基于單應性擴散約束的二步網(wǎng)格優(yōu)化視差圖像對齊

陳殷齊; 鄭慧誠; 嚴志偉; 林峻宇

doi:10.16383/j.aas.c210966

基于單應性擴散約束的二步網(wǎng)格優(yōu)化視差圖像對齊

doi: 10.16383/j.aas.c210966

陳殷齊^{1, 2,},
鄭慧誠^{1, 3, 4,},
嚴志偉^1,,
林峻宇^5,

1.
中山大學(xué)計算機學(xué)院廣州 510006
2.
季華實(shí)驗室新型顯示技術(shù)與裝備研究中心佛山 528000
3.
機器智能與先進(jìn)計算教育部重點(diǎn)實(shí)驗室廣州 510006
4.
廣東省信息安全技術(shù)重點(diǎn)實(shí)驗室廣州 510006
5.
復旦大學(xué)計算機科學(xué)技術(shù)學(xué)院上海 200438

基金項目: 國家自然科學(xué)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(jiǎn)介:
陳殷齊：中山大學(xué)計算機學(xué)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(xué)計算機學(xué)院副教授. 2004年獲得法國里爾第一大學(xué)博士學(xué)位. 主要研究方向為計算機視覺(jué), 神經(jīng)網(wǎng)絡(luò )和機器學(xué)習. 本文通信作者. E-mail: zhenghch@mail.sysu.edu.cn

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

林峻宇：復旦大學(xué)計算機科學(xué)技術(shù)學(xué)院碩士研究生. 主要研究方向為深度學(xué)習, 具身智能. E-mail: 22210240210@m.fudan.edu.cn

計量
- 文章訪(fǎng)問(wèn)數: 430
- HTML全文瀏覽量: 265
- PDF下載量: 85
- 被引次數: 0
出版歷程
- 收稿日期: 2021-10-13
- 錄用日期: 2022-05-17
- 網(wǎng)絡(luò )出版日期: 2022-09-05
- 刊出日期: 2024-06-27

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

CHEN Yin-Qi^{1, 2
,},
ZHENG Hui-Cheng^{1, 3, 4
,},
YAN Zhi-Wei^1
,,
LIN Jun-Yu^5
,

1.
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006
2.
New Display Technology and Equipment Research Center, Jihua Laboratory, Foshan; 528000
3.
Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou 510006
4.
Guangdong Province Key Laboratory of Information Security Technology, Guangzhou 510006
5.
School of Computer Science, Fudan University, Shanghai 200438

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

摘要: 目前, 在帶有視差場(chǎng)景的圖像對齊中, 主要難點(diǎn)在某些無(wú)法找到足夠匹配特征的區域, 這些區域稱(chēng)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(guān)事實(shí)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(fā), 利用單應變換系數擴散的思想設計了一個(gè)二步網(wǎng)格優(yōu)化的圖像對齊算法, 簡(jiǎn)稱(chēng)單應擴散變換(Homography diffusion warping, HDW)算法. 該方法在第一步網(wǎng)格優(yōu)化時(shí)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(wǎng)格, 進(jìn)行第二步網(wǎng)格優(yōu)化, 在保證優(yōu)化任務(wù)簡(jiǎn)潔高效的前提下實(shí)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(chǎng)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.
- 圖像對齊 /
- 視差場(chǎng)景 /
- 網(wǎng)格優(yōu)化 /
- 匹配特征缺失區域
Abstract: At present, the main difficulty in the image alignment with parallax scene is in the areas that cannot find sufficient matching features. We call these areas featureless regions. Cutting-edge research on parallax image alignment neglects modeling of regions without matching features. Indirect methods such as transferring partial homography of regions with matching features to featureless regions or transforming featureless regions to regions with matching features have been popularly used, which, however, do not guarantee satisfactory results. In fact, image regions belonging to the same plane should possess the same homography. In this paper, a two-stage mesh optimization algorithm, homography diffusion warping (HDW), is designed by homography diffusion. In the first stage, homography coefficients of mesh cells in the image regions with matching features are obtained. Then we propagate these homography coefficients to adjacent cells to form homography diffusion constraints, and perform the second stage optimization of the mesh by enforcing the constraints on the premise of ensuring the simplicity and efficiency of the optimization task. Compared with existing image alignment algorithms, the method proposed in this paper achieves better results on all metrics.
- Image alignment /
- parallax scene /
- mesh optimization /
- featureless regions

HTML全文

圖 1 本文算法與現有方法的圖像對齊效果對比

Fig. 1 Comparison of image alignment effects between existing methods and the method proposed in this paper

下載: 全尺寸圖片幻燈片

圖 2 HDW對齊示意圖

Fig. 2 The alignment process of HDW

下載: 全尺寸圖片幻燈片

圖 3 平面分割特性的分析

Fig. 3 Analysis of plane segmentation characteristics

下載: 全尺寸圖片幻燈片

圖 4 各圖像對量化指標的直觀(guān)對比

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

下載: 全尺寸圖片幻燈片

圖 5 Plant實(shí)例上的對齊結果對比

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

下載: 全尺寸圖片幻燈片

圖 6 Carpark實(shí)例上的對齊結果對比

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

下載: 全尺寸圖片幻燈片

圖 7 Stationery實(shí)例上的對齊結果對比

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

下載: 全尺寸圖片幻燈片

圖 8 Temple與Railtrack實(shí)例上的對齊結果對比

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

下載: 全尺寸圖片幻燈片

圖 9 HDW仍然有提升空間的實(shí)例

Fig. 9 Examples where HDW still has room for improvement

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圖 10 HDW的更多對齊結果實(shí)例

Fig. 10 Alignment results of HDW on more examples

下載: 全尺寸圖片幻燈片

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

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

		Plant	Carpark	Stationery
SMH^[17]	Err	1.2338	1.2659	1.0580
	PSNR	15.7496	12.3758	22.9365
	SSIM	0.6516	0.5719	0.9316
PCPS^[18]	Err	0.9396	1.1923	0.6695
	PSNR	15.3314	11.5550	23.5924
	SSIM	0.6858	0.5687	0.9355
APAP^[6]	Err	4.1854	1.1337	1.0154
	PSNR	13.2995	11.9361	23.4257
	SSIM	0.6245	0.6354	0.9236
CPW^[8]	Err	6.3718	1.6435	0.9038
	PSNR	12.7397	11.2034	23.4680
	SSIM	0.4818	0.5862	0.9258
ACW^[41]	Err	3.4302	0.7918	0.8070
	PSNR	13.2159	12.0738	23.6319
	SSIM	0.6589	0.6505	0.9278
HDW	Err	0.2741	0.2787	0.5134
	PSNR	18.1968	13.5019	24.2972
	SSIM	0.8221	0.7143	0.9400

下載: 導出CSV

表 3 其他算法相對HDW在耗時(shí)上的對比

Table 3 Temporal cost of HDW compared with those of other algorithms

方法	耗時(shí)占比 (%)	實(shí)際耗時(shí) (ms)
HDW	100	106
SMH^[17]	1256	1331
PCPS^[18]	542	575
APAP^[6]	19351	20511
CPW^[8]	45	48
LLPC^[37]	298	316
ACW^[41]	20887	22140

下載: 導出CSV

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