發(fā)布時間：2018-04-17 17:43

  本文選題：壓縮感知 + 點云數(shù)據(jù)　； 參考：《北京工業(yè)大學(xué)》2014年碩士論文

【摘要】：三維激光掃描設(shè)備的飛速發(fā)展已經(jīng)使得三維點云數(shù)據(jù)成為多媒體數(shù)據(jù)非常重要的組成部分。然而，隨著三維掃描設(shè)備精度的不斷提高，通過掃描設(shè)備所獲取的三維點云數(shù)據(jù)也越來越大、越來越復(fù)雜，這給三維點云模型在網(wǎng)絡(luò)資源有限的情況下，對其存儲、傳輸、處理帶來了很大困難，因此，關(guān)于三維點云數(shù)據(jù)的高效壓縮編碼方案一直被國內(nèi)外學(xué)者廣泛關(guān)注。 近幾年，Candes、Donoho等人提出的壓縮感知理論（Compressive Sensing/Compressed Sampling，CS）指出對于稀疏或者在某變換基下稀疏的信號可以對信號采用非線性下采樣的方法來進(jìn)行觀測，利用低維的觀測結(jié)果可以用與變換基滿足非一致性的觀測矩陣來對原始信號進(jìn)行高概率精確重建。區(qū)別于傳統(tǒng)的奈奎斯特采樣定理，壓縮感知理論結(jié)合信號的稀疏特性，利用觀測矩陣來對信號進(jìn)行觀測，從而使得信號的采樣過程不依賴于信號的帶寬，而是信號的內(nèi)容和結(jié)構(gòu)。因此，壓縮感知理論為多媒體信號的壓縮編碼開辟了一條嶄新的途徑。 本文從壓縮感知理論的最新成果出發(fā)，利用三維點云數(shù)據(jù)局部空間的相似性，建立了三維點云數(shù)據(jù)的規(guī)格化方法，提出了基于過完備字典的點云數(shù)據(jù)稀疏表示模型和編碼、重建模型。具體完成的工作如下： 第一，通過研究三維點云模型的幾何空間特性以及局部相似特性，提出了一種基于K近鄰的點云數(shù)據(jù)規(guī)格化方法，該方法有效的利用了點云數(shù)據(jù)的局部空間相似性，提高了三維點云數(shù)據(jù)在坐標(biāo)數(shù)值上的相似性，為三維點云數(shù)據(jù)的稀疏表示提供了重要保證。 第二，考慮到規(guī)格化后的點云數(shù)據(jù)之間具有自相似性，因此，本文首先提出基于K-SVD的字典訓(xùn)練算法來獲得規(guī)格化點云數(shù)據(jù)的稀疏表示基，使得規(guī)格化后的點云數(shù)據(jù)能夠在過完備字典下稀疏表示。但是，，傳統(tǒng)的過完備字典訓(xùn)練算法不能很好的適用于三維點云數(shù)據(jù)，因此，本文結(jié)合點云數(shù)據(jù)的空間幾何特性，提出一種基于K均值的三維點云數(shù)據(jù)過完備字典訓(xùn)練算法，為基于壓縮感知的點云數(shù)據(jù)編碼與重建奠定了基礎(chǔ)。 第三，以壓縮感知相關(guān)理論為指導(dǎo)，在點云數(shù)據(jù)規(guī)格化以及信號稀疏表示基礎(chǔ)下，本文針對過完備字典稀疏表示的三維點云數(shù)據(jù)，提出了基于隨-維1點云數(shù)據(jù)觀測編碼，方法，并且提出了基于1機觀測的三范數(shù)最小化重建模型，以及基于TV方法的重建模型 實驗結(jié)果表明，本文所述的三維點云數(shù)據(jù)處理方法具有良好的結(jié)果，為基于感知的三維點云數(shù)據(jù)編碼與重建提供了一條嶄新的思路，具有較高的創(chuàng)新性和實用價值。
[Abstract]:With the rapid development of 3D laser scanning equipment, 3D point cloud data has become a very important part of multimedia data.However, with the continuous improvement of the accuracy of the 3D scanning equipment, the 3D point cloud data obtained by the scanning device is becoming larger and more complex, which gives the 3D point cloud model storage and transmission in the case of limited network resources.Therefore, the efficient compression and coding scheme of 3D point cloud data has been widely concerned by domestic and foreign scholars.In recent years, Compression Sensing/Compressed sampling theory proposed by Candesl Donoho et al., points out that the signal can be observed by nonlinear down-sampling for sparse signal or sparse signal based on a transform basis.The low dimensional observation results can be used to reconstruct the original signal with high probability and precision by using an observation matrix which is not consistent with the transform basis.Different from the traditional Nyquist sampling theorem, compression sensing theory combined with the sparse characteristics of the signal, using the observation matrix to observe the signal, so that the signal sampling process does not depend on the bandwidth of the signal.It is the content and structure of the signal.Therefore, the theory of compression sensing opens up a new way for the compression and coding of multimedia signals.Based on the latest achievements of compression perception theory and the similarity of local space of 3D point cloud data, the normalization method of 3D point cloud data is established in this paper, and the sparse representation model and coding of point cloud data based on overcomplete dictionary are proposed.Reconstruct the model.The work accomplished is as follows:First, by studying the geometric spatial characteristics and local similarity of 3D point cloud model, a point cloud data normalization method based on K-nearest neighbor is proposed, which effectively utilizes the local spatial similarity of point cloud data.The similarity of coordinate values of 3D point cloud data is improved, which provides an important guarantee for sparse representation of 3D point cloud data.Secondly, considering the self-similarity between normalized point cloud data, this paper first proposes a dictionary training algorithm based on K-SVD to obtain sparse representation base of normalized point cloud data.The normalized point cloud data can be represented sparsely in an overcomplete dictionary.However, the traditional algorithm of over-complete dictionary training is not suitable for 3D point cloud data. Therefore, combining the spatial geometric characteristics of point cloud data, this paper proposes an algorithm for over-complete dictionary training of 3D point cloud data based on K-means.It lays a foundation for point cloud data coding and reconstruction based on compressed perception.Thirdly, under the guidance of the theory of compressed perception, based on the normalization of point cloud data and the sparse representation of signals, this paper proposes the observation coding based on in-dimension 1-point cloud data for over-complete dictionary sparse representation of three-dimensional point cloud data.In addition, a tri-norm minimization reconstruction model based on 1-machine observation and a reconstruction model based on TV method are proposed.The experimental results show that the 3D point cloud data processing method presented in this paper has good results and provides a new way of thinking for perceptual 3D point cloud data coding and reconstruction. It has higher innovative and practical value.
【學(xué)位授予單位】：北京工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.7

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