> For the complete documentation index, see [llms.txt](https://docs.imerit.net/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.imerit.net/3d-multi-sensor-fusion/labeling/3d-multi-sensor-fusion-labeling-editor/key-features/combined-tools-cuboid-3d-polygons-3d-polylines-+-2d-drawables.md). # Combined Tools (Cuboid, 3D Polygons, 3D Polylines + 2D Drawables) ## Overview The Multi-Modal Segmentation Tool is a new point cloud annotation capability that integrates three distinct segmentation methods - cuboid, polygon, and brush - into a single, unified workflow. Annotators can now apply the optimal tool for each geometric region of an object without switching contexts, losing progress, or accepting accuracy trade-offs. Real-world objects are geometrically diverse. A vehicle has a rectangular body, an irregular roof rack, and ground-contact surfaces that each demand a different annotation approach. Until now, annotators were forced to pick one tool and compromise everywhere else - or spend excessive time applying the wrong method to each part. ## What's New ### Integrated Tool Switching Annotators can now transition between cuboid, polygon, and brush modes within a single annotation session on the same point cloud object. No work is lost when switching modes, and all segments contribute to a unified output. ### Simultaneous Multi-Tool Operation All three tools operate on the same point cloud simultaneously. Segments created by different tools are merged into a single, coherent annotated object, preserving geometric accuracy across every region. ### Contextual Tool Optimization Each tool is designed for the annotation challenge it handles best. Cuboids provide fast, precise coverage of rectangular geometry. Polygons handle irregular outlines with accurate trace-based boundaries. Brush delivers pixel-level refinement for terrain contact and complex surface edges. ## Tool Reference
ToolBest ForUse in Multi-Modal
CuboidRectangular bodies, geometric shapesVehicle chassis, building facades
PolygonIrregular outlines, complex contoursRoof racks, cargo attachments
Brush SphereFine-detail refinement, terrain edgesGround contact points, irregular surfaces
## Example Workflow The following illustrates how the three tools work together when annotating a construction vehicle: **Step 1 - Brush:** **+ icon (class level):** creates a new annotation instance. Tooltip: *"Add annotation"*. Always visible on the class row. * **Pencil icon (annotation level):** edits an existing annotation. Tooltip: *"Edit with"*. * Clicking the pencil icon beside an annotation opens a palette labelled "Edit with" listing all drawables configured for that class. * Selecting a drawable enters add/remove-point editing mode for the existing annotation using the chosen tool. * If exactly one drawable is configured, the pencil icon activates it immediately without showing a palette. * The edit palette is sourced from the same project-setup configuration as the create palette. No hardcoded tool lists exist in either. **Step 2 - Polygon:** **+ icon (class level):** creates a new annotation instance. Tooltip: *"Add annotation"*. Always visible on the class row. * **Pencil icon (annotation level):** edits an existing annotation. Tooltip: *"Edit with"*. * Clicking the pencil icon beside an annotation opens a palette labelled "Edit with" listing all drawables configured for that class. * Selecting a drawable enters add/remove-point editing mode for the existing annotation using the chosen tool. * If exactly one drawable is configured, the pencil icon activates it immediately without showing a palette. * The edit palette is sourced from the same project-setup configuration as the create palette. No hardcoded tool lists exist in either. **Step 3 - Cuboid:** Draw a cuboid around the vehicle's main rectangular body. Fast, precise, and ideal for geometric shapes. * Leverage all cuboid features to get fast and accurate segmentation. Features that help reduce the time per object and enhance accuracy: interpolation, film reel, autogrounding, static dimension etc. The result is a fully segmented point cloud built with the optimal tool for each geometric challenge - completed in one continuous workflow, with no context switching. {% embed url="" %} *** ## Configured Drawables ### Smarter Annotation Palette * The drawable palette - the menu annotators use to choose their labeling tool - shows only the tools configured for the class being annotated. * When a class has only one tool configured, the palette is skipped entirely. The tool activates immediately and a brief confirmation strip tells the annotator which tool is active. ### Edit With Any Configured Tool * Annotators can edit an existing annotation using any drawable configured for that class - not just the tool that originally created it. This means an annotation created with Polygon can be refined using Brush sphere, and vice versa, without needing to re-annotate from scratch. * The edit and create palettes are sourced from the same project configuration, ensuring consistency. ### Visually distinct create and edit actions * The class list panel uses distinct icons to differentiate creating a new annotation from editing an existing one. The + icon creates. The pencil icon edits. *** ## Availability & Compatibility **Availability:** This feature is generally available in the current platform release. **Supported formats:** All standard point cloud formats currently supported by the annotation platform (pcd and las). **Existing projects:** Fully compatible with existing annotation projects. No migration required. **Access:** Available to all annotator roles. No additional permissions or feature flags required. *** ## Why It Matters As autonomous systems encounter increasingly diverse real-world objects - construction equipment, unusual vehicle configurations, complex urban structures - training data must capture geometric complexity accurately. Single-tool annotation workflows cannot keep pace with this diversity. Multi-Modal Segmentation addresses this directly: * Accuracy improves because each region is annotated with its geometrically appropriate method. * Speed increases because annotators are no longer spending excessive time forcing the wrong tool onto a task. * Workflow continuity is preserved - no saving, switching, or re-importing required between tool modes. * Training data quality scales with object complexity, rather than being bottlenecked by it.