Point cloud formats: overview and practical use

In this article, we take a look at the following questions: Which formats are currently common? What are their strengths, and where do they reach their limits? Which formats are particularly well suited for efficiently visualizing large point clouds or collaborating on them?

Example of a digitized flyover.

LAS was originally developed for airborne laser scanning. It stores coordinates as scaled integers and has a robust structure. The compressed variant, LAZ, significantly reduces file size, a real advantage when working with large datasets.

However, the format reaches its limits with terrestrial scans. Structured scans with row-column logic, panoramic images, or additional metadata cannot be represented. It is well suited for simple, unstructured point clouds, but less so for complex indoor projects with photo documentation.

Diagram of the E57 format.
Source: https://www.ri.cmu.edu/pub_files/2011/1/2011-huber-e57-v3.pdf.

E57 was developed to fill exactly these gaps. It can store both structured and unstructured scans, supports panoramic images, and manages additional information such as scanner positions or sensor data within a single file.

However, this versatility comes at a cost: the format is technically complex, both for developers and users. Some software solutions interpret E57 slightly differently, which can lead to compatibility issues, especially with metadata or images. Loading certain contents can also be slow at times, which may cause delays in large projects.

That said, anyone who values standardization, completeness, and future-proofing will find E57 hard to ignore.

Many scanner manufacturers rely on proprietary file formats, giving them full control over structure, content, and compression. These formats contain not only point data but also additional information such as calibration values, raw data, or scanner positions. They are usually essential for the initial processing, but exchanging data with other software becomes complicated.

The biggest challenge: Many of these formats are tightly coupled with the manufacturer’s software, often undocumented, and the necessary SDKs (Software Development Kits) are only available with restrictions or for a fee. This creates a certain dependency on the respective provider, which can limit long-term flexibility.

Modern laser scanners capture several hundred million measurement points per location. In large projects with many locations, this quickly adds up to terabytes of data. Even with compression, these data volumes are too large to be fully loaded into memory or onto a graphics card.

Another issue: Traditional formats usually need to be fully loaded before anything can be displayed. This results in long wait times, especially when working via cloud or network storages.

Streaming formats address exactly this challenge. They organize point clouds using so-called octrees, hierarchical data structures that make it possible to load only visible data first and stream in details as needed. At the same time, the points are available in multiple levels of detail. This allows smooth zooming and navigation, even with slower internet connections.

Thanks to this structure, hardware and network requirements are significantly reduced. In combination with caching techniques, even massive point clouds can be visualized and analyzed in real time. 

These formats are typically designed for web-based applications. Providers such as NavVis, Faro, PointSharePlus, and others offer commercial solutions for storing, managing, and sharing large point cloud projects in the cloud.

Gradual subdivision of a point cloud into an octree data structure.
Source: https://www.cg.tuwien.ac.at/research/publications/2020/SCHUETZ-2020-MPC/

Potree was originally developed for web-based visualization. In its first version, each octree node was stored as a separate file. In large projects, this quickly led to millions of individual files, making copying, backing up, and transferring the data time-consuming.

Potree 2 solves many of these issues through optimized storage, modern compression, and improved conversion.

EPT (Entwine Point Tile) comes from the GIS field. It stores many small LAZ tiles but also struggles with the high number of files.

CopC (Cloud Optimized Point Cloud) is a modern evolution: the entire octree is embedded within a single LAZ file. Existing tools can use these files, even if they don’t fully understand the specific structure. This saves time during transfer, without sacrificing compatibility.

The chosen point cloud format has a major impact on storage requirements, loading times, data exchange, and further processing. Traditional text-based formats are hardly practical today. Open standards like E57 or LAZ provide a solid foundation for flexible workflows. Proprietary formats are often well suited for internal processes but quickly reach their limits when it comes to data exchange.

Anyone working with very large point clouds, or in the cloud, should opt for modern streaming formats like CopC or Potree 2 from the outset. This ensures high performance and scalable projects.

Practical tip:

• For data exchange: use standardized and compressed formats like E57
• For processing: use manufacturer formats, but stay within the same system
• For large, collaborative projects: plan a streaming strategy early on

Qbitec takes a practical approach here: it brings the advantages of streaming formats directly into Revit. Thanks to a smart data structure and efficient caching, users can switch quickly between levels of detail even in very large projects, without loading times or data loss. This keeps access to the point cloud flexible, high-performing, and independent of project size or network environment, including panorama integration and smart visualization.