This product has additional charges associated with it for usage. You will be charged a base annual fee for access to the software, along with an additional per process usage fee. RapidPipeline 3D processor offers unparalleled speed and ease of use while optimizing and converting complex 3D models for real-time 3D applications and environments.
Tailored for tech artists and developers shaping 3D pipelines, RapidPipeline 3D processor stands out as the ultimate in flexibility, scalability, and customization. Our automation software grows seamlessly with your expanding use cases, delivering top-tier quality at the most economical cost and minimal carbon footprint. Elevate your 3D pipelines with unmatched efficiency.
RapidPipeline 3D processor has many capabilities starting with topology optimization (decimation, remeshing), reducing draw calls by scene graph optimization, texture baking, unwrapping UVs, renders preview images of your 3D assets, using a CPU-based raycaster, and much more. Our proprietary optimization algorithms were developed with top tier engineers and 3D graphics PhD researchers with over a decade of experience in the field. We combine this technology and know-how with the latest compression technologies to provide optimized results with an unmatched speed and efficiency.
All these features are neatly packaged into our software and can be set up as configuration settings, to enable automatic batch processing on whole 3D asset libraries with one-click delivery for any use case. RapidPipeline 3D processor is battle-tested on millions of production assets, making it a proven solution in scalable 3D pipelines.
Highlights
Optimize your 3D scenes in various ways with RapidPipeline 3D processor's flexible feature set depending on your input data and end goals. Whether you need to reduce the poly count, re-do the topology, bake textures, cull invisible geometry, flatten the scene to reduce draw calls, or preserve hierarchy tree to use with product configurators, our tools have you covered.
Achieve better real-time performance and hardware utilisation, as well as significantly faster loading times, by leveraging asset simplification algorithms provided by RapidPipeline 3D processor.
RapidPipeline 3D processor has you covered by supporting a wide range of 3D data formats such as .fbx, .obj, .gltf, .usd, .usdz, and .vrm. Amongst other formats, our software also supports .ktx and .webp compression for texture maps.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
You pay based on usage, measured in Process units. Each unit represents one RapidPipeline 3D Processor operation, such as optimizing, converting, or compressing a 3D model. There are no fixed tiers or instance sizes to choose from. Your cost scales directly with how many operations you run. The more processing you perform, the more units you consume. This single usage-based dimension keeps billing tied to actual work done.
Top-of-mind questions for buyers
What counts as one Process unit for billing?
A Process unit is one operation run by the RapidPipeline 3D Processor on a 3D model. This includes optimizing, converting formats, baking materials, or compressing a model. Each operation you run consumes one unit. The count rises with the number of operations, not the model size or output quality.
Do I pay anything when I am not running operations?
No. Charges apply only when you run a Process operation. Idle time between operations does not accrue units. Your cost tracks the actual number of operations performed, so a period with no processing produces no usage charges under this dimension.
Is this pay-as-you-go, or do I commit to a fixed amount upfront?
This is pay-as-you-go. You are metered per Process unit as operations run, with no upfront quantity commitment. There are no fixed tiers or instance sizes to select. This structure fits variable 3D processing workloads where operation volume changes over time.
docs.rapidpipeline.com
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Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.
Version release notes
Initial Release
Additional details
Usage instructions
You have the choice of deployment via CDK (recommended) or manual deployment.
Create Task Role: Create a new IAM Role and select the previously created policy
Task Definition: Create a new task definition
Provide a name
Select launch type: AWS Fargate
Select OS: Linux/X86_64
Choose Task size based on your model requirements: For simple models 1vCPU + 3 GB RAM is enough, for more complex models you might want to increase this.
Task Role: Select the IAM role you created in step 4
Container: Use 709825985650.dkr.ecr.us-east-1.amazonaws.com/darmstadt-graphics-group-gmbh/rapidcompact-renewal:0.0.4 as Image URI
Enable "Use log collection" and select Amazon CloudWatch
Navigate to your newly created Task Definition and note the container name for running the task later
Choose a public subnet for your task to run in
Navigate to AWS VPC, there should already be a default VPC, if you prefer to use a different VPC note it's ID
Make sure your input model is uploaded to your input bucket
Then adjust the following command based on your CLUSTER_NAME (of the cluster we created), TASK_DEFINITION_NAME (name of the task definition we created in step 5), CONTAINER_NAME (the name of the container to run the task on, see step 5), SUBNET (the ID of the subnet to run the task in, see step 6), SECURITYGROUP (the security group to run the task with, see step 7), INPUT_BUCKET, OUTPUT_BUCKET, INPUT_FILENAME (make sure your input bucket contains your input file with this name), OUTPUT_FILENAME (you can choose this freely as it will be created on the output bucket):
aws ecs run-task
--cluster CLUSTER_NAME
--task-definition TASK_DEFINITION_NAME
--launch-type FARGATE
--network-configuration "awsvpcConfiguration={subnets=[SUBNET],securityGroups=[SECURITYGROUP],assignPublicIp=ENABLED}"
--overrides '{"containerOverrides":[{"name":"CONTAINER_NAME", "command":["/bin/sh", "-c", "aws s3 cp s3://INPUT-BUCKET/INPUT_FILENAME . && /rpdx/rpdx -i INPUT_FILENAME -e OUTPUT_FILENAME && aws s3 cp OUTPUT_FILENAME s3://OUTPUT_BUCKET/OUTPUT_FILENAME"]}]}'
```
10. Monitor and Support: Monitor task execution with CloudWatch logs and reach out for support via AWS Marketplace.
11. More Examples: For an example CDK stack with the proposed setup configured see here: https://github.com/DGG3D/marketplace-deployment-example
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
nervIO, a low-code/no-code framework for a rapid development of data pipelines for processing kafka events. nervIO developed with open-source technologies, and packaged as a container deployable unit. nervIO supports all runtimes of kafka including AWS Managed Service for Kafka.
It comes with a User Interface to create, manage, and deploy data pipelines using it’s built-in DevOps capabilities.
It supports good number of event driven patters for data processing, and it is built with features like, observability, pluggable rules.
It can be deployed in on-premises and cloud environment.
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