We present a new method for real-time non-rigid dense correspondence between
point clouds based on structured shape construction. Our method, termed Deep
Point Correspondence (DPC), requires a fraction of the training data compared
to previous techniques and presents better generalization capabilities. Until
now, two main approaches have been suggested for the dense correspondence
problem. The first is a spectral-based approach that obtains great results on
synthetic datasets but requires mesh connectivity of the shapes and long
inference processing time while being unstable in real-world scenarios. The
second is a spatial approach that uses an encoder-decoder framework to regress
an ordered point cloud for the matching alignment from an irregular input.
Unfortunately, the decoder brings considerable disadvantages, as it requires a
large amount of training data and struggles to generalize well in cross-dataset
evaluations. DPC's novelty lies in its lack of a decoder component. Instead, we
use latent similarity and the input coordinates themselves to construct the
point cloud and determine correspondence, replacing the coordinate regression
done by the decoder. Extensive experiments show that our construction scheme
leads to a performance boost in comparison to recent state-of-the-art
correspondence methods. Our code is publicly available at
this https URL

提出一种新的实时非刚性稠密点云对应的方法 Deep Point Correspondence，该方法基于结构形状构造，需要比以往技术少得多的训练数据并且具有更好的泛化能力。