ADAS 360 Top-View Camera Stitching Calibration Mats PDF
AUDI ADAS 360° Calibration Mat System (Digital PDF Download)
Precision Reference Targets for Surround-View Camera Recalibration
Enhance your ADAS service operations with our ADAS 360° Calibration Mat System, delivered as a high-resolution PDF file ready for printing at full scale. This digital product provides a set of engineered, high-contrast reference targets developed to support the calibration routines of modern 360° surround-view and parking camera systems.
These printable mats are designed for service professionals, mobile calibration providers, and advanced DIY users who require reliable, repeatable camera alignment without investing in expensive proprietary equipment. The pattern geometry enables onboard vehicle software to identify visual markers, correct distortion, and realign stitched camera views.
Digital Product – What You Receive • High-resolution PDF files of the left, right, and center calibration mats • Print-ready layout sized for 1:1 scale output • Usage guidelines for optimal positioning and setup • Lifetime access to the purchased files
Note: This is a digital download. No physical mats are shipped. You may print the mats on vinyl, banner material, or any non-glare large-format substrate.
Printed Product - What you will Receive • Printed 2 mats on regular plotter paper • Cylindrical storing container
Key Features • Compatible with Audi systems. • High-Contrast Visibility The bold, engineered linework is optimized for quick camera recognition in a variety of lighting conditions. • Cost-Efficient Calibration Avoid costly shop equipment by printing your own reusable calibration targets. • Portable & Field-Ready Ideal for mobile technicians and facilities requiring rapid deployment in diverse environments.
Operational Benefits • Restores accurate 360° imaging and stitched-view alignment • Supports recalibration after camera replacement, software resets, or repairs • Reduces diagnostic time and enhances service efficiency • Provides professional-grade outcomes with a scalable, low-overhead toolset