發(fā)布時(shí)間：2018-04-21 02:09

  本文選題：三維重建 + 立體匹配��； 參考：《浙江大學(xué)》2013年博士論文

【摘要】：隨著影視、動(dòng)漫與游戲行業(yè)的蓬勃發(fā)展,其對(duì)高真實(shí)感三維場(chǎng)景重建的需求越來(lái)越多。而在文物數(shù)字化等領(lǐng)域,對(duì)于三維模型重建要求更高,從三維重建的逼真度要求上升到了對(duì)三維形體準(zhǔn)確度及表面色彩保真度的要求�；诙嘁晥D立體匹配的三維重建是實(shí)現(xiàn)上述需求的一種重要方法,可直接計(jì)算得到包含準(zhǔn)確色彩紋理的三維模型結(jié)果。準(zhǔn)確性、魯棒性以及計(jì)算效率是評(píng)價(jià)基于多視圖立體匹配三維重建的重要標(biāo)準(zhǔn)。圖像的畸變、隨機(jī)噪聲、重復(fù)紋理以及物體間的遮擋等因素影響了多視圖立體匹配算法的魯棒性和重建結(jié)果的準(zhǔn)確性。 本文主要從提升算法魯棒性和重建結(jié)果的準(zhǔn)確性?xún)蓚�(gè)方面來(lái)深入研究面向復(fù)雜場(chǎng)景的三維重建方法：一方面,研究高質(zhì)量的深度圖計(jì)算以及融合算法,通過(guò)對(duì)影響深度計(jì)算準(zhǔn)確性的一些因素進(jìn)行建模,提高計(jì)算結(jié)果的準(zhǔn)確性。另一方面,研究基于連續(xù)優(yōu)化的深度計(jì)算方法,利用連續(xù)優(yōu)化計(jì)算魯棒性高的特點(diǎn),來(lái)提高三維重建算法的魯棒性。 具體地,本文研究圖像的徑向畸變矯正、基于非凸連續(xù)優(yōu)化的深度計(jì)算、基于凸連續(xù)優(yōu)化的深度計(jì)算以及基于連續(xù)深度圖融合的多視圖立體匹配。主要工作與創(chuàng)新包括： ●提出了一種基于矩陣QR分解的圖像徑向畸變矯正算法,解決了現(xiàn)有三維重建管線中畸變參數(shù)計(jì)算不夠魯棒的問(wèn)題,提升了多視圖三維重建算法的魯棒性和重建結(jié)果的準(zhǔn)確性。通過(guò)將畸變參數(shù)計(jì)算轉(zhuǎn)化成矩陣分解問(wèn)題,簡(jiǎn)化了參數(shù)的計(jì)算過(guò)程。 ●提出了一種基于對(duì)稱(chēng)連續(xù)優(yōu)化的深度圖計(jì)算方法,使能量泛函的解更趨于全局最優(yōu)解,有效的提高了深度圖的質(zhì)量。通過(guò)將立體匹配問(wèn)題轉(zhuǎn)化成連續(xù)馬爾科夫隨機(jī)域的形式,建立了基于對(duì)稱(chēng)連續(xù)優(yōu)化的深度計(jì)算模型。在模型的數(shù)據(jù)項(xiàng)中,引入顏色一致性約束和梯度一致性約束,提高了算法的準(zhǔn)確性。設(shè)計(jì)了基于多層圖像金字塔的迭代計(jì)算框架,有效地提高了計(jì)算出的深度圖的質(zhì)量。在匹配泛函模型的設(shè)計(jì)中,還引入了左右一致性約束,進(jìn)一步提升了深度計(jì)算結(jié)果的準(zhǔn)確性。 ●提出了一種基于凸優(yōu)化的深度圖計(jì)算方法,有效地提高了深度計(jì)算過(guò)程的魯棒性和計(jì)算結(jié)果的準(zhǔn)確性。針對(duì)物體間的相互遮擋等原因?qū)е律疃炔⒉皇菄?yán)格連續(xù)的問(wèn)題,提出了分段連續(xù)假設(shè)條件下的深度圖計(jì)算方法將深度計(jì)算問(wèn)題轉(zhuǎn)化成自由不連續(xù)泛函模型來(lái)實(shí)現(xiàn)深度的計(jì)算,同時(shí)在泛函模型中引入了圖像分割的先驗(yàn)知識(shí),有效地抑制深度圖在圖像低頻區(qū)域的噪聲。通過(guò)利用將泛函模型松懈成凸泛函的方法,確保了深度圖的計(jì)算過(guò)程不依賴(lài)初始值,提升了算法的魯棒性,提高了深度圖的質(zhì)量。 ●提出了一種基于連續(xù)深度圖融合的三維重建方法,提高了重建模型的準(zhǔn)確性。通過(guò)利用左右一致性信息來(lái)控制深度圖不同區(qū)域的更新速度,提高了深度圖的質(zhì)量。設(shè)計(jì)了一種利用近鄰圖像信息和深度信息進(jìn)行深度圖優(yōu)化的機(jī)制,進(jìn)一步提高了深度圖的質(zhì)量。綜合利用左右一致性約束信息、點(diǎn)的法向量信息以及相機(jī)的視角信息有效解決了深度融合過(guò)程中的去噪問(wèn)題。
[Abstract]:With the vigorous development of animation and game industry, more and more demand for 3D scene reconstruction of high realism is needed. In the fields of digitalization of cultural relics, the reconstruction of 3D model is more demanding. The requirement of three-dimensional reconstruction from the fidelity of 3D reconstruction to the accuracy of three-dimensional shape and the surface color fidelity. 3D reconstruction of body matching is an important method to realize the above requirements. It can directly calculate the results of 3D model containing accurate color texture. Accuracy, robustness and computing efficiency are the important criteria for evaluating 3D reconstruction based on multi view stereo matching. Blocking factors affect the robustness of the multi view stereo matching algorithm and the accuracy of the reconstruction results.
In this paper, the methods of 3D reconstruction for complex scenes are studied in two aspects: the robustness of the lifting algorithm and the accuracy of the reconstruction results. On the one hand, the high quality depth map calculation and the fusion algorithm are studied. By modeling some factors that affect the accuracy of the depth calculation, the accuracy of the calculation results is improved. In order to improve the robustness of the 3D reconstruction algorithm, the depth calculation method based on continuous optimization is studied.
