基于图像辅助的三维模型形状检索

随着3D建模技术的快速发展,互联网上可用的3D模型库出现了爆炸式增长,越来越多的3D模型可以方便地通过网络下载使用.这直接促使了3D形状检索技术的发展,即给出特定的搜索信息,要求系统搜索出符合要求的、相似的3D模型.文章提出了一种新的3D形状检索方法,以3D模型作为输入,系统将会从模型数据库中自动检索出与输入形状最相似的模型.对于给定的输入模型以及数据库中的每一个模型,首先由计算机生成多幅在不同视角下的2D草图;然后,应用Gabor滤波器对每一幅2D草图提取图像上的局部特征,并对特征进行量化,从而得到代表该图像特征的直方图,这样对于每一个3D模型将得到多个代表该模型的直方图;最终,通过对比两个模型之间直方图的相似性,可以得到它们的相似性值,从而检索出与输入模型最相似的模型.文章所提出的方法通过采取2D图像分析方法提取能反映3D模型的特征并计算出模型之间的相似性值.经过测试,在一些公开的数据集上得到了较好的效果.

Image-Assisted 3D Model Shape Retrieval

In recent years, due to the rapid development of 3D modeling technology, 3D model databases have been increasingly available on the Internet. More and more 3D models can be easily downloaded through the Internet. This has directly led to the development of 3D shape retrieval technology, in which the system needs to return a similar 3D model according to the user requirement. This paper presents a new 3D shape retrieval method, which takes a 3D model as input and the system automatically returns some models that are most similar to the input shape from the model database. For a given input model and every model in the database, first, generated a magnitude of 2D sketch images of the model from different perspectives by the computer. Next, foreach 2D sketch image generated, the algorithm applies Gabor filter to extract the local features of the image, and quantifies the features in order to obtain a histogram representing the sketch image. For each 3D model, we then obtain a number of histograms representing the model. Thus, by comparing the histograms of every two models, we can compute the similarity value between the two models, and so retrieve the most similar shape to the input shape. In brief, the proposed method is capable of extracting effective features of 3D model through 2D image analysis method and evaluating the similarity between models. Experimental results show that the proposed algorithm performs well on some public datasets.