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Point cloud attributes

In this article, you will discover what you should be doing with the point clouds attributes

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To access a point cloud's attributes, open the project menu and select your point cloud. Click on the Attributes tab, below you will find a dropdown menu listing all possible attributes, select the one you wish to use. You will find a list of all possible attributes, and what each does, below.

 

 

Here you will find a list of all available values along with its usage and the options related :

  • Analyze : Will only work with analysis point clouds, for more information about analysis, check out this article.
    • Name : Change the name of the point cloud
    • See the position
    • See the rotation
    • See the Scale
    • Delete the analysis (will only delete it from the viewer, in can be reloaded later on if it is a server-side analysis)

 

  • Classification : Will only work with compatible point clouds. The goal of classification is to group similar points together into meaningful categories, such as ground, vegetation, buildings, and other objects.
    • Show / hide all : display or hides every category
    • One line per category, click on the line to show / hide it, click on the color to change it

 

  • Color : Colorise the whole point cloud with 1 color
    • Select a color using the palette
    • Use an hexadecimal code to select the color
    • Pick one of color from the default list

 

  • Composite : Use this if you want to use multiple options at the same time, drag the sliders to increase / decrease their usage.
    • RGBA : Colorized point cloud
      • Gamma : Basically the exposition of the point cloud, lower gamma equals to higher exposition, higher gamma, lower exposition, gives a sunset look
      •  Brightness : The brightness of the point cloud, lower brightness will make it darker, higher brightness will make it lighter.
      • Contrast : Changes the contrast of the point cloud, lower contrast will make the point cloud turn gray, higher contrast will overexpose the standard colors (red, yellow, green, blue…)
    • Intensity : Strength or magnitude of the signal
      • Range : From low intensity to high
      • Gamma : Basically the exposition of the point cloud, lower gamma equals to higher exposition, higher gamma, lower exposition, gives a sunset look
      •  Brightness : The brightness of the point cloud, lower brightness will make it darker, higher brightness will make it lighter.
      • Contrast : Changes the contrast of the point cloud, lower contrast will make the point cloud turn gray, higher contrast will overexpose the standard colors (red, yellow, green, blue…)
    • Elevation : Height or vertical position of each point
      • Elevation range : set where the gradient start and ends in term of height
      • Gradient mode 
        • Clamp : Standard gradient, outside of the range, keeps the final color of the edge
        • Repeat : When the gradient ends, starts over from the original color
        • Mirror Repeat : When the gradient ends, puts a new gradient inverted, similar to repeat except there are no color discontinuity
      • Gradient scheme : Change the colors of the gradient, selection from a predefined list only
    • Analyze : Only if available on that point cloud, model to point cloud comparison
    • Classification : Only if available on that point cloud, divides different types of objects (ground, vegetation, buildings…)
    • Return number : Amount of pulses that were needed to get that point
    • Point source ID : Grouped by scan ID

 

  • Elevation : It represents the height or vertical position of each point. Elevation data is important in many applications of point clouds, such as topographic mapping, flood modeling, urban planning, and infrastructure design. By analyzing the elevation data, it is possible to create accurate and detailed digital elevation models (DEMs) that can be used for a wide range of geospatial analysis and visualization purposes.
    • Elevation range : set where the gradient start and ends in term of height
    • Gradient mode 
      • Clamp : Standard gradient, outside of the range, keeps the final color of the edge
      • Repeat : When the gradient ends, starts over from the original color
      • Mirror Repeat : When the gradient ends, puts a new gradient inverted, similar to repeat except there are no color discontinuity
    • Gradient scheme : Change the colors of the gradient, selection from a predefined list only

 

  • Gps-time : GPS time is an important attribute associated with each point in the point cloud. It provides information about the time at which the laser pulse was emitted and the time at which the return pulse was received, allowing for accurate calculation of the range and position of the point.  In addition, GPS time can also be used for quality control and analysis purposes. By analyzing the distribution of GPS time values across the point cloud, it is possible to identify areas with temporal anomalies or errors, which may indicate problems with the data or the LiDAR system.

