论文标题
从嘈杂的点云到完整的耳朵形状：无监督管道
From noisy point clouds to complete ear shapes: unsupervised pipeline
论文作者
论文摘要
耳朵是模型面孔中特别困难的区域，不仅是由于形状之间存在的非刚性变形，而且还因为处理检索到的数据时面临的挑战。获得良好模型的第一步是进行对应性进行全面扫描，但是与大多数面部区域相比，这些模型通常会呈现更高量的阻塞，噪声和异常值，因此需要特定的程序。因此，我们提出了一条完整的管道作为无上述问题的输入的3D点云，并在丢失的数据完成后作为通信中的数据集输出。我们提供了几种最先进的注册方法的比较，并为管道的一个步骤之一提出了一种新方法，并为我们的数据提供了更好的性能。
Ears are a particularly difficult region of the human face to model, not only due to the non-rigid deformations existing between shapes but also to the challenges in processing the retrieved data. The first step towards obtaining a good model is to have complete scans in correspondence, but these usually present a higher amount of occlusions, noise and outliers when compared to most face regions, thus requiring a specific procedure. Therefore, we propose a complete pipeline taking as input unordered 3D point clouds with the aforementioned problems, and producing as output a dataset in correspondence, with completion of the missing data. We provide a comparison of several state-of-the-art registration methods and propose a new approach for one of the steps of the pipeline, with better performance for our data.
