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Surface modeling and analysis using range images: Smoothing, registration, integration, and segmentation.

机译:使用范围图像进行表面建模和分析:平滑,对齐,积分和分割。

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This dissertation presents a framework for 3D reconstruction and scene analysis, using a set of range images. The motivation for developing this framework came from the needs to reconstruct the surfaces of small mechanical parts in reverse engineering tasks, build a virtual environment of indoor and outdoor scenes, and understand 3D images.; The input of the framework is a set of range images of an object or a scene captured by range scanners. The output is a triangulated surface that can be segmented into meaningful parts. A textured surface can be reconstructed if color images are provided. The framework consists of surface smoothing, registration, integration, and segmentation.; Surface smoothing eliminates the noise present in raw measurements from range scanners. This research proposes area-decreasing flow that is theoretically identical to the mean curvature flow. Using area-decreasing flow, there is no need to estimate the curvature value and an optimal step size of the flow can be obtained. Crease edges and sharp corners are preserved by an adaptive scheme.; Surface registration aligns measurements from different viewpoints in a common coordinate system. This research proposes a new surface representation scheme named point fingerprint. Surfaces are registered by finding corresponding point pairs in an overlapping region based on fingerprint comparison.; Surface integration merges registered surface patches into a whole surface. This research employs an implicit surface-based integration technique. The proposed algorithm can generate watertight models by space carving or filling the holes based on volumetric interpolation. Textures from different views are integrated inside a volumetric grid.; Surface segmentation is useful to decompose CAD models in reverse engineering tasks and help object recognition in a 3D scene. This research proposes a watershed-based surface mesh segmentation approach. The new algorithm accurately segments the plateaus by geodesic erosion using fast marching method.; The performance of the framework is presented using both synthetic and real world data from different range scanners. The dissertation concludes by summarizing the development of the framework and then suggests future research topics.
机译:本文提出了一套利用距离图像进行3D重建和场景分析的框架。开发此框架的动机来自于在逆向工程任务中重建小型机械零件的表面,构建室内和室外场景的虚拟环境以及理解3D图像的需求。框架的输入是由范围扫描仪捕获的一组对象或场景的范围图像。输出是可以划分成有意义部分的三角化表面。如果提供彩色图像,则可以重建带纹理的表面。该框架包括表面平滑,配准,积分和分割。表面平滑消除了距离扫描仪原始测量中存在的噪声。这项研究提出了面积减少流量,理论上与平均曲率流量相同。使用面积减小的流量,无需估算曲率值,并且可以获得流量的最佳步长。折痕边缘和尖角通过自适应方案保留。表面配准可在一个共同的坐标系中对齐来自不同视角的测量值。这项研究提出了一种新的表面表示方案,称为点指纹。通过基于指纹比较在重叠区域中找到对应的点对来注册表面。表面整合将注册的表面补丁合并为整个表面。这项研究采用了基于表面的隐式集成技术。提出的算法可以通过空间雕刻或基于体积插值填充孔来生成水密模型。来自不同视图的纹理被集成在一个体积网格内。曲面分割可用于在逆向工程任务中分解CAD模型,并有助于在3D场景中识别对象。这项研究提出了一种基于分水岭的表面网格分割方法。新算法使用快速行进法通过测地线侵蚀精确地分割了高原。使用来自不同范围扫描仪的合成数据和真实数据来显示框架的性能。论文最后总结了框架的发展,然后提出了未来的研究主题。

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