Vision-language models (VLMs) have achieved remarkable success in various domains. However, their application to 3D scene understanding remains largely underexplored. Existing 3D VLMs predominantly focus on object-level tasks and often emphasize instance-centric representations within a scene, lacking holistic scene-level descriptions. In this work, we propose an automated annotation framework that leverages multi-view images to partition 3D scenes into localized point cloud sub-regions, which are then enriched with precise semantic information-all without any manual intervention. We processed ScanNet v2 and ScanNet++ to construct SceneCap, a large-scale dataset designed for scene-level description. To demonstrate the benefits of our framework for scene understanding, we introduce Natural Interactive Universal Multimodal Observer, NIUMO-LLM, a lightweight yet high-performing model training on SceneCap. We further demonstrate that NIUMO-LLM achieves state-of-the-art (SOTA) performance on both scene description benchmarks and object-level tasks, requiring only 12 hours of training on a single NVIDIA A800 GPU. This design significantly reduces computational demands, lowering the barrier for MLLM-related research.

Immersive technologies offer new opportunities to support collaborative visual data analysis by providing each collaborator a personal, high-resolution view of a flexible shared visualisation space through a head mounted display. However, most prior studies of collaborative immersive analytics have focused on how groups interact with surface interfaces such as tabletops and wall displays. This paper reports on a study in which teams of three co-located participants are given flexible visualisation authoring tools to allow a great deal of control in how they structure their shared workspace. They do so using a prototype system we call FIESTA: the Free-roaming Immersive Environment to Support Team-based Analysis. Unlike traditional visualisation tools, FIESTA allows users to freely position authoring interfaces and visualisation artefacts anywhere in the virtual environment, either on virtual surfaces or suspended within the interaction space. Our participants solved visual analytics tasks on a multivariate data set, doing so individually and collaboratively by creating a large number of 2D and 3D visualisations. Their behaviours suggest that the usage of surfaces is coupled with the type of visualisation used, often using walls to organise 2D visualisations, but positioning 3D visualisations in the space around them. Outside of tightly-coupled collaboration, participants followed social protocols and did not interact with visualisations that did not belong to them even if outside of its owner's personal workspace.