## 5 Discussion
It also creates a friendly development environment for those who already employed or studied such well-known library for different Web3D projects, thus facilitating adoption of the framework. The decoupling of collections and scenes (see Section 3.2) highlighted several advantages in terms of organization and reuse of existing assets for online publication (3D models, panoramic content, etc.). Both scenes and collections are assigned unique IDs within a given ATON instance, thus allowing to uniquely address these resources for public (or local) dissemination. The scene cloning was particularly useful to test different arrangements, hypotheses or lighting setups referencing the same resources (see Sections 4.2 and 4.3). The scene descriptor (JSON) used in all tested instances of the framework, including national and international projects--and case studies in Section 4--proved to be lightweight in terms of storage, ranging between few bytes and 100 kb, with rare occurrences beyond the 100 kb mark. This also easily enables versioning approaches on the server node, for instance providing _snapshot_ capabilities to client web-applications for a given 3D scene. Furthermore, the scene descriptor proved to be a powerful approach to reference hybrid content from local and remote collections, including public resources (for instance the open-access CC0 Smithsonian 3D models collection (in gIITF)--see Figure 23C). This also allows to reference assets across different servers in order to distribute content in multiple infrastructure nodes, where needed. Regarding referenced 3D items in scenes moreover, since the gITF format is highly extensible, it already allowed an initial support for _inline copyright_ data (when present): these fields are already provided in gITF models published by SketchFab and many others, and the topic is indeed actively discussed in gITF communities. These fields are automatically recognised and extracted by ATON loading routines (see Figure 23D), with copyright information such as author, license, source, etc. that can be presented (e.g., in Hathor) to final users. Regarding interactive semantic annotation workflow for 3D scenes (see Section 3.6.5), Aiano case study showed how easily a researcher can annotate different areas (Section 4.3) using tools offered by the framework, but also highlighted a few interesting aspects. Besides the obvious outcomes in dissemination, such an approach has interesting practical implications in the scientific field regarding ATON front-ends like _Hathor_. Such tools can be easily used to study and discuss with other remote researchers, who autonomously (and without any expertise in 3D modeling) can add personal interpretations and spatialized annotations, directly editable into the 3D scene. Furthermore semantic 3D shape approach adopted in the architecture provides all the building blocks to  
Figure 23: (**A**) AR Presentation of a 3D scene; (**B**) A PBR asset directly exported from Unreal Engine 4; (**C**) A 3D scene referencing an external 3D model from Smithsonian CC0 collection; (**D**) Inline copyright from gITF; (**E**) Workload distribution among available cores; (**F**) Instance content statistics  
create more sophisticated applications for professionals, supporting advanced semantic formalisms targeting CH, like the _Extended Matrix_[84] and the resulting EMviq tool ([[http://osiris.itabc.cnr.it/scenebaker/index.php/projects/emviq/](http://osiris.itabc.cnr.it/scenebaker/index.php/projects/emviq/)]([http://osiris.itabc.cnr.it/scenebaker/index.php/projects/emviq/](http://osiris.itabc.cnr.it/scenebaker/index.php/projects/emviq/)), accessed on 22 November 2021) based on ATON, developed under SSHOC european project ([[https://sshopencloud.eu/](https://sshopencloud.eu/)]([https://sshopencloud.eu/](https://sshopencloud.eu/)), accessed on 22 November 2021).