Title: 3D Surface Reconstruction in the Wild by Deforming Shape Priors from Synthetic Data. Abstract: We present a new method for category-specific 3D reconstruction from a single image. A limitation of current color image-based 3D reconstruction models is that they do not generalize across datasets, due to domain shift. In contrast, we show that one can learn to reconstruct objects across datasets by shape priors learned from synthetic 3D data and a point cloud pose canonicalization method. Given a single depth image at test time, we first place this partial point cloud in a canonical pose. Then, we use a neural deformation field in the canonical coordinate frame to reconstruct the 3D surface of the object. Finally, we jointly optimize object pose and 3D shape to fit the partial depth observation. Our approach achieves state-of-the-art reconstruction performance across several real-world datasets, even when trained without ground truth camera poses (which are required by some of the state-of-the-art methods). We further show that our method generalizes to different input modalities, from dense depth images to sparse and noisy LIDAR scans. 