In this paper, we propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds. The proposed approach is data-driven and adopts for the first time the transformer architecture in the registration task. Our method is general and applies to different settings. Given a fixed template with some desired properties (e.g. skinning weights or other animation cues), we can register raw acquired data to it, thereby transferring all the template properties to the input geometry. Alternatively, given a pair of shapes, our method can register the first onto the second (or vice-versa), obtaining a high-quality dense correspondence between the two. In both contexts, the quality of our results enables us to target real applications such as texture transfer and shape interpolation. Furthermore, we also show that including an estimation of the underlying density of the surface eases the learning process. By exploiting the potential of this architecture, we can train our model requiring only a sparse set of ground truth correspondences ($10\sim20\%$ of the total points). The proposed model and the analysis that we perform pave the way for future exploration of transformer-based architectures for registration and matching applications. Qualitative and quantitative evaluations demonstrate that our pipeline outperforms state-of-the-art methods for deformable and unordered 3D data registration on different datasets and scenarios.
翻译:在本文中,我们提出一个基于变压器的程序,以便有效登记非硬化 3D点云。 提议的方法是数据驱动的, 并首次在登记任务中采用变压器结构。 我们的方法是一般性的, 适用于不同的设置。 如果有一个固定的模板, 具有某些想要的属性( 如皮肤重量或其他动画提示), 我们就可以将原始获得的数据注册到它, 从而将所有模板属性转换到输入几何学中。 或者, 如果有两种形状, 我们的方法可以在第二个( 反向的) 上登记第一个( 10\ sim20 $ ), 获得两个系统之间的高质量密集通信。 在这两种情况下, 我们的结果的质量使我们能够瞄准真正的应用, 如质素转移和形状内插等。 此外, 我们还表明, 包含对表面基本密度的估计可以缓解学习过程。 通过利用这一架构的潜力, 我们可以对模型进行培训, 只需要一套稀少的地面对等( 10\ simm20 $ ) 。 拟议的模型和我们为未来探索基于变压的输管结构的定量和变压式数据注册和匹配应用的方法而进行不易化的定量评估的方法。 。 。 Q- 和匹配的定量的定量的模型和定量的模型应用。