一个相关反馈三维模型深度图像检索算法

基于图像的三维模型检索比形状特征和拓扑结构特征具有更易于实现且检索效果好等优点,但现有基于图像的三维模型检索方法存在一些问题,如没有考虑三维深度信息、所提特征不能全部表达三维模型信息且不能实现用户交互操作等。对此提出一种基于深度图像的三维模型相关反馈检索算法:首先提取三维模型深度图像边界方向直方图和Zernike矩特征;然后利用特征距离度量进行三维模型检索,并采用相关反馈技术实现分类器的构造和模型的标注;最后利用调整好的权值分类器进行检索和标注。实验表明,该算法实现了三维模型的相关反馈检索和标注,提高了检索的精确性,并增强了系统的实用性,为进一步进行语义检索打下基础。     

基于深度卷积神经网络的三维模型检索

为了更有效地利用三维模型数据集进行特征的自主学习,提出一种使用自然图像作为输入源,以三维模型的较优视图集为基础,通过深度卷积神经网络的训练提取深度特征用于检索的三维模型检索方法。首先,从多个视点对三维模型进行视图提取,并根据灰度熵的排序选取较优视图;然后,通过深度卷积神经网络对视图集进行训练,从而提取较优视图的深度特征并进行降维,同时,对输入的自然图像提取边缘轮廓图,经过相似度匹配获得一组三维模型;最后,基于检索结果中同类模型总数占检索列表长度的比例对列表进行重排序,从而获得最终的检索结果。实验结果表明,该算法能够有效利用深度卷积神经网络对三维模型的视图进行深度特征提取,同时降低了输入源的获取难度,有效提高了检索效果。     

基于深度学习的三维模型检索算法综述

近年来,深度学习被广泛应用于各个领域并取得了显著的进展,如何利用深度学习高效管理呈爆炸式增长的三维模型一直是一个研究热点。本文介绍了发展至今主流的基于深度学习的三维模型检索算法,并根据实验得出的算法性能评估分析了其优缺点。根据检索任务的不同,可将主要的三维模型检索算法分为两类：（1）基于模型的三维模型检索方法,即检索对象与被检索对象都是三维模型,按照对三维模型的表示方式不同,可进一步分为基于体素、基于点云和基于视图的方法;（2）基于二维图像的跨域三维模型检索方法,即检索对象是二维图像,被检索对象是三维模型,包括基于二维真实图像和基于二维草图的三维模型检索方法。最后,对基于深度学习的三维模型检索算法目前存在的问题进行分析和讨论,并展望未来发展的新方向。     

一种基于正交视图的三维模型多特征匹配算法

针对现今多数三维检索算法在匹配精度、检索速度以及算法复杂度三者难以相互兼顾的问题,文中提出一种基于正交视图的三维模型多特征匹配算法。首先对被检索三维模型的6个正视角进行投影,获得6个正视图像;接着分别用灰度级图像来描述各个正视图特征,并根据投影后模型各面的分布情况填补顶点与面片之间的间隙;然后动态提取每个灰度级图像的投影直方图特征和Zernike矩特征;最后在分析各特征的优缺点的基础上,融合多个特征来匹配三维模型的相似度。与传统LFD算法和D2算法进行对比实验,结果表明,文中算法能较好地提高对三维模型检索的查全率与查准率,具有计算量小、匹配精度高、运行速度快的优点。     

基于特征线条的三维模型检索方法

为了避免在三维模型检索中对输入源的限制,提出一种以自然图像为输入源、基于特征线条的三维模型检索方法.首先基于最优视点提取算法训练并获取三维模型较优视点集;然后在较优视点集下渲染三维模型混合轮廓线视图,并为各视点混合轮廓线视图提取Gabor边缘响应特征,建立特征库;最后对输入的自然图像提取相同的边缘响应特征,采用视觉词袋方法从特征库中检索相似模型,并根据相似度排序.实验结果表明,该方法利用自然图像与模型特征线视图的边缘相似性实现三维模型检索,降低了退化视图与自然图像纹理对三维模型检索的干扰,符合人类视觉辨识三维物体的机理,具有良好的检索效果.     

