Research of 3D Image Acquisition and Its Consistent Representation for Sared-Type Real Image Space

面向未来的3D图像通信，我们开始研究共享空间通信的“多媒体氛围通信”，通过引入长、中、近距离视图的三层模型概念，采用利用室外景物的实际图像来设计3D图像空间的方法。远程和中距离视图不需要精确表示其3D结构，因此我们根据倾斜平面模型采用像舞台设置的设置表示来近似其3D结构。我们处理的方法，以产生一致的设置表示，用于描述从范围和纹理数据测量的激光扫描仪和位于多个视点的数码相机的长和mic-range的意见。生产这样的表示需要几种技术的发展：非线性平滑的原始范围数据，平面分割的范围数据，注册的多视点范围数据，集成的多视点设置表示和纹理映射到每个设置平面。在本文中，我们集中在平面分割和多视点数据配准。我们的平面分割方法是基于区域竞争的概念，可以准确地提取拟合平面的距离数据。我们的配准方法使用两个不同视点之间对应的分割平面的方程来确定它们之间的3D欧几里得变换。通过整合多个视点的多个设置表示，可以构造统一一致的设置表示。

Toward future 3D image communication, we have started studying the "Multimedia Ambiance Communication", a kind of shared-space communication, and adopted an approach to design the 3D-image space using actual images of outdoor scenery, by introducing the concept of the three-layer model of long-, mid- and short-range views. The long- and mid-range views do not require precise representation of their 3D structure, and hence we employ the setting representation like stage settings to approximate their 3D structure according to the slanting-plane-model. We deal with an approach to produce the consistent setting representation for describing long- and mic-range views from range and texture data measured with a laser scanner and a digital camera located at multiple viewpoints. The production of such a representation requires the development of several techniques: nonlinear smoothing of raw range data, plane segmentation of range data, registration of multi-viewpoint range data, integration of multi-viewpoint setting representations and texture mapping onto each setting plane. In this paper, we concentrate on the plane segmentation and the multi-viewpoint data registration. Our plane segmentation method is based on the concept of the region competition, and can precisely extract fitting planes from the range data. Our registration method uses the equations of the segmented planes corresponding between two different viewpoints to determine the 3D Euclidean transformation between them. A unifying consistent setting representation can be constructed by integrating multiple setting representations for multiple viewpoints.