Spectral 3D computer vision combines wavelength-resolved measurements with geometric information to support material-aware reconstruction, perception, and scene analysis. However, the literature remains fragmented across hyperspectral and multispectral sensing, cross-spectral depth and pose estimation, spectral point clouds, LiDAR–hyperspectral fusion, and neural scene representations. This survey provides a structured review of the field, covering spectral and geometric acquisition, data representations, calibration and registration, spectral 3D reconstruction, integrated spectral 3D acquisition and joint physical modelling, learning-based methods, downstream tasks, benchmark datasets, applications, and software tools. We organise existing methods into three complementary methodological themes: spectral support for 3D information estimation, spectral 3D reconstruction and mapping, and integrated spectral 3D acquisition and joint physical modelling. We further compare representative datasets and reported results across classification, regression, segmentation, semantic scene analysis, and spectral novel-view synthesis. The analysis indicates that spectral information can provide complementary material, condition, and visibility cues when RGB appearance or geometry alone is insufficient; however, the magnitude of the benefit is task- and baseline-dependent, and many reported gains do not isolate the spectral contribution from concurrent changes in sensing, architecture, spatial context, or fusion. Progress remains constrained by limited native spectral 3D benchmarks, inconsistent radiometric and geometric calibration, heterogeneous evaluation protocols, and limited cross-sensor and cross-domain validation. We conclude by identifying research priorities in standardised benchmarking, uncertainty-aware fusion, physically grounded learning, efficient spectral 3D representations, and generalisation across sensors, environments, and application domains.
Optical Music Recognition (OMR) aims to convert images of music scores into structured digital representations that support preservation, accessibility, retrieval, editing, and computational music analysis. Recent advances in Deep Learning (DL) have substantially influenced the field, improving tasks such as document analysis, staff and symbol detection, and end-to-end music transcription. This paper reviews DL-based approaches to OMR, focusing on their target tasks, input and output representations, neural architectures, datasets, evaluation protocols, and reported results. The review shows clear progress across several OMR tasks, particularly in constrained scenarios. At the same time, the field remains limited by fragmented datasets and formats, inconsistent evaluation practices, reduced robustness to handwritten and degraded scores, and the difficulty of combining visual recognition with musical syntax and semantics. The paper also examines current research trends, including transformer-based architectures, graph-based representations, synthetic data generation, and end-to-end processing. By organizing the literature around research areas, system configurations, resources, methods, and open problems, this review provides a structured overview of the current state of DL-based OMR and outlines research directions toward more general, reliable, and reproducible recognition systems.
Federated agentic AI couples federated adaptation with autonomous agents deployed across distributed organizations and infrastructures. Unlike conventional federated learning (FL), these agents maintain persistent state, interact with tools and external services, and can translate learned or retrieved information into actions. This coupling introduces security and trust risks that cannot be analyzed adequately at either the federation or agent layer alone. A compromise may cross system boundaries, persist through redistributed artifacts, and influence later decisions under partial observability and fragmented governance. Existing surveys examine FL security, federated large-model adaptation, wireless deployment, or agentic AI security, but provide limited treatment of such cross-domain propagation and the maturity of the underlying evidence. We address this gap through a structured survey of 125 coded studies and a two-axis framework spanning four system domains and three security and trust concerns. We develop a propagation-centric threat model and assess the maturity and transferability of defense evidence. Finally, we identify open problems in end-to-end validation, privacy-preserving observability, governance of agent state and identity, and recovery under cross-domain compromise.