 

  • Indices : Indices are attributes associated with each point in a LiDAR point cloud that provide additional information about the point's spatial properties and relationships with other points in the cloud. They are often used to support advanced analysis and processing tasks, such as segmentation, classification, and feature extraction. There are several types of indices that can be used in LiDAR point cloud processing, including:
    • Normal vector: This index represents the orientation of the point relative to its local neighborhood. It can be used to identify planar surfaces or to estimate surface normals for surface reconstruction.
    • Curvature: This index represents the local curvature of the surface at the point. It can be used to identify sharp edges or to estimate the radius of curvature for curved surfaces.
    • Height above ground: This index represents the height of the point above the ground or a reference surface. It can be used for terrain modeling or to identify objects that are above or below the ground surface.
    • Relative height: This index represents the height of the point relative to its neighboring points. It can be used to identify objects that are higher or lower than their surroundings.
    • Density: This index represents the density of points in the local neighborhood around the point. It can be used to identify areas with high or low point density, such as vegetation or building facades.

 

  • Intensity : It represents the strength or magnitude of the signal that was received by a sensor or device when the point was captured. In some cases, intensity is related to the reflectance of the object at that point. For example, in a LiDAR point cloud, the intensity value represents the amount of laser light that was reflected back to the sensor by the object. In this case, a higher intensity value would indicate a surface that reflects more light, such as a white wall, while a lower intensity value would indicate a surface that reflects less light, such as a black car. In other cases, intensity can represent a different physical quantity. For example, in a photographic point cloud, intensity may represent the brightness of a pixel in the original image that was used to generate the point cloud.
    • Range : From low intensity to high
    • Gamma : Basically the exposition of the point cloud, lower gamma equals to higher exposition, higher gamma, lower exposition, gives a sunset look
    •  Brightness : The brightness of the point cloud, lower brightness will make it darker, higher brightness will make it lighter.
    • Contrast : Changes the contrast of the point cloud, lower contrast will make the point cloud turn gray, higher contrast will overexpose the standard colors (red, yellow, green, blue…)

 

  • Intensity gradient : It is a measure of the change in intensity between neighboring points in a point cloud. It represents the rate at which the intensity changes with respect to distance or position. The intensity gradient is calculated by taking the gradient of the intensity values in the point cloud. This involves calculating the partial derivatives of the intensity values with respect to the x, y, and z coordinates of each point. The magnitude of the gradient vector at each point represents the intensity gradient value. High values of intensity gradient indicate sharp edges or boundaries, while low values indicate smooth or gradual transitions.
    • Range : From low intensity to high
    • Gamma : Basically the exposition of the point cloud, lower gamma equals to higher exposition, higher gamma, lower exposition, gives a sunset look
    •  Brightness : The brightness of the point cloud, lower brightness will make it darker, higher brightness will make it lighter.
    • Contrast : Changes the contrast of the point cloud, lower contrast will make the point cloud turn gray, higher contrast will overexpose the standard colors (red, yellow, green, blue…)

 

  • Level of detail : In LiDAR point cloud processing, Level Of Detail (LOD) can refer to the density of points in the point cloud, the resolution of the data, or the level of abstraction used to represent the data. For example, a point cloud with a high level of detail would have a high density of points, a high resolution, and a fine level of abstraction. Conversely, a point cloud with a low level of detail would have a lower density of points, a lower resolution, and a coarse level of abstraction.