基于卷积神经网络和投票机制的三维模型分类与检索

针对现有基于深度学习的三维模型多视图分类算法利用最大池化、平均池化等像素级运算完成视图信息的融合,可能造成模型有益信息淹没和混淆的问题,提出一种基于卷积神经网络和投票机制的三维模型分类检索算法.首先将三维模型转化为一组二维视图,然后基于丰富的数字图像库ImageNet和成熟的图像深度学习模型CaffeNet完成二维视图的分类,最后利用加权投票的方式完成三维模型的分类;同时基于投票机制,提出4种三维模型距离度量算法,支持三维模型的检索.将文中算法应用于刚性三维模型库ModelNet10,ModelNet40,非刚性三维模型库SHREC10, SHREC11和SHREC15中,分类准确率分别为93.18%, 93.07%, 99.5%, 99.5%和99.4%,检索性能突出;并通过实验验证该算法的有效性.     

结合自适应融合网络与哈希的图像检索算法

卷积神经网络因其对图像识别准确率高而在图像检索领域备受青睐,但处理大规模数据集时,基于卷积神经网络提取的深度特征维度高,容易引发"维度灾难".针对图像检索中深度特征维度高的问题,提出一种基于自适应融合网络特征提取与哈希特征降维的图像检索算法.由于传统哈希处理高维特征复杂度高,因此本文在卷积神经网络中加入自适应融合模块对特征进行重新整合,增强特征表征能力的同时降低特征维度;然后应用稀疏化优化算法对深度特征进行第2次降维,并通过映射获得精简的哈希码;最后,实验以Inception网络作为基础模型,在数据集CIFAR-10和ImageNet上进行了丰富的实验.实验结果表明,该算法能有效提高图像检索效率.     

视觉图像相似性在三维模型相似性比较中的应用

提出一种基于视觉的三维模型相似性比较算法.首先计算三维模型的带深度信息的正交平面投影图像,然后采用Zernike描述子和Reeb图比较这些正交投影图像的形状相似性,最后通过正交投影图像的形状相似性来获得三维模型的相似性.实验结果表明：该算法具有较好的三维模型检索准确性,并且对坐标系旋转变换、模型噪声、网格简化和细分具有较好的鲁棒性.     

基于视觉面距离的三维模型检索

研究计算机视觉图像优化问题,针对目前三维模型检索较低的的检索精度,为改善检索结果与人视觉上保持一致,减少计算量,提出一种新的基于视觉面距离的模型检索算法。利用模型的整体特征;根据表层视觉特性构建视觉面距离模型将三维模型映射到二维模型;由于二维傅立叶算法不仅对噪音具有很好的鲁棒性,而且对几何变换具有不变性。采用二维傅立叶变换提取二维模型特征向量对特征进行融合,从而有效地评估三维模型之间的相似度。进行仿真分析,仿真结果表明,改进方法较传统其他三维模型检索有更高的精度,并与人视觉的一致性。     

基于深度学习的SIFT图像检索算法

深度学习作为一个新的机器学习方向,被应用到计算机视觉领域上成效显著.为了解决分布式的尺度不变特征转换（Scale-Invariant Feature Transform,SIFT）算法效率低和图像特征提取粗糙问题,提出一种基于深度学习的SIFT图像检索算法.算法思想：在Spark平台上,利用深度卷积神经网络（Convolutional Neural Network,CNN）模型进行SIFT特征抽取,再利用支持向量机（Support Vector Machine,SVM）对图像库进行无监督聚类,然后再利用自适应的图像特征度量来对检索结果进行重排序,以改善用户体验.在Corel图像集上的实验结果显示,与传统SIFT算法相比,基于深度学习的SIFT图像检索算法的查准率和查全率大约提升了30个百分点,检索效率得到了提高,检索结果图像排序也得到了优化.     

