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A study for scalable representation of 3D object models and its applications based on information sensitivity

基于信息敏感性的3D物体模型可扩展表示及其应用研究

基本信息

批准号：
17300033
负责人：
NAGAHASHI Hiroshi
金额：
$ 10.21万
依托单位：
Tokyo Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2005
资助国家：
日本
起止时间：
2005 至 2007
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-17300033/
关键词：
multi-resolution measurement 3D object representation objective sensitivity mesh adaptations analysis of scalable textures least-square mesh self-organizing 3D deformable model 3 dimensional video contents 情報感度特性 3次元物体モーフィング変形度主観的情

项目摘要

In this project, we propose a new method for 3D object model representation and its applications based on a concept of objective sensitivity to a 3D shape, which can deal with multi-view and multi-resolution range data, stereo images, texture images and videos. The objective sensitivity means how much information about the object can be obtained, when its 3D shape is presented in a certain level of detail. The work contains the following sub-goals : The first one is to integrate range data measured in different resolutions and from different view points by considering two certainties for each measured point, and then to generate a 3D shape model in a certain resolution level by using mesh adaptations like subdivision and decimation techniques. Some experimental results have proved that the goal has been achieved.The second goal is to propose a statistical texture analysis method that can extract some 3D scale factor from a natural image taken in an outdoor scene, where it is not so eas … More y to perform 3D measurement without using an expensive tool such as a laser range finder. The method is based on a hierarchical linear discriminant analysis that can classify some features calculated from higher-order local auto-correlation functions. It has been proved that the method is available for extracting 3D scale factor from texture images. We have also constructed an active stereo vision system that can control panning, tilting and zooming of the camera as an intelligent vision system of a robot. This system can gather some available information of a scene in the local system without any request from outside.The third goal is to develop a new cross-parameterization technique between 3D mesh models that can be used in various 3 dimensional Digital Geometry Processings (DGP). The cross-parameterization method proposed is based on a least-square mesh technique and a self-organizing deformable model(SDM) developed by the authors. This technique has enabled us to transfer texture and motion attributes of a 3D model to another one directly, or to generate intermediate models between two 3D mesh models. As a special application of the 3D morphing that presents these intermediate models temporally, we did several psychological experiments where subjects answer their results when they recognize what an intermediate shape presented is. These experiments have shown that their cognitive processes depend on the combination of the source and target object models.Finally, as other applications of the proposed method, we have developed a facial enhancement system based on the SDM, a 3D motion synthesis system based on a machine learning and a clustering algorithm, and a 3D video reconstruction system based on a factorization method. Less

在本课题中，我们提出了一种新的三维物体模型表示方法及其应用，该方法基于物体对三维形状的客观灵敏度概念，可以处理多视图和多分辨率范围数据、立体图像、纹理图像和视频。物镜灵敏度是指当物体的三维形状以一定程度的细节呈现时，可以获得多少关于物体的信息。该工作包括以下几个子目标：第一，通过考虑每个测量点的两个确定性，对不同分辨率和不同视点测量的距离数据进行整合，然后利用细分和抽取等网格自适应技术生成一定分辨率水平的三维形状模型。一些实验结果证明，该目标已经实现。第二个目标是提出一种统计纹理分析方法，该方法可以从室外场景中拍摄的自然图像中提取一些3D比例因子，在室外场景中，无需使用昂贵的工具（如激光测距仪）即可进行3D测量。该方法基于层次线性判别分析，可以对高阶局部自相关函数计算的特征进行分类。实验证明，该方法可用于纹理图像的三维比例因子提取。我们还构建了一个主动立体视觉系统，可以控制摄像机的平移、倾斜和变焦，作为机器人的智能视觉系统。该系统可以在不需要外部请求的情况下，在本地系统中收集到一些场景的可用信息。第三个目标是开发一种新的三维网格模型之间的交叉参数化技术，该技术可用于各种三维数字几何处理（DGP）。交叉参数化方法是基于最小二乘网格技术和自组织变形模型（SDM）提出的。这种技术使我们能够直接将纹理和运动属性的3D模型转移到另一个模型，或者在两个3D网格模型之间生成中间模型。作为3D变形的一个特殊应用，我们暂时呈现了这些中间模型，我们做了几个心理实验，让受试者在认识到呈现的中间形状是什么时回答他们的结果。这些实验表明，他们的认知过程依赖于源对象和目标对象模型的结合。最后，作为该方法的其他应用，我们开发了基于SDM的面部增强系统，基于机器学习和聚类算法的3D运动合成系统，以及基于因式分解方法的3D视频重建系统。少