Is object-based image analysis (OBIA) suitable for the semi-automatic classification of Lidar point clouds? Read more about the promising prospects. Object-based image analysis (OBIA) has been develop...
Do Lasers Trump Photogrammetry in Point Cloud Modeling?
Technological advancements have one main goal: to make it easier for us to accomplish tasks. Each new technology must show that it has solved a problem, and point cloud modeling is not different. For a long time, conducting land surveys was a bit of a challenge. Surveyors, contractors, and engineers invested numerous resources in these activities, increasing the project costs and periods. Point cloud modeling, a new concept, has since changed this and made it easier to survey work sites.
What is point cloud modeling?
Point clouds feature an array of data points, with each point comprising X, Y and Z coordinates. Each data point indicates a surface area, creating a 3D structure that surveyors and engineers alike can study. In addition, point clouds are manipulatable to reveal more details about a site, e.g., its terrain and features.
Point cloud modeling is thus the art of creating point clouds, which is possible using LiDAR and photogrammetry.
Breaking down LiDAR (light detection and ranging)
LiDAR is not necessarily a system. Instead, this term encompasses the tools used in point cloud modeling using remote sensors which collect dimensions from a site and convert these into a 3D model. From objects to environments, these tools map out areas using ultraviolet or near-infrared light. The tools send light pulses and measure how long the pulses take to return to the scanner based on the ground or launched from a drone. Based on the working mechanism of a laser system, the pulses move through a gain medium like those available in EksmaOptics.com for their amplification. The LiDAR tools then convert this pulse to-and-fro time into direction and distance by covering a circle in several sweeps. Finally, the tools create point cloud data in 3D models with X, Y and Z coordinates from each reading. If possible, GPS timestamps are added to the model. LiDAR applies to various materials, including clouds, rocks, rain, and even aerosols. It has proven to be a gem to people working in fields where physical access might not be possible.
While this method has worked great, it comes saddled with a few challenges. First, the beams must have sight access which can be tricky if there is bad weather. Secondly, reflection from surfaces can interfere with data collection.
The role of photogrammetry
Rather than relying on laser beams to map out sites, this method uses drones to capture pictures of a site. A camera mounted on the drone does the trick. Given that cameras rely on angles, there is a need to adjust the camera for each site’s environmental conditions. Moreover, the person handling the camera should also adjust the angles to ensure that the pictures get the whole scope.
What follows is the processing of the photos to create an overlap which creates a 3D mesh. The overlaps require reconstructing, where the person creating the mesh must fill in the gaps between the data points. The fewer images and angles there are, the more gaps there will be, increasing the processing time.
Benefits of Point Cloud Modeling
While there are other ways to create 3D models, people using point cloud modeling enjoy the benefit of:
- Spending less time on calibrating coordinates by allowing software to do the work.
- Accuracy to a millimeter scale when using ground-based systems and up to a centimeter-scale with drone-based systems.
- Avoiding cost overruns by understanding a site’s features before starting any work on it.
Is one point cloud modeling method more effective than the other? As far as accuracy goes, both methods have similar efficacy rates. Therefore, choosing either option comes down to your requirements in the project and how easy it is to implement one of the techniques.
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