Stream massive point clouds over the web
Tile your point clouds into 3D Tiles for efficient streaming and visualization with CesiumJS.
As the creators of 3D Tiles we leverage its full potential to ensure a lightweight and smooth streaming experience, even on mobile devices!
Upload your point clouds for tiling on Cesium ion, or use the on-premises 3D Tiling Pipeline Command Line Interface (CLI). The CLI is easy to integrate into any platform, including Linux, Windows, AWS, and Azure. It's built from the ground up for performance and optimized for the best visual experience and precision.
Dynamically style with per-point attributes
The 3D Tiling Pipeline preserves per-point attributes so you can use them to control a point's color, size, or visibility using the 3D Tiles Styling Language.
Leverage the GPU acceleration in CesiumJS to dynamically style points at run-time using a classification, intensity, or a custom expression combining multiple attributes.
Faster streaming with point compression
To optimize load times, the 3D Tiling Pipeline provides an option to use Google's Draco point-cloud-aware compression library. Both precision and compression can easily be tuned to have the best results for your data.
Compressing data at a finer precision decreases file sizes at no loss. This highly detailed cabin was compressed from the original 4.48 GB LAS down to 0.64 GB.
The 3D Tiling Pipeline 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 runs in parallel using web workers.
|1.83 GB||0.67 GB|
Draco produces far better compression rates than general purposes algorithms like Gzip.
Easy to use
The 3D Tiling Pipeline brings together all the algorithms you need in an easy-to-use configurable CLI. Convert your raw LAS data to an efficient tileset that's ready for streaming in a single command.
The point cloud pipeline is written in C++ and makes use of every available CPU core, resulting in a significant performance advantage.
The multithreaded architecture overlaps I/O and compute, and an out-of-core implementation permits scaling for massive datasets.
The point cloud CLI can process around 5 million points per second on average hardware for in-core paths.
The 3D Tiling Pipeline produces tilesets that are optimized for the web. Tile sizes are tuned for efficient network traffic. Tiles are computed to be a visually representative subset at any given view using a precise geometric error so tilesets can stream faster.
The Hierarchical Level of Detail (HLOD) algorithm allows tiles to be loaded only when needed and contain just enough detail for any given view. Additive refinement allows higher resolution data to smoothly transition in.
The 3D Tiling Pipeline performs a robust preprocessing step to detect the optimal configuration settings for your data. No need to waste time digging through layers of configuration.
We've tested it with everything from city-scale point clouds to detailed interior scenes, and optimized for both uniformly and non-uniformly distributed data sets.
Whether your data comes from LiDAR scanners on cars, satellites or anything in between, you can expect it to tile.
Cesium is used by many to make real world decisions, and that's why precision is at the core of everything we do.
The 3D Tiling Pipeline has no trouble with points at millimeter precision even on mobile devices. No data is lost at any step in the pipeline.
The leaf tiles maintain the highest resolution accuracy so once a view is fully refined you can measure and analyze with confidence.
Check out the full documentation for the on-premises 3D Tiling Pipeline.
We provide custom tiling pipelines and services for your 3D data so you can focus on developing your CesiumJS app.