Implicit Neural Representations (INRs) have emerged in the last few years as a powerful tool to encode continuously a variety of different signals like images, videos, audio and 3D shapes. When applied to 3D shapes, INRs allow to overcome the fragmentation and shortcomings of the popular discrete representations used so far. Yet, considering that INRs consist in neural networks, it is not clear whether and how it may be possible to feed them into deep learning pipelines aimed at solving a downstream task. In this paper, we put forward this research problem and propose inr2vec, a framework that can compute a compact latent representation for an input INR in a single inference pass. We verify that inr2vec can embed effectively the 3D shapes represented by the input INRs and show how the produced embeddings can be fed into deep learning pipelines to solve several tasks by processing exclusively INRs.

翻译：隐式神经表示（INR）近年来已成为一种强大工具，能够连续编码图像、视频、音频和三维形状等多种信号。当应用于三维形状时，INR能够克服以往常用离散表示中存在的碎片化与缺陷。然而，考虑到INR本身由神经网络构成，目前尚不清楚是否以及如何将其输入旨在解决下游任务的深度学习流程。本文提出这一研究问题，并构建了inr2vec框架，该框架能在单次推理过程中为输入INR计算紧凑的潜在表示。我们验证了inr2vec能够有效嵌入输入INR所表征的三维形状，并展示了如何将生成的嵌入向量输入深度学习流程，通过仅处理INR来解决多项任务。