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Implicit Neural Representations (INRs) have emerged in the last few years asa powerful tool to encode continuously a variety of different signals likeimages, videos, audio and 3D shapes. When applied to 3D shapes, INRs allow toovercome the fragmentation and shortcomings of the popular discreterepresentations used so far. Yet, considering that INRs consist in neuralnetworks, it is not clear whether and how it may be possible to feed them intodeep learning pipelines aimed at solving a downstream task. In this paper, weput forward this research problem and propose inr2vec, a framework that cancompute a compact latent representation for an input INR in a single inferencepass. We verify that inr2vec can embed effectively the 3D shapes represented bythe input INRs and show how the produced embeddings can be fed into deeplearning pipelines to solve several tasks by processing exclusively INRs.

Deep Learning on Implicit Neural Representations of Shapes