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.

一个新的端到端方法可以将嘈杂，不完整的三维扫描转换为干净，完整的物体几何，通过对齐 CAD 模型并在对称感知稠密对象对应预测配对的 Procrustes 坐标系改进，该方法可以单次前向传递地将 CAD 模型与扫描的对象对齐，比以前的数据驱动方法快约 250 倍，并且性能超过现有技术。