本文選題：光流估計 + 大位移��； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：光流是計算機視覺中的重要研究領(lǐng)域,在運動分割、物體識別、目標(biāo)跟蹤、視頻差值、三維重建等方面均有應(yīng)用。光流估計問題是計算機視覺中一個經(jīng)典和基礎(chǔ)的問題。自從光流被提出之后,大量研究人員和學(xué)者開始從事光流的計算方面的研究。近年來專家學(xué)者們陸陸續(xù)續(xù)提出了很多有效的光流估計方法。目前光流估計仍然存在一些問題,如在實際工程應(yīng)用中面臨大位移和戶外場景時難以得到較為滿意的結(jié)果,計算復(fù)雜度非常高,時耗大,即使使用GPU實現(xiàn)也很難做到實時估計。本文提出了一種針對大位移問題的一種非常有效的快速光流估計方法,該方法能夠有效并快速的得到較為準(zhǔn)確的光流估計結(jié)果。該方法將匹配思想與傳統(tǒng)變分優(yōu)化方法相結(jié)合,使之能夠良好地處理大位移情況下的光流估計問題。結(jié)合特征匹配去噪,增加可靠的匹配點數(shù)量,進而快速地獲得稠密的初始化光流場。在初始化光流場的基礎(chǔ)上,使用基于變分優(yōu)化的細節(jié)優(yōu)化,實現(xiàn)了高質(zhì)量快速的稠密光流場估計。此外,我們還采用了 GPU實現(xiàn)對算法進行了加速,從而能滿足一些對光流計算速度要求較高的應(yīng)用要求。同時將我們的光流估計算法應(yīng)用于三維重建,在現(xiàn)有方法上增加了時空一致性優(yōu)化改善了重建結(jié)果。利用連續(xù)幀之間的關(guān)聯(lián)信息,建立了連續(xù)幀間的光滑約束進行時空一致性深度優(yōu)化,提高了深度恢復(fù)和三維重建質(zhì)量。實驗表明,本文的方法在KITTI數(shù)據(jù)集上有良好的表現(xiàn)。能夠在確保一定精度的前提下,進下快速的光流估計。在原有深度結(jié)果的基礎(chǔ)上,使用我們的光流結(jié)果對其進行時空一致性優(yōu)化,得到了更為準(zhǔn)確的深度信息和三維重建結(jié)果。
[Abstract]:Optical flow is an important research field in computer vision, which has been applied in motion segmentation, object recognition, target tracking, video difference, 3D reconstruction and so on.Optical flow estimation is a classical and fundamental problem in computer vision.Since the optical flow was proposed, a large number of researchers and scholars began to study the calculation of optical flow.In recent years, many effective optical flow estimation methods have been proposed by experts and scholars.At present, there are still some problems in optical flow estimation, for example, it is difficult to obtain satisfactory results when facing large displacement and outdoor scenes in practical engineering applications, the computational complexity is very high, the time consumption is very large, and it is difficult to achieve real-time estimation even using GPU.In this paper, a very effective fast optical flow estimation method for large displacement problem is proposed. This method can get more accurate results of optical flow estimation effectively and quickly.This method combines the idea of matching with the traditional variational optimization method, so that it can deal with the problem of optical flow estimation in the case of large displacement.Combined with feature matching denoising, the number of reliable matching points is increased, and the dense initial optical flow field is obtained quickly.In addition, we use GPU to speed up the algorithm, which can meet the requirements of some applications with high speed of optical flow calculation.At the same time, our optical flow estimation algorithm is applied to 3D reconstruction, and the spatio-temporal consistency optimization is added to the existing methods to improve the reconstruction results.Using the correlation information between successive frames, the smooth constraints between successive frames are established to optimize the spatio-temporal consistency depth, which improves the quality of depth recovery and 3D reconstruction.Experiments show that the proposed method has a good performance on KITTI data sets.Under the premise of ensuring certain precision, fast optical flow estimation can be achieved.On the basis of the original depth results, we use our optical flow results to optimize the temporal and spatial consistency, and obtain more accurate depth information and 3D reconstruction results.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

【參考文獻】

相關(guān)碩士學(xué)位論文 前1條

1 孔相澧;基于全局優(yōu)化的高精度多視圖三維重建[D];浙江大學(xué);2011年

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本文編號：1741424