Specifically, this paper studies the correction of radial distortion of images, depth calculation based on non convex continuous optimization, depth calculation based on convex continuous optimization and multi view stereo matching based on continuous depth map fusion. The main work and innovation include:
An image radial distortion correction algorithm based on matrix QR decomposition is proposed to solve the problem that the distortion parameter calculation in the existing 3D reconstruction pipeline is not robust enough to improve the robustness of the multi view 3D reconstruction algorithm and the accuracy of the reconstruction results. The calculation process.
A depth map calculation method based on symmetric continuous optimization is proposed, which makes the energy functional solution more global optimal solution and improves the quality of the depth map effectively. By transforming the stereo matching problem into the form of the continuous Markov random domain, a depth calculation model based on symmetrical continuous optimization is established. With the introduction of color consistency constraint and gradient conformance constraint, the accuracy of the algorithm is improved. An iterative calculation framework based on the multi-layer image Pyramid is designed to effectively improve the quality of the calculated depth map. In the design of the matched functional model, the left and right constraints are introduced, which further improves the depth calculation results. Accuracy.
A method of depth map calculation based on convex optimization is proposed, which effectively improves the robustness of the depth calculation process and the accuracy of the calculation results. In view of the problem that the depth is not strictly continuous for the reasons of mutual occlusion among objects, a depth figure calculation method is proposed under the condition of piecewise continuous hypothesis. It is converted into a free discontinuous functional model to realize the calculation of depth. At the same time, the prior knowledge of image segmentation is introduced in the functional model, and the noise of the depth map is effectively suppressed in the low frequency region of the image. By using the method of reducing the functional model into a convex functional, the calculation process of the depth map does not depend on the initial value, and the algorithm is improved. The robustness is improved and the quality of the depth map is improved.
A three-dimensional reconstruction method based on continuous depth map fusion is proposed to improve the accuracy of the reconstruction model. By using the information of the left and right consistency to control the update speed of the different areas of the depth map and improve the quality of the depth map, a mechanism of depth map optimization is designed by using the information of adjacent images and depth information. The quality of the depth map is improved step by step. Using the information of the left and right consistency constraints, the point information of the normal vector and the camera's angle of view information can effectively solve the denoising problem in the process of depth fusion.

【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TP391.41

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本文編號(hào)：1780454