3D Tiling Pipeline

Point Clouds Tiler

Use this command line tool to tile your point clouds into 3D Tiles.

The tiler is built from the ground up for performance and the output is optimized for visual quality and precision. The hierarchical level of detail algorithm creates tiles that will be loaded by CesiumJS as needed and contain just enough detail for any given view. As the creators of 3D Tiles, we leverage its full potential to ensure a lightweight and smooth streaming experience, even on mobile devices!

The point clouds tiler command line interface is easy to integrate into any platform, including Linux, Windows, AWS, and Azure. Convert your raw LAS data to an efficient tileset by running a single command. See the full documentation.

High performance

The tiler's multithreaded architecture makes use of every available CPU core and can process upwards of 5 million points per second on average hardware for in-core paths. An out-of-core implementation permits scaling for massive datasets.

Robust

The tiler performs a robust preprocessing step to detect the optimal configuration settings for both uniformly and non-uniformly distributed datasets. Whether your data comes from LiDAR scanners on cars, satellites or anything in between; from city-scale point clouds to detailed interior scenes, you can expect it to tile.


Preserve per-point attributes

The point clouds tiler preserves per-point attributes so you can use this metadata for dynamic runtime styling in your CesiumJS apps.

Set color, size, and visibility using a classification, intensity, or a custom expression combining multiple attributes.

Millimeter precision

The point clouds tiler preserves source data fidelity for accurate visualizations. The leaf tiles maintain the highest resolution accuracy, so once a view is fully refined you can measure and analyze with confidence.

Make real world decisions with confidence; with our roots in aerospace, precision is at the core of everything we do.

Point compression

The point clouds tiler provides an option to use Google's Draco point-cloud-aware compression library for fast streaming load times. Compressing data at a finer precision decreases file sizes at no loss, and both precision and compression can easily be tuned to have the best results for your data.

The tiler produces an average 7x reduction compared to the source LAS files and a 3x reduction compared to LAZ. Decoding in CesiumJS is fast, using WebAssembly and running in parallel using web workers.

Draco

This highly detailed cabin was compressed from a 4.48 GB LAS down to 0.67 GB.

Interested in on-premises 3D tiling?

Contact our sales team to request an evaluation, or ask about custom tiling pipelines and services for your 3D data.

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