Deep learning requires the use of a huge amount of training data to develop and test a successful neural network model that generalizes well. Several public datasets are already available for different kinds of object recognition and autonomous cars, but none exist for aeronautic applications. We present a data-driven pipeline for the creation of synthetic imagery and the acquisition of real imagery and data for the development of autonomous flying vehicles.

July 28th 10:40 – 11:20 PDT
Anastasio Garcia
Head of Simulation
Harvest Zhang
Head of Perception
Alexis Casas

1st. Presenter: Anastascio Garcia

Summarize the Airbus pipeline and what makes it different compared to a VFX pipeline and let the audience know what key points will be discussed in the presentations, first by your’s truly, then by Harvest.

The Data-driven pipeline vs synthetic
Data is the king. We need large amounts of *quality* data — for our supervised learning techniques, we need both the raw data itself and accurate ground truth labels (implicit in simulation, a significant challenge for real data).
* Comparison to VFX pipeline.
* We don’t have an army of 3D artists. Scalability considerations.
* Large-scale, outdoor scenarios (need automatic solutions)
* VFX’s final product is a movie (i.e. many frames). We use many frames (not necessarily consecutive frames) to produce a neural network model.
* No offline rendering (yet)
* Hybrid pipeline: synthetic and real imagery are currently independent of each other (i.e no compositing of synthetic over real, but that could be a future use case)

* Reference frames (real world and virtual world)
* GPU-based rendering engine (OpenGL / Vulkan). XPlane, Unreal Engine
* Flight dynamics model (i.e. physics engine)
* Environmental conditions: weather, time of the day
* Automatic labeling of relevant features. CGI to provide ground-truth labels
* Failure case: procedural city creation with Houdini

2nd. Presenter: Harvest Zhang

Real data
* On-board Wayfinder Laboratory: aircraft instrumented with an array of sensors
* Camera calibration
* Data labeling pipeline: from the aircraft to the data center, from raw sensor data to labeled ground truth for NNs to consume

What does the future hold for the Airbus pipeline?

What are some of the changes that will happen and what are some of the things that we want to do?

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