基于網(wǎng)格分割和層級特征的三維模型檢索方法研究
發(fā)布時間：2018-07-15 11:43
【摘要】：隨著計算機軟、硬件的更新?lián)Q代、互聯(lián)網(wǎng)的快速普及和計算機圖形學(xué)理論的不斷完善，在影視動漫、仿真、生物醫(yī)學(xué)等領(lǐng)域，3D網(wǎng)格建模技術(shù)已經(jīng)被廣泛應(yīng)用。以三維立體取代二維平面，用虛擬模擬現(xiàn)實的3D建模技術(shù)帶領(lǐng)人們步入了立體世界。然而，建模高度逼真的3D網(wǎng)格模型十分費時費力，假如能高效復(fù)用網(wǎng)絡(luò)資源中現(xiàn)存的3D網(wǎng)格模型，就可以大大減少新模型的建模工作，如何從海量3D網(wǎng)格模型資源中快速查找并精確的篩選出所需模型成為了必需解決的問題。因此，研究3D網(wǎng)格模型檢索方法工作是一項具有重要現(xiàn)實意義的工作。 基于內(nèi)容的檢索是當(dāng)前三維模型檢索的一個熱點研究方向，該方法根據(jù)網(wǎng)格模型的材質(zhì)、紋理、空間結(jié)構(gòu)等信息計算并提取其形狀特征作為該網(wǎng)格模型的唯一標(biāo)識，然后計算目標(biāo)模型與模型數(shù)據(jù)庫中待查模型的特征差值，將差值最小的前N個模型作為結(jié)果輸出，實現(xiàn)三維模型的檢索。因此，三維模型檢索的關(guān)鍵在于如何提取網(wǎng)格模型的形狀特征。 本文對檢索系統(tǒng)結(jié)構(gòu)進(jìn)行了分析并對現(xiàn)有的特征提取算法進(jìn)行了總結(jié)，并提出改進(jìn)的檢索方法。主要做了以下三方面工作： 1.分析了三維模型檢索方法的研究背景及意義，介紹了模型檢索的流程和系統(tǒng)的框架結(jié)構(gòu)，總結(jié)了模型檢索中的關(guān)鍵技術(shù)。當(dāng)前常用的特征提取算法種類繁多，缺少統(tǒng)一分類標(biāo)準(zhǔn)，本文對其大致分類，并分別進(jìn)行闡述。 2.針對現(xiàn)有檢索算法在提取形狀特征時僅整體計算模型信息、忽略模型局部信息、未充分利用網(wǎng)格模型特征點等問題，提出一種基于網(wǎng)格分割的三維模型檢索方法，并將之應(yīng)用于三維模型檢索系統(tǒng)。 首先，對多種信號值計算方法進(jìn)行比較，獲得穩(wěn)定度較高的網(wǎng)格三角形平坦度，并將其作為高度函數(shù)應(yīng)用到改進(jìn)算法中，，然后，對網(wǎng)格模型進(jìn)行預(yù)處理，采用多維標(biāo)度分析MDS（multi-dimension scaling）描述模型姿勢不變性并提取模型顯著特征點，采用特征點作為種子點指導(dǎo)網(wǎng)格分割。分割結(jié)束后，為避免出現(xiàn)過分割區(qū)域，采用多輪動態(tài)加權(quán)完成從局部到全局的合并使分割結(jié)果更加合理。最后，對三維模型的局部信息進(jìn)行特征提取建立特征樹，比較樹的匹配程度檢索相似三維模型。通過對幾種目標(biāo)模型進(jìn)行檢索，分析檢索方法的合理性與有效性，進(jìn)一步改進(jìn)算法思想、精練算法步驟，設(shè)計程序結(jié)構(gòu)并編寫算法。實驗驗證，算法較好地利用了模型局部信息，檢索速度快，在相同查全率下具有較高的查準(zhǔn)率。 3.目前多數(shù)三維模型檢索算法只采用單一形狀特征表述，然而單一形狀特征描述能力終歸有限，只能有針對性的描述網(wǎng)格模型的某些性質(zhì)，并不能適應(yīng)所有模型的檢索，有一定的局限性。因此提出層級特征檢索，研究多種特征按照層次結(jié)構(gòu)進(jìn)行匹配，同時結(jié)合用戶反饋機制，動態(tài)的計算與模型數(shù)據(jù)庫中模型匹配時的權(quán)值，利用用戶反饋的方法，對訓(xùn)練中的特征權(quán)值進(jìn)行動態(tài)調(diào)整，得到不同的閾值，最后在網(wǎng)格模型檢索階段，先利用第一類形狀特征與閾值比較，再選擇一個權(quán)值與第二特征進(jìn)行比較計算，對模型數(shù)據(jù)庫中模型進(jìn)行對比，實現(xiàn)三維模型檢索。
[Abstract]:With the updating and replacement of computer software and hardware, the rapid popularization of the Internet and the continuous improvement of the theory of computer graphics, 3D mesh modeling technology has been widely used in the fields of animation, animation, simulation, biomedicine and so on. In order to replace the two-dimensional plane with three-dimensional stereotyping, the 3D modeling technology of virtual mode pseudo reality has led people into the stereoscopic world. However, the highly realistic modeling of 3D grid model is very time-consuming and time-consuming. If we can reuse the existing 3D grid model in the network resources, it can greatly reduce the modeling work of the new model. It is a necessary problem to find out and select the required model from the massive 3D grid model resources. Therefore, research 3 D grid model retrieval is an important practical work.
Content based retrieval is a hot research direction in current 3D model retrieval. This method calculates and extracts its shape characteristics according to the material, texture and spatial structure of the grid model, and then calculates the difference between the target model and the model data base in the model data base, and minimization of the difference. The former N models are output as the result, and the retrieval of 3D models is realized. Therefore, the key of 3D model retrieval is how to extract the shape features of mesh models.
