For example, what types of variations they need on the materials (color, roughness, specific types of damage, etc.), then I’ll ask them to provide any reference photos they have.įrom there, I’ll start with the base cardboard or the paper material, then layer in the decors (stickers and logos), and at the end add in the damage. Usually, I start with meeting the internal customers to understand their specific needs for the project. The node graph makes it clear and easy to use, and Designer allows me to build custom materials that can batch-generate thousands of various textures with one Substance graph. I think Substance 3D Designer is a powerful tool and the learning curve is not extremely steep. Haining: For the box generation, I work on the procedural material using Substance 3D Designer. This dataset will play a crucial role in training data perception models with randomized inputs. Through this process, we have successfully produced boxes with countless variations in terms of shape, size, color, and texture, resulting in thousands of unique combinations. Therefore, our goal was to develop a workflow that would allow us to automate the generation of diverse types of boxes and apply different materials to them. Considering the vast range of products sold by Amazon, it would have been extremely challenging to include every single item in the dataset. As mentioned previously, we have recently designed a new pipeline dedicated to creating a representative dataset of ASIN (Amazon Standard Identification Number) packaging. Haining: In the past year, we have developed multiple workflows by leveraging various DCC tools and NVIDIA Omniverse. This is a huge project because there is a large quantity of different objects in the warehouse, and we must incorporate variations in conditions on equipment and infrastructure to mimic real-life situations. Here is an example of a sample we generated.Īnother project we are working on is recreating one of the Amazon warehouses in 3D for various simulations and testing. We use Houdini, a powerful software tool capable of performing mesh modifications, for procedural mesh generation, and Substance 3D Designer for procedural texture generation and loading them into Omniverse. Our team can run simulations in 3D environments with large quantities of completely procedural boxes which saves time and money. Haining: A recent project involved creating procedurally randomizable boxes from Amazon and other vendors. Joining us today are Haining Cao and Hunter Liu from the Virtual Systems team, who will discuss the integration of Substance 3D and Omniverse. To automate this process, the team adopted Substance 3D and Houdini to create thousands of random, procedurally generated assets. The challenge of visualizing and running these simulations lies in the creation of thousands of digital twins, i.e., virtual packages. Comprised of software development engineers and technical artists, the team collaborates to create diverse models representing numerous solutions within Amazon. In late 2021, the team adopted Adobe Substance 3D, Universal Scene Description (USD), and NVIDIA Omniverse, a software platform for connecting 3D pipelines and developing advanced, real-time 3D applications, to enhance the development of 3D environments. In this article, we will learn how Amazon Robotics tackles this challenge for warehouse operations by leveraging the procedural power of Substance 3D combined with NVIDIA Omniverse. This applies to a wide variety of industries, such as autonomous driving, blueprint editing in architecture, defect detection in manufacturing, and much more. One of these challenges is the creation of massive amounts of 3D assets to train AI perception programs in large-scale real-time simulations. The increasing importance of AI and synthetic data to run simulation models comes with new challenges. Amazon Robotics combines the power of NVIDIA Omniverse and Adobe Substance 3D to simulate warehouse operations
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