> For the complete documentation index, see [llms.txt](https://stage-precision.gitbook.io/grid/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://stage-precision.gitbook.io/grid/spatial-interactive-experiences/overview.md). # Overview
## Spatial Interactive Experiences Grid Studio can use spatial sensor data to turn physical spaces, surfaces, and movement into real-time interaction. This can range from detecting a single touch on a wall to tracking people across an entire venue. Sensor data can be processed directly inside Grid Studio, used to trigger internal logic, sent to external systems as real-time tracking data, or converted into depth images for image-based and content-reactive workflows. ### What You Can Build Spatial sensing can be used for a wide range of interactive applications. Typical examples include: * People and object tracking * Interactive rooms and environments * Touch walls and interactive surfaces * LED floors and projection areas * Trigger zones and occupancy logic * Real-time position and tracking data * Localized tracking data for individual interactive areas * Depth images for real-time content, interaction, and external visual systems such as TouchDesigner or Notch A project can be as simple as a single sensor covering one interactive surface or consist of many sensors covering a large shared environment. ### Different Sensors, One Spatial Environment Grid Studio can work with different types of spatial sensors, including: * 2D LiDAR sensors * 3D LiDAR sensors * Depth cameras * Stereo depth cameras * RGB-D cameras * Other spatial sensing systems capable of providing 3D point data These sensors may use very different technologies to observe their environment. Some measure distance directly, while others derive depth from stereo vision, active illumination, or other depth-sensing methods. For Grid Studio, the important part is that the resulting measurements can be represented as spatial points and placed within the shared 3D project space. Once a sensor has been positioned and aligned, its data exists in the same coordinate system as the rest of the project. This also means that different sensor technologies can be combined. A 2D LiDAR and several 3D LiDARs can, for example, contribute data to the same tracking environment. The sensor technology itself does not define the application. The more important question is what should be detected and how the resulting data should be used. > Grid Studio also supports camera-based workflows using RGB or infrared imagery. These workflows do not use the point-cloud processing pipeline described in this section and are therefore not covered here. For a deeper introduction to sensor data and point clouds, see **Understanding Spatial Sensor Data**. ### From Sensor Data to Interaction Most spatial tracking projects follow the same general process: **Sensors → Spatial Alignment → Point Cloud Processing → Clustering → Interaction / Output** Each physical sensor is represented by its own Sensor Object. Multiple sensors can then be combined inside a Point Cloud Volume, where their data is filtered and prepared for the intended application. Depending on the project, the resulting data can then be used in several ways: * React directly to points inside defined zones * Detect and track clustered objects * Trigger Workflow Events * Count objects globally or inside specific areas * Output positions, IDs, Bounding Boxes, and other tracking data * Convert global tracking positions into relative or UV coordinates * Generate depth images from the processed point cloud These different outputs can be used independently or combined within the same project. ### Design Around the Application A useful way to approach a spatial tracking project is to start with the desired result rather than the available sensor technology. For example, a people-tracking installation may use several 3D LiDARs, a combination of 2D and 3D LiDARs, or another suitable spatial sensor configuration. Once their data is aligned in the same space, the tracking system can process the resulting points together. A touch installation, on the other hand, may require a different clustering method or dedicated sensors positioned specifically for the interaction surface. The Point Cloud Volume therefore represents an **analysis and processing goal**, rather than a specific type of sensor. This becomes especially important in larger projects where one tracking environment may serve several applications at the same time. ### Global and Local Interaction Tracking does not have to stop at the boundaries of the main tracked space. A large Point Cloud Volume can detect people or objects across an entire environment while additional Volume Objects select smaller areas from the final tracking result. For example, one project could track an entire room while independently providing data for: * an LED floor * a projection area * an interactive table * several separate installation zones Each local area can expose its own object count and output tracking positions in global, relative, or UV coordinates. This allows one global tracking system to serve multiple interactive experiences without processing each area as an independent tracking system. ### Tracking Data and Image Data Spatial sensor data can be used both as structured tracking data and as visual information. Tracking data can contain information such as: * Cluster IDs * Positions * Bounding Boxes * Object counts * Zone states This data can be used directly inside Grid Studio or mapped to external protocols such as OSC. The processed point cloud can also be rendered into depth images. These images can be generated using perspective or orthographic cameras, or projected through the UV coordinates of a mesh. Depth images are exposed as Image Providers and can be sent to external systems over media outputs such as SDI or NDI. This makes it possible to combine Grid Studio's spatial processing with real-time content, interaction, and visual systems such as TouchDesigner or Notch. ### Where to Start If you are building a spatial tracking project for the first time, continue with **Building an Interactive Tracking Project**. The guide walks through the complete setup process: **Connect → Align → Merge → Define Area → Clean → Cluster → Refine → Interact → Output → Test** For a more detailed understanding of the sensor data itself, start with **Understanding Spatial Sensor Data**. The **Applications** section contains practical architectures for common use cases, while the **Reference** section documents individual objects, filters, clustering methods, zones, and output systems in detail.