%0 Journal Article
%T 三维重建中的多模型融合:克服光照和尺度影响<br>Multiple model fusion in 3-D reconstruction: Illumination and scale invariance
%A 陈宝华
%A 邓磊
%A 段岳圻
%A 陈志祥
%A 周杰
%J 清华大学学报（自然科学版）
%D 2016
%R 10.16511/j.cnki.qhdxxb.2016.21.046
%X 互联网图像三维可视化通常使用运动恢复结构方法将互联网图像重构成为三维点云，用于支持用户在三维空间中自由移动观察三维点云和图像。但由于同一场景互联网图像间光照条件差异巨大，传统方法往往不会重构成唯一的三维点云，而是依照光照条件的分布，构建成多个独立的点云。该文提出了一种三维点云配准框架，将这些因为光照差异而分离的点云融合成为统一的点云。首先利用点云的三维几何特征而非二维图像特征描述点云，克服了光照差异对配准的影响。其次提出了一种克服尺度差异的配准方法，以解决不同尺度点云的匹配问题。在两个数据集上的实验证明了该方法的有效性。<br>Abstract：3-D internet photo visualization reconstructs objects in 3-D using structure information gained from the object's motion to give users motion experience. However, due to the large illumination difference between photographs on the Internet, traditional reconstruction methods cannot generate a single point cloud, but will generate multiple independent point clouds. This paper describes a 3-D model registration framework based on 3-D geometries that generates unified 3-D models from various illuminations to complete a structure from multiple models. The 3-D point cloud geometry is used instead of the 2-D features to overcome the influence of large illumination changes. Secondly, a scaled-PCA-ICP algorithm was then used to do the registration that can overcome the large scale variance between the two point clouds. Tests on two datasets show the effectiveness of this method.
%K 三维模型配准
%K 运动恢复结构
%K 尺度自适应主成分迭代最近点算法
%K &lt
%K br&gt
%K 3-D model registration
%K structure from motion
%K scaled-PCA-ICP
%U http://jst.tsinghuajournals.com/CN/Y2016/V56/I9/969