The data points collected for a given geographical area, terrain, building, or space are referred to as point cloud data. The dataset contains the X, Y, and Z geometric coordinates of a single point on an underlying sample surface. They are a means of collating a large number of single spatial measurements into a dataset that can be represented as a whole.
When the color information is presented, the point cloud becomes 4D. The point cloud data is imported into CAD software for visualizing the project, and once it can be parsed, manipulated, and modified to aid in visualization, design, engineering, and construction projects.
The cloud data management can be used in many ways, including creating CAD models, modeling landforms, and surveying complex locations.
Let’s know how a point cloud is created
To create a point cloud, there are two primary technologies that can be used for its generation:
Laser Scanners
The laser scanner is the survey-grade system, and it includes a number of different sensors and technologies. It captures point clouds with the highest accuracy. In this process, it is completed by a trained technician to ensure proper collection of data and correct scanning methods.
Photogrammetry
Under this tool, the camera captures the space from all angles, and images are processed with specialized software to reconstruct the space in 3D. The software can process the images to create individual point cloud data sets.
While capturing a point cloud of a building with drones, there are chances that it will be used under the photogrammetry because the lidar technology, such as laser scanners, is heavy for a drone.
Industries that use point cloud data
Point cloud data completely changed the documentation for AEC and various other industries. Not tech apart from this could provide this much detailed virtual replication of assets in a facility. It serves a greater purpose by integration with the following applications –
- Building Information Modeling (BIM)
- Virtual reality (VR) and augmented reality (AR)
- Heritage Preservation
Challenges faced while working in Point Cloud Data Management
The laser scanning and point cloud technology is surely lucrative. But it has its own set of challenges like any other technology. Let us discuss the very same challenges one by one in detail.
The Challenges of Large Data Volume
Large data volume is one big challenge in point cloud data management, as an act of scanning could create a sheer volume of data. A single laser scanning device could create billions of data points. And the size of those could be some gigabytes or terabytes.
Processing and computational complexity
Laser scanning generates tremendously dense and complex point cloud data. It is therefore challenging, and computationally, it demands –
- Cleaning data
- Filtering noise
- Aligning images
- Producing 3D models
All these tasks could require a significant amount of time and skills for completion.
Data quality and accuracy
Maintaining the quality of point cloud data is also another challenge. It could be affected by the following metrics –
- Environmental conditions
- Scanning equipment limitations
- Human error
- Noise
- Irrelevancy and missing scans due to occlusions or bad scanning angles
Interoperability and Format Compatibility
The construction industry is struggling badly with standardization, and the same is the case with point cloud data formats. The differing point cloud data creates issues with data interchange, workflow, and compatibility across several platforms.
Real-Time Visualization and Analysis
In today’s time, real-time actions are a must for businesses to have a competitive edge. But rendering billions of points without lags or crashes is challenging. This requires advanced graphics processing and software optimization.
Collaboration and Data Sharing
Laser scanning and point cloud working have multiple critical stakeholders. And therefore, transferring these very same datasets across teams is daunting due to –
- Data size
- Format differences
- Network limits
- Geographical constraints
We talked about the challenges associated with point cloud data management. Now we will get on the solutions that could solve the very same problems mentioned above.
Data Compression
Using contemporary compression algorithms, we can reduce the amount of point data while retaining quality. Algorithms like LASzip can compress LiDAR or 3D model data effectively.
Cloud Storage
One advantage of storing in the cloud is savings on tangible resources. This further supports scalability. Some of the common platforms that could help organizations scale their point cloud operations without physical infrastructure could be –
Parallel processing and GPU acceleration
Hardware investments such as multi-core CPUs and GPUs could help in accelerating point cloud computation. Many software systems like Bentley Context Capture and Autodesk Recap, use GPU acceleration to speed up complex computations.
Subsampling
Subsampling point cloud data reduces processing effort and allows us to design simpler models. It demands less computational resources while maintaining acceptable accuracy levels for some applications.
Noise Reduction Techniques
One could use specialized tools for removing noise from capture point cloud data too. Some of the common tools like that are CloudCompare and Autodesk Recap. These provide filtering algorithms to clean up point cloud data and remove outliers.
High-Quality Scanners
Investing in high-end scanning machines with increased resolution and precision reduces the likelihood of noise and errors. Devices with many scanning modes acquire more complete data, reducing gaps and increasing accuracy.
Software Integration
Also, it is advised to use software that supports multi-format import/export. Some good examples of those are Bentley’s Microstation and Autodesk’s Revit for seamless transitions across formats.
Efficient data transfer protocols
For the transmission of larger datasets, it is advisable to adopt techniques such as FTP or WebDAV. These techniques could be a great help in reducing bandwidth restrictions. In parallel to that, compression techniques can also be deployed for an effective transmission.
The Future of Point Cloud Data Management
Cloud data management is said to develop with the advancement of the following technologies –
- Machine learning
- Cloud computing
- Drone scanning
Over time, they will be able to function more efficiently, accurately, and in real time. As a result, we could also see the prominence of digital twin technology in the AEC and facility management sectors.
Conclusion
Point cloud data management operations should have a multidimensional strategy. It is extremely crucial to have a thorough understanding of the latest trends for the following –
- Data storage
- Data processing
- Data visualization
- Data security
One should also attempt to address the above-mentioned challenges. By this, the industry could unlock the full potential of point cloud data, further leading to a seamless AEC future.
