As autonomous driving and robotic systems mature, 3D LiDAR point clouds and sensor-fusion data have become key data types.
High-quality training data built from these unstructured inputs is the base for better 3D perception models.
Among many annotation types, 3D polygon and polyline annotation often receive less attention than 3D cuboids (3D bounding box). Yet they are vital for scene-level perception, providing precise spatial ground truth for drivable area detection, path planning, and road-structure parsing.
In this post, we‘ll explain how 3D polygon and polyline annotation works in point cloud and fusion data. It covers common tasks, real-world use cases, and a practical workflow using the BasicAI Data Annotation Platform.
What is 3D polygon annotation in computer vision?
In 3D perception projects, polygons and polylines describe two kinds of geometry.
A 3D polygon represents an irregular spatial boundary or surface. It’s built from a series of ordered vertices in 3D space. The vertices form a closed shape.
In autonomous driving, a 3D cuboid is used for an object-level box, such as a vehicle, pedestrian, or traffic sign. A 3D polygon is better for continuous areas, such as a lane region, road surface, or parking area.
Most polygon vertices sit near ground height. Together, they form a near-planar region and provide area-level structure.
This structure helps the model learn where a vehicle can safely drive. It also teaches the geometric limits behind behaviors like avoid, yield, and overtake.
What is 3D polyline annotation in computer vision?
A 3D polyline captures linear geometry and topology rather than enclosed area. It’s an ordered sequence of vertices, open at both ends, so it represents a path rather than a region.
With 3D polylines, teams can annotate lane markings, curbs, pipelines, boundary contours, forest-road centerlines, and similar objects in LiDAR point clouds. In autonomous driving, lane markings and lane centerlines are typical 3D polyline targets.
Pure vision-based lane detection struggles at night, in rain or snow, and under glare. LiDAR has lower spatial resolution than cameras, but many mechanical and solid-state LiDAR sensors include an intensity channel. Road marking paint, thermoplastic materials, and glass beads often have distinct reflectance. In intensity views, they can appear as visible line structures.
LiDAR-based 3D polyline annotation, combined with camera data, can reduce the weakness of a single sensor. It gives lane detection and lane geometry models a more stable supervision signal.
What tasks and applications benefit most from 3D polygon and polyline data?
Tasks supported by these annotations fall into three groups: region understanding, line and topology understanding, and cross-modal projection or auxiliary tasks.
The downstream models vary just as widely. It may be a classic point cloud semantic segmentation network, a bird’s-eye view (BEV) perception and planning model, an end-to-end multi-task network, or a graph-based topology learning model.
For drivable area detection, a common approach is to project 3D point clouds into BEV maps or voxel grids. A 2D or 3D convolutional model then predicts occupancy and traversability for each grid cell. The 3D polygon can be rasterized into a binary grid label to supervise the model.
For lane detection and lane topology learning, a 3D polyline can be parameterized as a sequence of control points or a spline. The model regresses the geometric parameters and connectivity, with the polyline annotation acting as ground truth.
Autonomous driving and robotics
In passenger cars and logistics robots, 3D polygons mark drivable area boundaries while 3D polylines trace curbs, lane markings, and route guides. Both are core inputs to path planning and lateral control. OpenLane-V1, for example, is a widely used dataset that annotates real-world 3D lane lines at scale.
This data is also useful for mobile robots, such as indoor AGVs, delivery robots, and campus inspection robots. In indoor environments, polygons can mark traversable areas such as corridors, rooms, and open spaces. Polylines can represent virtual lanes or navigation paths. This helps robots navigate repeated routes with higher precision.
Utilities and infrastructure management
Airborne and vehicle-mounted LiDAR can capture large city-scale point clouds. These scans cover roads, buildings, green belts, and public assets.
3D polygons are the natural fit for ground feature boundaries. Building footprints, rooftop outlines, parking lots, vegetation patches, water bodies, and land-use boundaries all qualify.
3D polylines describe linear infrastructure. Underground utilities, water and gas pipes, fiber, power lines, and street lighting cables are all linear assets best represented as polylines.
Precision agriculture and forestry
In precision agriculture, 3D polygons can divide farmland into detailed plots and support slope surveys. In forestry, ground robots use 3D polylines to extract trail edges and tree-row axes, producing reliable trajectories through irregular natural boundaries. This matters for automated forest maintenance, orchard logistics, and other off-road robotic workflows.
How to annotate 3D polygon and 3D polyline (using the BasicAI platform)
This section walks through drivable area polygon labeling and lane line polyline labeling using BasicAI Data Annotation Platform, an enterprise-grade multimodal data annotation tool. We assume your team has already finished data collection and basic preparation.
Create the dataset and ontology
Open BasicAI platform. In the left sidebar of the home page, open the “Dataset” tab.
