  We investigate the generalization capabilities of neural signed distance
functions (SDFs) for learning 3D object representations for unseen and
unlabeled point clouds. Existing methods can fit SDFs to a handful of object
classes and boast fine detail or fast inference speeds, but do not generalize
well to unseen shapes. We introduce a two-stage semi-supervised meta-learning
approach that transfers shape priors from labeled to unlabeled data to
reconstruct unseen object categories. The first stage uses an episodic training
scheme to simulate training on unlabeled data and meta-learns initial shape
priors. The second stage then introduces unlabeled data with disjoint classes
in a semi-supervised scheme to diversify these priors and achieve
generalization. We assess our method on both synthetic data and real collected
point clouds. Experimental results and analysis validate that our approach
outperforms existing neural SDF methods and is capable of robust zero-shot
inference on 100+ unseen classes. Code can be found at
https://github.com/princeton-computational-imaging/gensdf.