 

  • Matcap : In LiDAR point cloud processing, matcap can be used to add visual textures and shading effects to 3D models created from point clouds. By applying a matcap texture to a 3D model, it is possible to create a more realistic and visually appealing representation of the scene. For example, a matcap texture could be used to simulate the appearance of a concrete wall or a tree bark, adding depth and realism to the 3D model.
    • Select the texture to apply

 

  •  Number of returns : It represents the total number of laser pulses that were emitted and returned to the LiDAR sensor to capture that particular point. Similar to return number, the number of returns can range from 1 to 5 or more, depending on the LiDAR system used. The total number of returns can provide information about the complexity and structure of the object or scene being scanned. For example, a point with a high number of returns may indicate an object with multiple layers or complex geometry, such as a tree canopy or a building facade.

 

  • Point source ID : It identifies the specific laser sensor that generated the point. Each laser sensor in a LiDAR system has a unique ID or number, and this information is recorded in the point cloud data to allow for analysis and quality control. Point source ID is particularly useful in situations where multiple LiDAR sensors are used to capture a single scene or area. By identifying which sensor generated each point, it is possible to perform quality control checks on the data and ensure that the data is properly aligned and registered between different sensors. This is especially important in applications such as forestry, where multiple LiDAR sensors may be used to capture data from different angles and perspectives. On our platform, point source id is used to store the source scan id, this allows users to easily track which scan each point in the point cloud originated from. For example, if multiple scans were taken of the same area using a 3D laser scanner, each scan could be assigned a unique scan id and the point source id attribute for each point in the point cloud could be set to the corresponding scan id.

 

  • RGBA : It is used to represent the color of each point in the point cloud. The red, green, and blue channels are used to represent the color of the point, while the alpha channel is used to represent the transparency or opacity of the point. The use of color in LiDAR point clouds can be helpful for visualization and interpretation purposes, as it can provide additional information about the properties of the objects and surfaces represented by the points. For example, in vegetation mapping, the color of the points can be used to distinguish between different types of vegetation or to identify areas of high or low vegetation density.
    • Gamma : Basically the exposition of the point cloud, lower gamma equals to higher exposition, higher gamma, lower exposition, gives a sunset look
    •  Brightness : The brightness of the point cloud, lower brightness will make it darker, higher brightness will make it lighter.
    • Contrast : Changes the contrast of the point cloud, lower contrast will make the point cloud turn gray, higher contrast will overexpose the standard colors (red, yellow, green, blue…)

 

  • Return number : It represents the number of times a laser pulse was emitted and returned to the LiDAR sensor in order to capture that particular point. Similar to number of returns, return number is typically a value between 1 and 5, with 1 indicating the first return and 5 indicating the fifth return. The first return represents the laser pulse that is reflected from the top surface of the object, while subsequent returns may represent reflections from lower surfaces or multiple reflections within the object.

 

  • Scan angle rank : It represents the angle between the laser beam and the scanner's reference line when the laser pulse was emitted to capture that particular point. Scan angle rank provides information about the angle at which the point was captured, with a value of 0 indicating the scanner's reference line and positive or negative values indicating the deviation from the reference line. In building extraction, scan angle rank can be used to identify building facades and roof structures based on their orientation relative to the scanner's reference line. In road surface analysis, scan angle rank can be used to detect surface defects or irregularities based on the angle of the scanner relative to the road surface.

 

  • User data : It allows users to add additional information to the point cloud beyond the standard attributes like XYZ coordinates, intensity, and return number. User data can be used to store a wide range of information, such as RGB color values, classification labels, or metadata associated with the point. For example, user data could be used to indicate whether a point is part of a building, a tree, or a road, or to store information about the quality of the point measurement or its uncertainty. The use of user data depends on the specific needs and applications of the user. It can be used for a variety of tasks, such as object recognition, classification, segmentation, and mapping. By adding customized user data to a point cloud, it is possible to extract more meaningful information and perform more advanced analysis and processing tasks.

 

If your point cloud doesn't show the right way at first, try the RGBA or intensity attributes as these are the most used.

 

If you run into any issues, our support team is here to help. Happy exploring!

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Creator : Alexis Helper
Creation date : 08/02/2024

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