【摘要】：近年來,隨著計算機視覺的發(fā)展和成熟,多視圖三維重建以其成本低廉,操作方便的優(yōu)勢倍受關注,目前已廣泛應用于數(shù)字城市、醫(yī)療成像、虛擬現(xiàn)實等領域。圖像特征提取、特征匹配和光束法平差是多視圖三維重建的幾個關鍵步驟,但其中也在一些問題。首先,特征提取階段通常采用的SIFT算法并不能適用所有場景。其次,隨著GPU在計算能力和存儲器帶寬上展現(xiàn)出巨大的優(yōu)勢,基于GPU加速的多視圖三維重建算法成為研究的熱點。然而特征匹配算法遷移至GPU平臺的較少,并且由于硬件架構的不同,并行算法的可靠性以及有限的顯存對光束法平差并行算法進行了極大的制約。為此本文展開了以下研究:(1)本文提出了一種Harris-Laplace特征結合SIFT描述的圖像配準并行算法。特征提取階段,使用改進的Harris-Laplace算法提取出對圖像亮度、旋轉和尺度具有不變性的特征點,然后SIFT描述子對特征點進行描述。在特征點匹配階段則利用雙向匹配法和極線幾何約束對特征點進行粗匹配和精匹配。在分析該算法并行性的基礎上,采用CPU_GPU協(xié)同處理技術,從任務的性質和傳輸時間兩方面考慮,在CPU和GPU端進行合理的任務劃分,提升了整體算法的運行效率。(2)BA問題時常采用LM將其線性化,并用PCG算法求解法方程。本文先是利用PCG算法不需要顯式存儲法方程系數(shù)矩陣的特性,將其分解為簡單的雅各比矩陣和向量相乘的問題。然后在BA并行算法預處理過程中增加了一個過濾步驟,剔除了由數(shù)值轉換導致的錯誤點,保證精度的情況下最大限度發(fā)揮GPU高峰值的單精度浮點運算能力。最后在深入分析雅各比轉置矩陣與原矩陣聯(lián)系的基礎上,提出了一種光束法平差并行算法。對涉及到雅各比轉置矩陣的運算重新進行并行化設計,無需存儲雅各比轉置矩陣即可解決光束法平差問題。從實驗結果分析可知,本文提出Harris-Laplace特征結合SIFT描述的圖像配準并行算法,提升了算法的整體效率,準確度也得到可靠保證。本文提出光束法平差并行算法在獲得較好加速效果的同時,也大幅縮減了算法占用的顯存空間。借助GPU的強大的并行計算能力,多視圖三維重建實時性得到了很好的滿足。
[Abstract]:In recent years, with the development and maturity of computer vision, multi-view 3D reconstruction has been widely used in many fields such as digital city, medical imaging, virtual reality and so on because of its low cost and convenient operation. Image feature extraction, feature matching and beam adjustment are the key steps in multi-view 3D reconstruction, but there are also some problems. First of all, the SIFT algorithm usually used in the feature extraction phase is not suitable for all scenes. Secondly, with the huge advantages of GPU in computing power and memory bandwidth, the multi-view 3D reconstruction algorithm based on GPU has become a hot research topic. However, the migration of feature matching algorithm to GPU platform is less, and because of the different hardware architecture, the reliability of parallel algorithm and the limited display memory greatly restrict the parallel algorithm of beam adjustment. The main contents of this paper are as follows: (1) A parallel image registration algorithm based on Harris-Laplace feature and SIFT description is proposed in this paper. In the feature extraction phase, the improved Harris-Laplace algorithm is used to extract the feature points which are invariant to the brightness, rotation and scale of the image, and then the feature points are described by the SIFT descriptor. In the stage of feature point matching, two-way matching method and polar geometric constraint are used for coarse matching and fine matching of feature points. On the basis of analyzing the parallelism of the algorithm, the CPU_GPU cooperative processing technology is used to divide the tasks reasonably on the CPU and GPU sides, considering the nature of the task and the transmission time. The efficiency of the whole algorithm is improved. (2) LM is often used to linearize the BA problem and PCG algorithm is used to solve the equation. In this paper, we first decompose the PCG algorithm into a simple Yakubi matrix and a vector multiplication problem by taking advantage of the characteristic of the coefficient matrix of the equation without the need of explicit memory method. Then a filtering step is added to the pre-processing process of the BA parallel algorithm, which eliminates the error points caused by the numerical conversion and maximizes the single-precision floating-point operation capability of the GPU with high peak value under the condition of ensuring the accuracy. Finally, on the basis of deeply analyzing the relationship between Yakubi transposable matrix and original matrix, a parallel algorithm of beam adjustment is proposed. The parallel design of the operation involving Yakubi transposable matrix can solve the adjustment problem of beam method without storing Yakubi transposable matrix. From the analysis of the experimental results, this paper proposes a parallel image registration algorithm based on Harris-Laplace feature and SIFT description, which improves the overall efficiency of the algorithm and ensures the accuracy of the algorithm. In this paper, a parallel algorithm for beam adjustment is proposed, which not only achieves better acceleration effect, but also greatly reduces the memory space occupied by the algorithm. With the powerful parallel computing ability of GPU, the real-time performance of multi-view 3D reconstruction is satisfied.
【學位授予單位】：江西理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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本文編號：2458898