  We present Scan2CAD, a novel data-driven method that learns to align clean 3D
CAD models from a shape database to the noisy and incomplete geometry of a
commodity RGB-D scan. For a 3D reconstruction of an indoor scene, our method
takes as input a set of CAD models, and predicts a 9DoF pose that aligns each
model to the underlying scan geometry. To tackle this problem, we create a new
scan-to-CAD alignment dataset based on 1506 ScanNet scans with 97607 annotated
keypoint pairs between 14225 CAD models from ShapeNet and their counterpart
objects in the scans. Our method selects a set of representative keypoints in a
3D scan for which we find correspondences to the CAD geometry. To this end, we
design a novel 3D CNN architecture that learns a joint embedding between real
and synthetic objects, and from this predicts a correspondence heatmap. Based
on these correspondence heatmaps, we formulate a variational energy
minimization that aligns a given set of CAD models to the reconstruction. We
evaluate our approach on our newly introduced Scan2CAD benchmark where we
outperform both handcrafted feature descriptor as well as state-of-the-art CNN
based methods by 21.39%.
