DRIVING SIMULATIONS FOR

AUTONOMOUS VEHICLES WITH

3D TILES AND CESIUM

Tom Fili

Cesium

tom@cesium.com

@CesiumFili

What is Cesium?

• CesiumJS• Open-source JavaScript library for rendering 3D globes and 2D maps

• Apache 2.0 License

• Runs in a browser and on mobile

• Cesium ion• Online service for processing and efficiently streaming data

• Accepts point clouds, terrain, imagery, photogrammetry, BIM/CAD models, CityGML

• Free for non-commercial use/paid enterprise accounts

• https://cesium.com/ion to sign up

What is Cesium?

• Specification for streaming massive heterogeneous 3D geospatial datasets.

• Open Specification and Open Source implementation in Cesium.

• Allows custom tiling schemes.

• Declarative Styling.

• Adopted by OGC as a Community Standard in February 2019.

https://github.com/AnalyticalGraphicsInc/3d-tiles

Demo

• Autonomous cars use many sensor types that generate TBs of heterogeneous geospatial data• Telemetry

• GPS

• Gyroscope

• Accelerometer

• Point clouds• LIDAR

• Photogrammetry• Camera

Data…a whole lot of it

• What gets collected• Position

• Orientation

• Velocity

• Acceleration

• How can we stream it• CZML

• JSON schema for time-varying values

• Distribute simulation across multiple CZML files…like streaming video

• Can be real-time or playback

Telemetry data

Telemetry data

• What is collected• Position• Color• Intensity

• How can we stream it• Streamed as a single 3D Tiles point cloud tile• No level of detail• Can handle 500k points before we need to LOD tiling

• How can we make it fast• Draco compression

• 4x smaller than uncompressed 3D Tiles

• 2x faster in CesiumJS

Dynamic Point Clouds

Next Generation

• GPU-accelerated sensor visibility

• Multiple geometry type including cones, rectangles, and domes

• Custom sensor geometries

View Sheds

• Can be billion or even trillions of points

• How can we stream it• Points

• Meshes

• How can we make it fast• Intermediate LODs

• Progressively load in more points

• We provide tilers as part of Cesium ion

• Draco compression

Tiled Point clouds

• How can we make it fast (cont.)• Adaptive spatial subdivision

Tiled Point Clouds

• Hardware• 4 core i7 with 16GB of RAM

• 185.3 million points in 109 seconds

• 6.4 billion points in 24 minutes

Point Cloud Tiling Performance

• Can be tens of thousand of photos

• Output models can very large

• How can we make it fast• Intermediate LODs

• Draco compression• Slightly better than point clouds (~5x compression)

• Adaptive spatial subdivision

• Skipping levels of detail• Children replace the parents, so we can skip unneeded higher level tiles

Photogrammetry

Photogrammetry

• Hardware• 4 cores with 16 GB of RAM

• 0.3 square km and 1.4 million triangles

• 4.5 minutes

• Hardware• 16 cores with 128 GB of RAM

• 12cm with heavy textures

• 5 minutes per square km.

Photogrammetry Performance

• The intersection of the geometry, e.g., bounding boxes, classifies the environment

• Geometry tilesets can be generated in a variety of ways (eg. open datasets, machine learningalgorithms, etc.)

Classification

Classification

• Dynamic data• CZML

• Time Dynamic point clouds

• Large data collections• 3D Tiles

• Cesium ion

Summary

Come see us at our booth

https://cesium.com

Twitter: @CesiumJS