As software developers, we are always looking for solutions that meet the real needs of professionals, and the main purpose of this article is to understand the challenges of working with point cloud data and how our software can help you overcome them.
While point cloud data is incredibly valuable to various industries (land surveying, construction, civil engineering, interior design, planning, etc.), it presents several challenges in its management and use.
Here are five major pain points organizations often encounter when working with point cloud data:
1. Overwhelming data volume
Point cloud datasets are notoriously large, often reaching dozens or even hundreds of gigabytes in size.
This massive volume of data creates numerous issues:
- Processing Limitations: Standard computers often lack the processing power to handle point cloud data efficiently, leading to slow performance and frequent crashes
- Transfer Difficulties: Sharing and transferring these enormous files becomes time-consuming and bandwidth-intensive.
2. Complexity in Data Classification
Point cloud data typically comes unclassified, making it challenging to extract meaningful information
This leads to several issues:
- Time-Consuming Manual Classification: Surveyors and engineers must spend significant time manually classifying points like surfaces, vegetation, buildings, or other features.
- Inconsistent Classification: Different team members may classify points differently, leading to inconsistencies in the final output.
- Limited Automated Solutions: While machine learning algorithms for automatic classification are improving, they're still not reliable enough for many applications.
3. Software and Hardware Limitations
Working with point cloud data often requires specialized software and hardware, which can be problematic:
- Expensive Software: Point cloud processing software can be extremely costly, putting it out of reach for smaller organizations
- Steep Learning Curve: These specialized tools often have a steep learning curve, requiring significant training and expertise.
- Hardware Requirements: Processing point cloud data demands high-performance computers with powerful GPUs, adding to the overall cost
4. Data Integration Challenges
Integrating point cloud data with other data types and existing workflows can be difficult:
- Incompatibility Issues: Point cloud data may not easily integrate with traditional CAD or GIS software.
- Workflow Disruption: Incorporating point cloud data often requires significant changes to established workflows.
- Data Silos: The unique nature of point cloud data can lead to it being isolated from other data sources, hindering comprehensive analysis.
5. Accuracy and Quality Control
Ensuring the accuracy and quality of point cloud data presents its own set of challenges:
- Data Noise: Point clouds often contain noise or irrelevant data points that need to be filtered out
- Accuracy Verification: Verifying the accuracy of millions or billions of points is a complex and time-consuming process.
- Data Gaps: Occlusions during data capture can lead to gaps in the point cloud, requiring additional processing or recapture.
How can Tcp PointCloud Editor help you deal with these challenges?
Our Tcp PointCloud Editor software is a cost-effective and powerful alternative to deal with all these pain points, providing:
1. Web Viewer
Your projects can be published to the cloud and inspected with a web viewer from any device. Also, points, models, alignments, and drawings can be exported to 3D PDF and video. Additionally, you can incorporate breaklines to recover lost information and edit models to correct areas with noise or add missing information.
2. Management of points and images
Manage point cloud data obtained from SLAM and LiDAR scanners, mobile apps, drones, or photogrammetry technologies. You can also measure, dimension, and draw easily with intuitive CAD tools such as AutoCAD®, BricsCAD®, GstarCAD®, or ZWCAD®.
The visualization is optimized, making it easy to analyze the point cloud data, including displaying property captures with histograms, etc. Then you can share results with the final client using the web viewer.
3. Filtering and editing
Save time in data classification thanks to the editing and assign categories feature that allows you to select points and hide the rest, for example, points that belong to vegetation or streetlights. You can quickly and visually verify the classification through the color-coded representation of the points. Additionally, there are several ways to select points for classification correction, such as visual selection or using a quick profile view. We’re also working on AI classification features to enhance this process.
4. Affordability and User-Friendliness
The application is user-friendly and cost-effective, making it accessible to users of all skill levels. Additionally, there are several learning resources available, such as our training videos for all their needs: Tcp PointCloud Editor – Training Playlist.
5. Create digital models, calculate profiles and volumes
With Point Cloud, you can create a surface or mesh and generate contour lines. The symbology of models can be based on their elevations, slopes, orientations, and shading of our orthophotos. Meshes can be edited interactively or apply smoothing, peak, removal, etc. You can also import, and export surfaces and meshes, as well as 3D models in various formats.
The application has tools to import alignments in LandXML and IFC format and calculate a quick profile from the point clouds model. It is also possible to obtain a longitudinal profile and cross-sections along an alignment and draw 3D polylines on the profile. Top view sections are especially useful for BIM projects. This ensures that model management, BIM, and profiles are integrated. Additionally, it supports CAD drawing and connection with external CAD tools.
Useful resources
If you want to learn more about Tcp PointCloud Editor, you can download the trial version here or read some educational information to fully understand our solution:
🔗 Tcp PointCloud Editor Playlist