A systematic approach for 2D-image to 3D-range registration in urban environments

The photorealistic modeling of large-scale objects, such as urban scenes, requires the combination of range sensing technology and digital photography. In this paper, we attack the key problem of camera pose estimation, in an automatic and efficient way. First, the camera orientation is recovered by matching vanishing points (extracted from 2D images) with 3D directions (derived from a 3D range model). Then, a hypothesis-and-test algorithm computes the camera positions with respect to the 3D range model by matching corresponding 2D and 3D linear features. The camera positions are further optimized by minimizing a line-to-line distance. The advantage of our method over earlier work has to do with the fact that we do not need to rely on extracted planar facades, or other higher-order features; we are utilizing low-level linear features. That makes this method more general, robust, and efficient. We have also developed a user-interface for allowing users to accurately texture-map 2D images onto 3D range models at interactive rates. We have tested our system in a large variety of urban scenes.     

基于相交颜色直方图的图像检索算法

提出一种基于相交颜色直方图的图像检索算法,该算法首先计算示例图像和被检索图像的相交颜色直方图,然后计算两直方图的相交距离,同时考虑各色在两图像中的分布情况。实验结果表明该算法不受背景颜色的影响。     

Combining topological and view-based features for 3D model retrieval

With the rapidly increasing of 3D models, the 3D model retrieval methods have been paid significant research attention. Most of the existing methods focus on taking advantage of one kind of feature. These methods can not achieve ideal retrieval results for different classes of 3D models. In this paper, we propose a novel 3D model retrieval algorithm by combining topological and view-based features. To preserve the topological structure of the 3D model, a multiresolutional reeb graph (MRG) is constructed according to the salient topological points. The view-based features are extracted from the images, which are rendered at each of the topological points. To preserve the spatial structure information of the images, we modify the bag-of-features (BOF) method by using the combined shell-sector model. We take the view-based features as the attribute information of the corresponding MRG nodes. The comparison between two 3D models is transformed to the problem of computing the similarity of the corresponding MRGs. Finally, we calculate the similarity between the query model and the models in the databases by adapting the earth mover distance method. Experimental results on two standard benchmarks show that our algorithm can achieve satisfactory retrieval performance.     

Spherical Correlation of Visual Representations for 3D Model Retrieval

In recent years we have seen a tremendous growth in the amount of freely available 3D content, in part due to breakthroughs for 3D model design and acquisition. For example, advances in range sensor technology and design software have dramatically reduced the manual labor required to construct 3D models. As collections of 3D content continue to grow rapidly, the ability to perform fast and accurate retrieval from a database of models has become a necessity. At the core of this retrieval task is the fundamental challenge of defining and evaluating similarity between 3D shapes. Some effective methods dealing with this challenge consider similarity measures based on the visual appearance of models. While collections of rendered images are discriminative for retrieval tasks, such representations come with a few inherent limitations such as restrictions in the image viewpoint sampling and high computational costs. In this paper we present a novel algorithm for model similarity that addresses these issues. Our proposed method exploits techniques from spherical signal processing to efficiently evaluate a visual similarity measure between models. Extensive evaluations on multiple datasets are provided.     

Prediction of Individual Non-Linear Aging Trajectories of Faces

Represented in a Morphable Model, 3D faces follow curved trajectories in face space as they age. We present a novel algorithm that computes the individual aging trajectories for given faces, based on a non-linear function that assigns an age to each face vector. This function is learned from a database of 3D scans of teenagers and adults using support vector regression. To apply the aging prediction to images of faces, we reconstruct a 3D model from the input image, apply the aging transformation on both shape and texture, and then render the face back into the same image or into images of other individuals at the appropriate ages, for example images of older children. Among other applications, our system can help to find missing children.