In this paper, the structure of the retrieval system is analyzed and the existing feature extraction algorithms are summarized, and the improved retrieval methods are put forward. The following three aspects are mainly done:
1. analyze the research background and significance of 3D model retrieval method, introduce the process of model retrieval and frame structure of the system, and summarize the key technologies in model retrieval. There are many kinds of feature extraction algorithms in common use and lack of unified classification standard.
2. a 3D model retrieval method based on grid segmentation is proposed and applied to 3D model retrieval system for the existing retrieval algorithms that only calculate model information, ignore the local information of the model and make full use of the feature points of the grid model.
First, a variety of method of signal value calculation is compared, and the grid flatness with high stability is obtained, and it is applied to the improved algorithm as a height function. Then, the mesh model is preprocessed, and the multidimensional scaling analysis MDS (multi-dimension scaling) is used to describe the invariance of the model posture and the significant feature points are extracted. The feature points are used as seed points to guide the mesh segmentation. After the segmentation, in order to avoid the segmentation area, the combination of multi wheel dynamic weighting from local to global makes the segmentation result more reasonable. Finally, the feature tree is extracted from the feature extraction of the local information of the 3D model, and the matching degree of the tree is compared to retrieve the similar 3D model. By retrieving several target models, analyzing the rationality and validity of the retrieval method, further improving the algorithm thought, refining the algorithm steps, designing the program structure and writing algorithms. The experiment proves that the algorithm makes good use of the local information of the model, the retrieval speed is fast, and the precision rate is higher under the same recall rate.
3. at present, most of the 3D model retrieval algorithms are only expressed in single shape features. However, the ability of single shape feature description is limited. It can only describe some properties of the grid model, and can not adapt to all models. Therefore, the hierarchical feature retrieval is proposed, and a variety of features are studied in accordance with hierarchical nodes. The structure is matched, and the user feedback mechanism is combined to dynamically calculate the weights of the model matching in the model database. The user feedback method is used to dynamically adjust the feature weights in the training, and different thresholds are obtained. Finally, in the grid model retrieval stage, the first type of shape features is compared with the threshold value, then one more choice is selected. The weights are compared with the second features, and the models in the model database are compared to realize the 3D model retrieval.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.41
[Abstract]:With the updating and replacement of computer software and hardware, the rapid popularization of the Internet and the continuous improvement of the theory of computer graphics, 3D mesh modeling technology has been widely used in the fields of animation, animation, simulation, biomedicine and so on. In order to replace the two-dimensional plane with three-dimensional stereotyping, the 3D modeling technology of virtual mode pseudo reality has led people into the stereoscopic world. However, the highly realistic modeling of 3D grid model is very time-consuming and time-consuming. If we can reuse the existing 3D grid model in the network resources, it can greatly reduce the modeling work of the new model. It is a necessary problem to find out and select the required model from the massive 3D grid model resources. Therefore, research 3 D grid model retrieval is an important practical work.
Content based retrieval is a hot research direction in current 3D model retrieval. This method calculates and extracts its shape characteristics according to the material, texture and spatial structure of the grid model, and then calculates the difference between the target model and the model data base in the model data base, and minimization of the difference. The former N models are output as the result, and the retrieval of 3D models is realized. Therefore, the key of 3D model retrieval is how to extract the shape features of mesh models.
In this paper, the structure of the retrieval system is analyzed and the existing feature extraction algorithms are summarized, and the improved retrieval methods are put forward. The following three aspects are mainly done:
1. analyze the research background and significance of 3D model retrieval method, introduce the process of model retrieval and frame structure of the system, and summarize the key technologies in model retrieval. There are many kinds of feature extraction algorithms in common use and lack of unified classification standard.
2. a 3D model retrieval method based on grid segmentation is proposed and applied to 3D model retrieval system for the existing retrieval algorithms that only calculate model information, ignore the local information of the model and make full use of the feature points of the grid model.
First, a variety of method of signal value calculation is compared, and the grid flatness with high stability is obtained, and it is applied to the improved algorithm as a height function. Then, the mesh model is preprocessed, and the multidimensional scaling analysis MDS (multi-dimension scaling) is used to describe the invariance of the model posture and the significant feature points are extracted. The feature points are used as seed points to guide the mesh segmentation. After the segmentation, in order to avoid the segmentation area, the combination of multi wheel dynamic weighting from local to global makes the segmentation result more reasonable. Finally, the feature tree is extracted from the feature extraction of the local information of the 3D model, and the matching degree of the tree is compared to retrieve the similar 3D model. By retrieving several target models, analyzing the rationality and validity of the retrieval method, further improving the algorithm thought, refining the algorithm steps, designing the program structure and writing algorithms. The experiment proves that the algorithm makes good use of the local information of the model, the retrieval speed is fast, and the precision rate is higher under the same recall rate.
3. at present, most of the 3D model retrieval algorithms are only expressed in single shape features. However, the ability of single shape feature description is limited. It can only describe some properties of the grid model, and can not adapt to all models. Therefore, the hierarchical feature retrieval is proposed, and a variety of features are studied in accordance with hierarchical nodes. The structure is matched, and the user feedback mechanism is combined to dynamically calculate the weights of the model matching in the model database. The user feedback method is used to dynamically adjust the feature weights in the training, and different thresholds are obtained. Finally, in the grid model retrieval stage, the first type of shape features is compared with the threshold value, then one more choice is selected. The weights are compared with the second features, and the models in the model database are compared to realize the 3D model retrieval.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.41
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1 崔晨e
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