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              基于單應性擴散約束的二步網格優化視差圖像對齊

              陳殷齊 鄭慧誠 嚴志偉 林峻宇

              陳殷齊, 鄭慧誠, 嚴志偉, 林峻宇. 基于單應性擴散約束的二步網格優化視差圖像對齊. 自動化學報, 2024, 50(6): 1?14 doi: 10.16383/j.aas.c210966
              引用本文: 陳殷齊, 鄭慧誠, 嚴志偉, 林峻宇. 基于單應性擴散約束的二步網格優化視差圖像對齊. 自動化學報, 2024, 50(6): 1?14 doi: 10.16383/j.aas.c210966
              Chen Yin-Qi, Zheng Hui-Cheng, Yan Zhi-Wei, Lin Jun-Yu. Parallax image alignment with two-stage mesh optimization based on homography diffusion constraints. Acta Automatica Sinica, 2024, 50(6): 1?14 doi: 10.16383/j.aas.c210966
              Citation: Chen Yin-Qi, Zheng Hui-Cheng, Yan Zhi-Wei, Lin Jun-Yu. Parallax image alignment with two-stage mesh optimization based on homography diffusion constraints. Acta Automatica Sinica, 2024, 50(6): 1?14 doi: 10.16383/j.aas.c210966

              基于單應性擴散約束的二步網格優化視差圖像對齊

              doi: 10.16383/j.aas.c210966
              基金項目: 國家自然科學基金 (61976231), 廣東省基礎與應用基礎研究基金 (2019A1515011869), 廣州市科技計劃項目 (201803030029) 資助
              詳細信息
                作者簡介:

                陳殷齊:中山大學計算機學院碩士研究生. 主要研究方向為圖像對齊與拼接. E-mail: chenyq277@mail2.sysu.edu.cn

                鄭慧誠:中山大學計算機學院副教授. 2004年獲得法國里爾一大博士學位. 主要研究方向為計算機視覺, 神經網絡和機器學習. 本文通信作者. E-mail: zhenghch@mail.sysu.edu.cn

                嚴志偉:中山大學計算機學院碩士研究生. 主要研究方向為深度學習, 目標檢測. E-mail: yanzhw5@mail2.sysu.edu.cn

                林峻宇:復旦大學計算機科學技術學院碩士研究生. 主要研究方向為深度學習, 具身智能. E-mail: 22210240210@m.fudan.edu.cn

              Parallax Image Alignment With Two-stage Mesh Optimization Based on Homography Diffusion Constraints

              Funds: Supported by National Natural Science Foundation of China (61976231), Guangdong Basic and Applied Basic Research Foundation (2019A1515011869), and Science and Technology Program of Guangzhou (201803030029)
              More Information
                Author Bio:

                CHEN Yin-Qi Master student at the School of Computer Science and Engineering, Sun Yat-sen University. His research interest covers image alignment and stitching

                ZHENG Hui-Cheng Associate professor at the School of Computer Science and Engineering, Sun Yat-sen University. He received his Ph.D. degree from University of Lille 1, France, in 2004. His research interest covers computer vision, neural networks, and machine learning. Corresponding author of this paper

                YAN Zhi-Wei Master student at the School of Computer Science and Engineering, Sun Yat-sen University. His research interest covers deep learning and object detection

                LIN Jun-Yu Master student at the School of Computer Science, Fudan University. His research interest covers deep learning and embodied artificial intelligence

              • 摘要: 目前, 在帶有視差場景的圖像對齊中, 主要難點在某些無法找到足夠匹配特征的區域, 這些區域稱為匹配特征缺失區域. 現有算法往往忽略匹配特征缺失區域的對齊建模, 而只將有足夠匹配特征區域中的部分單應變換系數(如相似性變換系數)傳遞給匹配特征缺失區域, 或者采用將匹配特征缺失區域轉化為有足夠匹配特征區域的間接方式, 因此對齊效果仍不理想. 在客觀事實上, 位于相同平面的區域應該擁有相同的完整單應變換而非部分變換參數. 由此出發, 利用單應變換系數擴散的思想設計了一個二步網格優化的圖像對齊算法, 簡稱單應擴展變換(Homography diffusion warping, HDW)算法. 該方法在第一步網格優化時獲得有足夠匹配特征區域的單應變換, 再基于提出的單應性擴散約束將這些單應變換系數擴散到鄰域網格, 進行第二步網格優化, 在保證優化任務簡潔高效的前提下實現單應變換系數的傳播與圖像對齊. 相較于現有的針對視差場景圖像對齊算法, 所提方法在各項指標上都獲得了更好的效果.
              • 圖  1  HDW與現有方法的圖像對齊效果對比

                Fig.  1  Comparison of image alignment effects between HDW and existing methods

                圖  2  HDW對齊示意圖

                Fig.  2  The alignment process of HDW

                圖  3  平面分割特性的分析

                Fig.  3  Analysis of plane segmentation characteristics

                圖  4  各圖像對上量化指標的直觀對比

                Fig.  4  Intuitive comparison of the quantitative indicators on all the image pairs

                圖  5  Plant實例上的對齊結果對比

                Fig.  5  Comparison of the alignment results on the Plant case

                圖  6  Carpark實例上的對齊結果對比

                Fig.  6  Comparison of the alignment results on the Carpark case

                圖  7  Stationery實例上的對齊結果對比

                Fig.  7  Comparison of the alignment results on the Stationery case

                圖  8  Temple與Railtrack實例上的對齊結果對比

                Fig.  8  Comparison of the alignment results on the Temple and Railtrack cases

                圖  9  HDW仍然有提升空間的實例

                Fig.  9  Examples where HDW still has room for improvement

                圖  10  HDW的更多對齊結果實例

                Fig.  10  Alignment results of HDW on more examples

                表  1  HDW相對其他算法在Err、PSNR和SSIM上的平均改進(%)

                Table  1  The average improvement of HDW compared with other algorithms on Err, PSNR, and SSIM (%)

                SMH[17]PCPS[18]APAP[6]CPW[8]LLPC[37]ACW[41]
                Err?63.80?66.07?67.15?57.50?71.78?54.49
                PSNR+4.59+9.01+8.02+7.48+13.46+7.12
                SSIM+6.24+8.48+8.65+10.33+36.45+5.67
                下載: 導出CSV

                表  2  圖像對的對齊效果量化指標對比

                Table  2  Quantitative comparison of alignment performance on image pairs

                PlantCarparkStationery
                SMH[17]Err1.23381.26591.0580
                PSNR15.749612.375822.9365
                SSIM0.65160.57190.9316
                PCPS[18]Err0.93961.19230.6695
                PSNR15.331411.555023.5924
                SSIM0.68580.56870.9355
                APAP[6]Err4.18541.13371.0154
                PSNR13.299511.936123.4257
                SSIM0.62450.63540.9236
                CPW[8]Err6.37181.64350.9038
                PSNR12.739711.203423.4680
                SSIM0.48180.58620.9258
                ACW[41]Err3.43020.79180.8070
                PSNR13.215912.073823.6319
                SSIM0.65890.65050.9278
                HDWErr0.27410.27870.5134
                PSNR18.196813.501924.2972
                SSIM0.82210.71430.9400
                下載: 導出CSV

                表  3  其他算法相對HDW在耗時上的對比

                Table  3  Temporal costs of other algorithms compared with that of HDW

                方法耗時占比 (%)實際耗時 (ms)
                HDW100106
                SMH[17]12561331
                PCPS[18]542575
                APAP[6]1935120511
                CPW[8]4548
                LLPC[37]298316
                ACW[41]2088722140
                下載: 導出CSV
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