Is Point Cloud Data Eating Up Your Storage space

Introduction

In the age of digital transformation, industries like construction, engineering, and manufacturing increasingly rely on laser scanning and LiDAR technologies for accurate, high-resolution 3D data. This data, represented as point clouds, forms the backbone of modern design, analysis, and operational workflows. However, the sheer size of point cloud files presents a growing challenge. With projects easily producing terabytes of data, managing, storing, and processing this information has become a critical pain point for businesses. This blog explores why point cloud data consumes so much storage and presents effective data management strategies to handle these large files.

What is Point Cloud Data?

Point cloud data consists of millions—or even billions—of points in 3D space, each representing a precise location captured by laser scanning devices. These datasets are used to create detailed models of physical environments or objects. While their utility is unparalleled in creating accurate 3D models, point clouds can quickly overwhelm storage systems due to their high density and the additional metadata they carry, such as color, reflectance, and intensity.

The Growth of Point Cloud Data

• Higher Resolution Scans: Modern scanners capture finer details, leading to exponentially larger files.
• Increased Scan Frequency: Projects now involve scanning at various stages for better project tracking, further adding to data volume.
• Demand for Complex Models: Industries demand intricate models for precision engineering, necessitating denser point clouds.

Why Does Point Cloud Data Consume So Much Storage?

• File Size: A single scan can generate gigabytes of data, and larger projects requiring multiple scans can reach terabytes.
• Redundancy: Overlapping scans or redundant data points are common in large projects, increasing file sizes unnecessarily.
• Data Formats: Common formats like LAS, PLY, or E57 store additional metadata, further inflating file sizes.
• Processing Versions: Every time data is cleaned, segmented, or processed, new versions are generated, multiplying storage requirements.
• Retention Policies: Regulations or internal policies often require data to be archived for years, leading to long-term storage burdens.

Challenges in Managing Large Point Cloud Files

• Hardware Limitations: Legacy storage systems often lack the capacity and speed to handle large datasets effectively.
• Cost: High storage costs for on-premise or cloud solutions can strain budgets.
• Data Accessibility: As datasets grow, retrieving and transferring files becomes slower and more cumbersome.
• Collaboration Issues: Large files can be challenging to share with remote teams or stakeholders.
• Data Loss Risks: Without proper backup strategies, the risk of losing critical data increases significantly.

Data Management Strategies for Point Cloud Data

Effective data management ensures that point cloud data is stored, processed, and shared efficiently without compromising accessibility or security. Here are some proven strategies:

• Optimize Data Collection
• Set Resolution Appropriately: Avoid overscanning by adjusting the scanner's resolution and density to meet project-specific requirements.
• Scan Planning: Plan scans strategically to minimize redundancy.
• Data Reduction and Compression
• Use Compression Formats: Employ formats like LAZ, which compress point cloud files without losing data integrity.
• Decimate Data: Reduce the number of points in non-critical areas while maintaining accuracy in important regions.
• Eliminate Redundant Data: Tools like Faro SCENE or Autodesk Recap can filter out overlapping points.
• Adopt Cloud Storage Solutions
• Scalability: Cloud platforms like AWS or Microsoft Azure offer scalable storage tailored for massive datasets.
• Collaboration: Cloud systems enable remote teams to access and process data seamlessly.
• Cost Efficiency: Pay-as-you-go models allow businesses to manage costs based on actual usage.
• vest in On-Premise Storage Systems
• RAID Configurations: Redundant arrays of independent disks (RAID) improve data reliability and speed.
• Network-Attached Storage (NAS): NAS systems offer a centralized storage solution for teams working on point cloud data.
• High-Capacity Drives: Invest in drives specifically designed for handling large datasets.
• Leverage Data Management Software
• Version Control: Implement tools like Bentley ProjectWise to manage versions and avoid duplication.
• Metadata Tagging: Tag files with metadata for easier organization and retrieval.
• Automated Cleanup: Use software that automates redundant data removal.
• Archive Strategically
• Cold Storage Solutions: Store infrequently accessed data in cost-effective cold storage systems.
• Retention Policies: Establish clear policies to determine which data should be archived or deleted.
• Use Efficient Workflows
• Real-Time Processing: Process data on-site to eliminate unnecessary scans and reduce storage needs.
• Streaming Workflows: Stream point cloud data directly to software instead of storing raw files locally.
• Enable Data Interoperability
• Format Standardization: Use interoperable formats that multiple software platforms can read efficiently.
• Centralized Platforms: Invest in platforms that support multi-user access to streamline workflows.
• Monitor and Audit Storage
• Usage Analytics: Regularly audit storage to identify inefficiencies.
• Automated Alerts: Set alerts for when storage usage nears capacity.

Case Studies

Case Study 1: Construction Firm Tackles Data Bloat

A construction company scanning a 500,000 sq. ft. site reduced its data storage needs by 40% by compressing files into LAZ format and using cloud-based storage for collaboration.

Case Study 2: Point Cloud Data in Oil & Gas

An oil and gas company implemented automated cleanup tools, reducing redundant scans by 30%. Archiving non-critical data to cold storage saved $15,000 annually.

Case Study 3: Architecture Firm Uses Streaming

An architectural firm adopted streaming workflows, enabling designers to access point cloud data in real time without downloading raw files. This reduced local storage requirements by 50%.