Defining Point Cloud Boundaries Using Pseudopotential Scalar Field Implicit Surfaces
Defining Point Cloud Boundaries Using Pseudopotential Scalar Field Implicit Surfaces
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DOI：
10.1109/icip46576.2022.9897175
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Ethan Payne;Amanda Fernandez
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文献类型：
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作者：
Ethan Payne;Amanda Fernandez
在有噪声的三维点云中识别光滑且有意义的目标边界是一个挑战。而不是依赖于云本身的点，我们确定了一个光滑的隐式表面来表示云的边界。通过使用语义信息伪势函数构造标量场，我们取任意分辨率等面，并对伪势场的二维切片应用标准计算机视觉形态学变换和边缘检测。当重新组合时，这些切片组成一个新的点云，表示由所选等值面确定的对象的3D边界。我们的方法利用了2D视觉工具的强度和可访问性来识别不明确的3D物体的平滑和语义上重要的边界，并且还提供了一个包含关于物体内部结构的洞察力的连续标量场。我们的方法为三维边界识别提供了一个强大且易于实现的管道，特别是在已经存在伪势函数的自然候选者的领域。
Identifying smooth and meaningful object boundaries of noisy 3D point-clouds presents a challenge. Rather than rely on the points of the cloud itself, we identify a smooth implicit surface to represent the boundary of the cloud. By constructing a scalar field using a semantically-informative pseudopotential function, we take an arbitrary-resolution iso-surface and apply standard computer vision morphological transformations and edge detection on 2D slices of the pseudopotential field. When recombined, these slices comprise a new point-cloud representing the 3D boundary of the object as determined by the chosen isosurface. Our method leverages the strength and accessibility of 2D vision tools to identify smooth and semantically significant boundaries of ill-defined 3D objects, and additionally provides a continuous scalar field containing insight regarding the internal structure of the object. Our method enables a powerful and easily implementable pipeline for 3D boundary identification, particularly in domains where natural candidates for pseudopotential functions are already present.