top of page
PointCloud of the Storage Facility

Expertise in LiDAR Laser 3D Data Collection and Processing - Accurate, Complete, and Photorealistic 3D Images

Enhance your 3D mapping and surveying capabilities with our Lidar Point Cloud services. Utilizing state-of-the-art FARO scanners, we create accurate, complete, and photorealistic images of any environment or object, providing precise measurements and insights for your project needs.

 Experience accurate measurement capture for any space with our cutting-edge technology.

Container PointCloud

Expert LiDAR Point Cloud Services:

We offer 3D LiDAR point cloud captures with a range of 70m and 150 m. Examples of LiDAR scanner collection include:

Our team specializes in providing high-quality 3D LiDAR point cloud captures with a range of 70m and 150m. We have extensive experience in capturing data for various applications, including large laboratory equipment, architectural facades, construction sites, complex structures, production and supply facilities, and the evaluation of damaged cars, trucks, and equipment. Contact us today to learn how we can support your project with our LiDAR point cloud services

Orthoimages for construction and mapping
Our reality capture services provide value for your construction or repair project at each phase:  planning, design, and construction. 3D CAD models of the site give architects, engineers, and construction professionals the ability to provide the best possible service. We deliver custom  LiDAR collection services, ranging from raw point cloud data to custom CAD models.
Our areal photogrammetry services represent reality for powerful 3D visualization while providing geospatial information for accurate mapping.  They are used to create:
  • orthoimages
  • 3D plans
  • 3D models 
Point clouds can be integrated into almost any CAD platform to perform standard fieldwork while at the convenience of a desk. 

Unlock the power of AI with high-quality point cloud data

Our LiDAR point cloud segmentation services provide an accurate and efficient classification of point clouds into different regions, making it possible to analyze and understand the 3D structure of a scene.

Our expert team utilizes advanced techniques for 3D point cloud segmentation, including X, Y, and Z coordinates as well as other optical properties such as reflectance and color, to achieve high-quality results.

We also provide multiple scan position registration, or stitching, to create comprehensive point clouds of an entire scene.

With our services, you can overcome the challenges of high redundancy, uneven sampling density, and lack of explicitly labeled structure of point cloud data. Our segmentation process is a fundamental step in processing 3D point clouds and is essential for various applications such as object recognition and scene understanding

  • What is data collection planning for LiDAR sensor data, and why is it crucial for AI model development?
    Data collection planning is the process of deciding how and where to gather LiDAR data for training AI models. For instance, if you're building an autonomous drone, data collection planning ensures you obtain the right data to teach it to avoid obstacles.
  • Why is data diversity important in LiDAR data collection planning, and can you provide examples of diverse data sources?
    Data diversity ensures your AI model can handle different situations. For a LiDAR-based agricultural robot, you need data from various crop types, soil conditions, and weather conditions to improve crop management. If you're building an autonomous drone, obtain data from the urban enviroment.
  • What are common challenges in LiDAR data collection planning, and how can they be addressed?
    LiDAR data collection on moving platforms, such as autonomous vehicles, drones, or mobile robots, introduces unique challenges. The motion of the platform can affect the quality and accuracy of LiDAR data: Motion artifacts: The platform's movement can introduce artifacts in LiDAR scans, such as distortions, gaps, or misalignments in the point cloud data. Prior LiDAR calibration: Maintaining precise calibration between the LiDAR sensor and the platform is critical. Any misalignment can lead to inaccuracies in data collection. Data synchronization: Ensuring that data from the LiDAR sensor is synchronized with other sensors on the platform, such as cameras or GPS, is essential for accurate data fusion. Bottleneck in Real-time processing: Processing data in real-time is often necessary for immediate decision-making, which can be challenging given the large volume of data generated by LiDAR sensors. Obstacle avoidance: On moving platforms, LiDAR is frequently used for obstacle detection and avoidance. Planning for experienced drone skills and navigation in dynamic environments is crucial.
  • Can LiDAR data collection planning adapt to changing environmental conditions?
    If you're using LiDAR for monitoring a construction site, you might need to adjust the scanning schedule based on weather conditions, ensuring data collection isn't hindered by heavy rain or fog. However, if data is collected under less-than-ideal conditions, advanced post-processing techniques can help filter out noise or artifacts caused by adverse weather, making the collected data usable.

Optimizing 3D Point Cloud Segmentation for AI 


Maximize the accuracy and efficiency of your 3D point cloud segmentation with our expert data collection and preparation services. Our team specializes in identifying and classifying objects within point clouds through thorough data collection, cleaning, and labeling techniques. Trust us to prepare your data for optimal performance in your AI module.


bottom of page