取得了很大進展。雙向引導擴散式神經網絡（Dual-GDNet），於本研究中提出，其不僅在網絡設計和訓練過程中，採用由左影像至右影像的傳統匹配架構，並於訓練中加入由右至左的左右一致性檢測之特性，意於減少錯誤匹配的機率。此外，抑制型回歸(Suppressed Regression) 亦於本研究中提出，其通過在視差回歸前，去除無關機率信息來估計視差，避免在多峰機率分布中，估計出不屬於任何一峰的視差結果。Dual-GDNet中的左右一致性概念，可應用於現有的DCNN 模型，以進一步改善視差估計。為了評估各新型神經網路設計之性能，選擇了GANet作為骨幹和主要比較對象，並採納Scene Flow 和KITTI 2015 立體數據集，做為訓練與評估模型的資料集。實驗結果證明，在端點誤差(EPE Error)、大於一之像素誤差比率(Error Rate) 和top2誤差方面，與相關模型比較之下，表現較為優異，其中Scene Flow 數據集的改進為2-10%，KITTI 2015 數據集的改進為2-8%。

Stereo dense matching which plays a key role in 3D reconstruction still remains a challenging task in photogrammetry and computer vision. In addition to block-based matching, recent studies based on machine learning have achieved great progress in stereo dense matching by using deep convolutional neural networks (DCNN). In this paper, a novel neural network called dual guided-diffusion network (Dual-GDNet) is proposed, which utilizes not only left-to-right but right-to-left image matchings in the network design and training with a consistentization process to reduce the possibilities of mis-matching. In addition, suppressed regression is proposed to refine disparity estimation by removing unrelated information before regression to prevent ambiguous predictions on multi-peaks probability distributions. The proposed Dual-GDNet can be applied to existing DCNN models for further improvement on disparity estimation. To estimate the performance, GA-Net is selected as the backbone, and the model was evaluated on the stereo datasets including Scene Flow and KITTI 2015. Experimental results demonstrate the superior, in terms of end-point-error, > 1 pixel error rate, and top-2 error, of the proposed model, compared with related models. An improvement of 2-10% on Scene Flow and 2-8% on KITTI 2015 datasets were obtained.

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