Driven by the appealing properties of neural fields for storing and communicating 3D data, the problem of directly processing them to address tasks such as classification and part segmentation has emerged and has been investigated in recent works. Early approaches employ neural fields parameterized by shared networks trained on the whole dataset, achieving good task performance but sacrificing reconstruction quality. To improve the latter, later methods focus on individual neural fields parameterized as large Multi-Layer Perceptrons (MLPs), which are, however, challenging to process due to the high dimensionality of the weight space, intrinsic weight space symmetries, and sensitivity to random initialization. Hence, results turn out significantly inferior to those achieved by processing explicit representations, e.g., point clouds or meshes. In the meantime, hybrid representations, in particular based on tri-planes, have emerged as a more effective and efficient alternative to realize neural fields, but their direct processing has not been investigated yet. In this paper, we show that the tri-plane discrete data structure encodes rich information, which can be effectively processed by standard deep-learning machinery. We define an extensive benchmark covering a diverse set of fields such as occupancy, signed/unsigned distance, and, for the first time, radiance fields. While processing a field with the same reconstruction quality, we achieve task performance far superior to frameworks that process large MLPs and, for the first time, almost on par with architectures handling explicit representations.

In recent years, Neural Fields (NFs) have emerged as an effective tool for encoding diverse continuous signals such as images, videos, audio, and 3D shapes. When applied to 3D data,NFs offer a solution to the fragmentation and limitations associated with prevalent discrete representations. However, given thatNFs are essentially neural networks, it remains unclear whether and how they can be seamlessly integrated into deep learning pipelines for solving downstream tasks. This paper addresses this research problem and introducesnf2vec, a framework capable of generating a compact latent representation for an inputNFin a single inference pass. We demonstrate thatnf2veceffectively embeds 3D objects represented by the inputNFs and showcase how the resulting embeddings can be employed in deep learning pipelines to successfully address various tasks, all while processing exclusivelyNFs. We test this framework on severalNFs used to represent 3D surfaces, such as unsigned/signed distance and occupancy fields. Moreover, we demonstrate the effectiveness of our approach with more complexNFs that encompass both geometry and appearance of 3D objects such as neural radiance fields.

Recent developments in the field of deep learning have steered research on music generation systems towards a massive use of large end-to-end neural architectures. The capability of these systems to produce convincing outputs has been extensively proven. Nonetheless, they usually come with several drawbacks, such as a low degree of user control, a lack of global structure, and the inherent impossibility of online generation due to high computational costs. Our contribution is two-fold: first, we identify these limitations and show how they have been discussed and partially addressed in the existing literature; then, we propose a novel music generation approach aimed at overcoming such limitations, by properly combining a set of samples under user-defined constraints. We model our task as a job-shop problem, and we show that interesting results can be obtained at very low computational costs. Our framework is genre-independent as it deals with samples metadata rather then individual notes, even though additional genre-specific constraint could be introduced by users to meet their stylistic requirements.