Data & collection
Point cloud
A point cloud is a collection of points representing sampled locations in two- or three-dimensional space. Each point has coordinates in a stated reference frame and may also carry fields such as colour, intensity, timestamp, surface normal, semantic label, or sensor-specific metadata.
Also known as: 3D point cloud, point-cloud data, point clouds
Updated
A point is more than three numbers
Point clouds commonly come from LiDAR, structured-light or time-of-flight cameras, stereo reconstruction, multi-view geometry or simulation. Cartesian x, y and z coordinates locate a sample, but its meaning also depends on the sensor origin, reference frame, units and capture time.
The ROS PointCloud2 message represents a timestamped set of points with a coordinate frame and an extensible set of fields. A cloud may therefore contain colour, reflectance, ring index or other values alongside position. Consumers should inspect the schema instead of assuming that every point has the same layout.
Organised and unorganised clouds behave differently
The Point Cloud Library distinguishes organised and unorganised point clouds. An organised cloud retains a row-and-column structure like an image, which can preserve neighbourhood relationships from a depth camera. An unorganised cloud is a flat set of points, as is common after merging scans or filtering samples.
A depth image and a point cloud can encode closely related geometry, but they are not interchangeable. The depth image stores distance on an image grid and relies on camera calibration for back-projection. A point cloud stores coordinates that have already been expressed in a frame, potentially after projection, motion compensation or registration.
Dataset quality depends on geometry and provenance
Point count alone says little about usefulness. Density changes with range and viewpoint; reflective or transparent surfaces can create missing or spurious returns; moving objects can distort a scan; and downsampling can remove small structures important for grasping or foot placement.
A robotics dataset should document the generating sensor or simulator, fields and data types, units, coordinate frame, calibration, timestamps, invalid-point convention, filtering and registration steps. If several captures were merged, retain the poses and method used. Labels should specify whether they apply to individual points, objects, voxels or the source image.
Sources
Related terms
Data & collection
Depth data
Depth data records the distance associated with image locations or sensor rays, usually as a depth image in which each pixel stores a metric value relative to a camera. The exact geometry, units, invalid-value convention, and coordinate frame depend on the sensor and encoding. RGB-D data pairs depth with colour imagery; a point cloud is a separate 3D representation derived from or aligned with such measurements.
Hardware & control
Coordinate frame
A coordinate frame is a defined origin and set of oriented axes used to express positions, orientations, motions, forces, or other spatial quantities. A value has no complete geometric meaning until its frame and convention are known. Transformations relate measurements expressed in frames such as world, robot base, camera, end effector, object, or sensor.
Models & learning
Pose estimation
Pose estimation is the process of inferring the position and orientation of a body, object, camera, hand, or robot relative to a specified coordinate frame. In three-dimensional robotics this is often called 6D or 6-DoF pose estimation because the result has three translational and three rotational degrees of freedom, even when orientation is stored with more than three numbers.
Hardware & control
SLAM
SLAM is the joint estimation of a moving robot or sensor rig's pose and a map of its environment from sensor observations. It addresses the coupled problem of needing a map to localise while needing pose estimates to build that map.
Data & collection
Sensor calibration
Sensor calibration is the estimation and documentation of parameters that map raw sensor readings into physically meaningful values and known spatial relationships. In robotics it can include scale, bias, distortion, intrinsic camera parameters, sensor-to-sensor or sensor-to-robot transforms, and timing offsets. Calibration does not remove all noise or drift.
Data & collection
Data synchronisation
Data synchronisation is the process of placing sensor, state, action, annotation, and outcome records on a common timeline so samples that describe the same physical instant or transition can be matched. It requires trustworthy timestamps or trigger relationships and an explicit rule for handling streams with different rates, delays, dropped samples, and clock offsets.