Click “Create” in the upper-right corner. In the pop-up window, choose “LiDAR Fusion” as the data type. Enter a dataset name, such as urban_drivable_and_lane_mapping, then confirm.
Open the new dataset and switch to the “Ontology” tab to configure ontology assets.
Create a class named drivable_area. Set “Tool Type” to “Polygon”. Add attributes if needed, for example: surface_type > Asphalt/Concrete/Dirt.
Create another class named lane_line and set its Tool Type to Polyline. Bind attributes as your spec requires, such as line_style: Solid/Dashed/Double.
Annotate drivable area with 3D polygons
Switch back to the “Data” tab, select the LiDAR fusion data you want to annotate, and click “Annotate” to load the point cloud annotation editor.
The UI includes a 3D point cloud canvas and orthographic views (overhead, side, and rear). The left side contains tools. The right side shows ontology labels.
Pick the “3D Polygon” tool from the left toolbar (or press 3).
To cleanly isolate the ground in cluttered urban scans, two settings help.
Turn on smart ground segmentation. Open the control menu in the lower-left corner of the canvas. Under “Ground”, choose “Model”. The built-in ground detection model highlights terrain points in the point cloud view. Since drivable-area polygons should sit on the ground plane, this makes the road boundary easier to judge.
Filter by height range. In the upper-left corner of the point cloud canvas, find “Height Range”. Enter min and max height values. This hides irrelevant high objects, such as vehicles, building tops, streetlights, and tree crowns. The result is a cleaner ground-level view.
After the view is ready, click the road boundary to place the first vertex. During drawing, press “I” to enable point snapping. This lets vertices snap to the nearest LiDAR return and reduces manual alignment work.
Click clockwise or counterclockwise along the drivable boundary. When the shape is complete, press “Enter” or “Space”. The platform closes the polygon by linking the last vertex to the first and opens the attribute panel.
In the attribute panel, select the drivable_area ontology class and any required attributes. Confirm the annotation.
To insert a new control point, click directly on a polygon edge.
To refine the shape, drag polygon vertices in orthographic views. Use synchronized 2D camera images as an extra reference.
Annotate lane lines and trajectories with 3D polylines
Road markings reflect strongly in LiDAR returns and become faintly visible in the point cloud. But sparsity, occlusion, and rain-soaked diffuse reflection break those lines into fragments. To label them well, you need to cross-check against the projected 2D camera frames.
Pick the “3D Polyline” tool (shortcut 4). We’re going to mark the left-turn lane line in the point cloud.
Left-click on the start of the marking to place the first vertex. Follow the curve, dropping control points as it bends. Click the endpoint and press Enter or Space to finish.
The generated 3D polyline carries a direction by default, from start point to end point. This direction provides useful flow topology for downstream path planning models.
A few useful shortcuts:
Split a polyline. Select any non-endpoint vertex and press Shift + F. The polyline splits into two independent polylines that inherit the original class, attributes, and group information.
Merge polylines. Hold Shift, select two polylines, and click “Merge” in the top-left. You can choose to join the end of one to the start of the other.
Create a centerline. Hold Shift, select two polylines, and press “.” to generate a centerline between them.
Finish the work and export
Once every polygon and polyline is in place, click “Save” in the top-right of the canvas to commit your work, then “Close” to exit the editor.
Back on the “Data” tab, select the annotated point cloud frames and click the cyan “Export” button. In the export dialog, pick your target 3D coordinate format and submit. When the job finishes, you’ll have standardized PCD files and a JSON topology bundle ready to drop into a training pipeline.
Practical tips for 3D polygon and polyline annotation
Control vertex density. Too many vertices slow down annotation and reduce consistency. Too few vertices fail to match curved curbs, tight bends, and complex corners.
Use ground segmentation before drawing polygons. For drivable areas, enable the 3D ground segmentation model first. Then use height range filtering to remove floating noise above the road surface. This speeds up boundary drawing and helps keep polygons on the ground plane.
Check across modalities. LiDAR becomes sparse at longer range, especially beyond 20 meters. Use clear lane details in 2D camera images, then map them back to the 3D point cloud. This improves long-range lane-line accuracy.
Choose tools built for the workload. LiDAR fusion scenes can be huge. A single complex frame may contain millions or tens of millions of points. Use enterprise-level annotation platform designed for large point clouds and multi-camera fusion. This helps avoid browser lag, freezing, memory issues, crashes, and lost coordinates.
Partner with a specialized BPO. 3D polygon and polyline annotation have strict quality requirements. Production-grade autonomous driving projects may need millions of frames of ground truth. A trained 3D annotation service team, backed by strong QA and AI-assisted tools such as ground segmentation and tracking interpolation, can help deliver large volumes of accurate spatial data.
