A cuboid, also called a 3D bounding box, is an annotation type that encloses an object in three dimensions, capturing its position, size, and orientation in space, most often within a LiDAR point cloud.
A cuboid is the 3D extension of a bounding box: instead of a rectangle on the image plane, it is a box in space defined by its center position, its dimensions along three axes, and its rotation. A common parameterization is 7 degrees of freedom (x, y, z, length, width, height, yaw), extended to 9 when pitch and roll matter. Cuboids are usually drawn over LiDAR point clouds, often fused with camera views so annotators can see the object from multiple angles. Each cuboid carries a class and can be tracked across frames.
Key takeaways
A cuboid adds depth and orientation that a 2D box cannot represent.
It is the core annotation type for autonomous driving and robotics perception.
Orientation accuracy, not just overlap, is what makes a cuboid useful, because heading drives motion prediction.
Types and parameterization
7-DoF vs 9-DoF: yaw only, or full orientation with pitch and roll.
Amodal vs visible: the full object extent even where occluded, or only the observed part.
Single-frame vs tracked: a cuboid per frame, or one identity followed across a sequence.
How it works, and how FiftyOne fits
Annotators place and orient the box in a 3D view, often snapping it to the point cluster and checking it against fused camera images. In FiftyOne, cuboids render natively in the 3D visualizer alongside the point cloud and camera data, so you can inspect them from any angle and compare predicted 3D detections against ground truth.
Cuboid vs related labels
How a cuboid compares to other ways of localizing an object in 2D and 3D.
How a cuboid compares to other ways of localizing an object in 2D and 3D.
Term
What it captures
When to use
Bounding box
2D rectangle: location and extent on the image
Image and video objects
Cuboid
3D box: location, size, and orientation in space
LiDAR and 3D scenes
Point cloud segmentation
Per-point class labels
Dense 3D scene understanding
Why it matters
Cuboids give a perception model the spatial reasoning to know where an object is and which way it is heading, the difference between predicting a car's path and missing it. Standard axis-aligned IoU underweights orientation, so a cuboid can score a high IoU while pointing the wrong way, a real failure mode in autonomous-vehicle perception. That is why 3D tasks use rotated or 3D IoU, and why heading error is often reported separately from overlap.
Frequently asked questions
What is the difference between a cuboid and a bounding box?
A bounding box is 2D, a cuboid adds depth and orientation in 3D.
What data are cuboids drawn on?
Usually LiDAR point clouds, often fused with camera images.
Why does cuboid orientation matter?
Because heading drives motion prediction, and standard IoU can miss orientation errors.