Floating Scale Surface Reconstruction
Floating Scale Surface Reconstruction
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DOI：
10.1145/2601097.2601163
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发表时间：
2014-07-01
影响因子：
6.2
通讯作者：
Goesele, Michael
中科院分区：
文献类型：
--
作者：
Fuhrmann, Simon;Goesele, Michael
从真实世界几何对象或场景获取的任何采样点都表示有限的曲面区域，而不仅仅是单个曲面点。因此，样本具有固有的规模，非常有价值的信息，这对高质量的重建至关重要。我们介绍了一种新的方法，从定向的、可缩放的样本点重建曲面，该方法操作在大的、冗余的和潜在的噪声点集上。该方法利用一种简单而有效的数学公式，将隐函数构造为紧支承基函数之和。隐函数在空间上具有连续的“浮动”尺度，无需任何预处理即可方便地求值。最后的曲面被提取为隐函数的零水平集。该方法的关键特性之一是，即使对于复杂的、混合比例的数据集，它实际上也是无参数的。此外，我们的方法易于实现，可伸缩，不需要任何全局操作。我们在广泛的数据集上对我们的方法进行了评估，与流行的经典方法和当前方法相比，我们的方法更具优势。
Any sampled point acquired from a real-world geometric object or scene represents a finite surface area and not just a single surface point. Samples therefore have an inherent scale, very valuable information that has been crucial for high quality reconstructions. We introduce a new method for surface reconstruction from oriented, scale-enabled sample points which operates on large, redundant and potentially noisy point sets. The approach draws upon a simple yet efficient mathematical formulation to construct an implicit function as the sum of compactly supported basis functions. The implicit function has spatially continuous "floating" scale and can be readily evaluated without any preprocessing. The final surface is extracted as the zero-level set of the implicit function. One of the key properties of the approach is that it is virtually parameter-free even for complex, mixed-scale datasets. In addition, our method is easy to implement, scalable and does not require any global operations. We evaluate our method on a wide range of datasets for which it compares favorably to popular classic and current methods.