Abstract
We present a novel, end-to-end approach to align CAD models to an 3D scan of a scene, enabling transformation of a noisy, incomplete 3D scan to a compact, CAD reconstruction with clean, complete object geometry. Our main contribution lies in formulating a differentiable Procrustes alignment that is paired with a symmetry-aware dense object correspondence prediction. To simultaneously align CAD models to all the objects of a scanned scene, our approach detects object locations, then predicts symmetry-aware dense object correspondences between scan and CAD geometry in a unified object space, as well as a nearest neighbor CAD model, both of which are then used to inform a differentiable Procrustes alignment. Our approach operates in a fully-convolutional fashion, enabling alignment of CAD models to the objects of a scan in a single forward pass. This enables our method to outperform state-of-the-art approaches by $19.04\%$ for CAD model alignment to scans, with $\approx 250\times$ faster runtime than previous data-driven approaches.
Abstract (translated)
我们提出了一种新颖的端到端方法，将CAD模型与场景的三维扫描对齐，从而能够将嘈杂、不完整的三维扫描转换为具有干净、完整的对象几何的紧凑、CAD重建。我们的主要贡献在于建立一个可微的procrustes对齐，与一个对称感知的稠密目标对应预测相匹配。为了同时将CAD模型与扫描场景中的所有对象对齐，我们的方法检测对象位置，然后预测在统一的对象空间中扫描和CAD几何体之间的对称感知密集对象对应关系，以及最近的相邻CAD模型，这两个模型都用于通知可区分的过程对齐。我们的方法以完全卷积的方式运行，使CAD模型能够在一次正向扫描中与扫描对象对齐。这使得我们的方法比最先进的方法要快19.04美元，用于扫描的CAD模型对齐，运行时间比以前的数据驱动方法快大约250倍。
URL
https://arxiv.org/abs/1906.04201
