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

在这个项目中，我们基于对 3D 形状的客观敏感性的概念，提出了一种新的 3D 对象模型表示及其应用方法，可以处理多视图和多分辨率范围数据、立体图像、纹理图像和视频客观灵敏度意味着当物体的 3D 形状以一定的细节程度呈现时，可以获得多少关于物体的信息。这项工作包含以下子目标：第一个目标是整合以不同分辨率测量的距离数据。考虑两个不同的观点确定每个测量点的确定性，然后通过使用网格自适应（例如细分和抽取技术）生成特定分辨率级别的 3D 形状模型。一些实验结果证明已经实现了目标。第二个目标是提出统计纹理。一种分析方法，可以从户外场景中拍摄的自然图像中提取一些 3D 比例因子，而在户外场景中，如果不使用激光测距仪等昂贵的工具来执行 3D 测量，则该方法基于分层线性判别分析可以对从高阶局部自相关函数计算出的一些特征进行分类，已经证明该方法可用于从纹理图像中提取3D比例因子，我们还构建了一个可以控制平移的主动立体视觉系统。相机的倾斜和变焦作为机器人的智能视觉系统，该系统可以在本地系统中收集场景的一些可用信息，而无需任何外部请求。第三个目标是开发一种新的3D网格之间的交叉参数化技术。模型可以可用于各种 3 维数字几何处理（DGP）。提出的交叉参数化方法基于作者开发的最小二乘网格技术和自组织变形模型（SDM）。直接将一个 3D 模型的纹理和运动属性转换为另一个模型，或者在两个 3D 网格模型之间生成中间模型，作为暂时呈现这些中间模型的 3D 变形的一种特殊应用，我们做了几个心理实验，让受试者回答。当他们识别出中间形状是什么时，他们的结果表明，他们的认知过程取决于源对象模型和目标对象模型的组合。最后，作为所提出方法的其他应用，我们开发了一种基于面部增强的系统。在SDM上，基于机器学习和聚类算法的3D运动合成系统，以及基于Less分解方法的3D视频重建系统。