论文标题
大规模评估形状吸引的邻里重量和邻里大小
A Large-Scale Evaluation of Shape-Aware Neighborhood Weights and Neighborhood Sizes
论文作者
论文摘要
在本文中，我们定义并评估了点集的社区加权方案。我们的权重考虑了几何形状，即正常信息。这使得获得的社区更加可靠，因为连接性也取决于点集的方向。我们利用sigmoid根据正常变化来定义权重。为了评估加权方案，我们求助于香农熵模型进行特征分类，这可以证明是我们的权重家庭的非分类。基于此模型，我们评估了大规模清洁和现实世界模型的加权术语。该评估提供了有关我们加权方案中最佳参数的选择的结果。此外，大规模评估还表明，处理模型时不应在全球范围内固定邻域大小。最后，我们强调了加权方案的适用性，以降级的应用上下文。
In this paper, we define and evaluate a weighting scheme for neighborhoods in point sets. Our weighting takes the shape of the geometry, i.e., the normal information, into account. This causes the obtained neighborhoods to be more reliable in the sense that connectivity also depends on the orientation of the point set. We utilize a sigmoid to define the weights based on the normal variation. For an evaluation of the weighting scheme, we turn to a Shannon entropy model for feature classification that can be proven to be non-degenerate for our family of weights. Based on this model, we evaluate our weighting terms on a large scale of both clean and real-world models. This evaluation provides results regarding the choice of optimal parameters within our weighting scheme. Furthermore, the large-scale evaluation also reveals that neighborhood sizes should not be fixed globally when processing models. Finally, we highlight the applicability of our weighting scheme withing the application context of denoising.
