Key Takeaways

• Integrating MetriCal into Evercoast’s capture pipeline standardized intrinsic and RGBD calibration, improving consistency at scale.

• More reliable per-sensor calibration reduced setup time and operational overhead when deploying multi-camera systems.

• Higher-quality, more consistent input data for Evercoast’s multi-camera registration system led to improved 4D reconstruction outputs and more usable datasets for training physical AI and robotics systems.

The Mission

Evercoast set out to improve the consistency and quality of sensor-level calibration across its capture systems to better support large-scale data collection for AI training. The goal was to strengthen intrinsic and intra-device calibration in a way that integrates cleanly into production workflows and improves downstream reconstruction quality, without adding operational complexity or disrupting existing system architecture.

Evercoast recognized this need from the very beginning, and was one of the first companies to reach out to Tangram Vision for calibration solutions. They provided early pressure-testing of MetriCal and were valuable partners when Tangram was hardening the software for wider adoption across computer vision and physical AI. The results speak for themselves.

Building a Scalable 4D Data Collection Platform

Evercoast develops multi-camera capture systems designed to collect synchronized, high-quality spatial data for a range of applications, including physical AI, robotics, simulation, and industrial workflows. These systems combine tightly coordinated sensor arrays, capture software, and downstream processing pipelines to generate structured datasets that can be used for training, analysis, and real-time applications.

While Evercoast supports media and entertainment use cases, a significant portion of its deployments leverage depth sensor arrays, using RGBD devices such as RealSense, Orbbec, and others. These systems are often deployed to capture real-world motion, interactions, and tasks at scale, where consistency across sensors and over time is essential.

A key aspect of the platform is its ability to operate across a wide range of configurations, from smaller setups to large, dense camera arrays. This flexibility enables Evercoast to support diverse use cases while maintaining a consistent approach to synchronization, data capture, and downstream processing.

The Calibration Gap at the Sensor Level

Despite having a robust and proprietary solution for multi-camera extrinsic registration, Evercoast identified a distinct challenge at the individual device level.

RGBD cameras provide strong performance and flexibility, but maintaining consistent intrinsic calibration and accurate alignment between depth and color sensors within each device is critical. These intra-device calibration factors directly affect the quality of the captured data before it ever reaches the multi-camera reconstruction stage.

Prior approaches may have been effective, but they did not provide the level of consistency required when deploying large numbers of sensors across different environments. Variability at the sensor level introduced noise into the pipeline, which became more significant as datasets scaled for AI training.

The need was clear: we needed to improve intrinsic and intra-device calibration without overlapping with or replacing Evercoast’s multi-camera system.

Integrating MetriCal into the Capture Pipeline

Evercoast integrated MetriCal directly into its capture software to address this specific layer of calibration.

MetriCal is used to calibrate individual devices, refining intrinsic parameters and improving alignment between IR depth and RGB streams within each sensor. This ensures that each device produces internally consistent data before entering Evercoast’s broader system.

This creates a clean architectural separation:

• MetriCal handles intrinsic and intra-device calibration

• Evercoast’s system handles global multi-camera extrinsic registration and 4D reconstruction

Calibration is typically performed during initial system setup and commissioning, and in some cases during maintenance cycles when systems are redeployed or show signs of drift. Because MetriCal is embedded into the workflow, calibration outputs are immediately usable and require minimal manual intervention.

Low Interference, High Impact For Physical AI

The integration of MetriCal has improved both the efficiency of deployment and the quality of collected data. Plus, with a more reliable calibration baseline at the sensor level, Evercoast’s engineering team can focus on higher-level system performance rather than troubleshooting calibration inconsistencies.

From an operational standpoint, calibration workflows are now more structured and repeatable. This reduces setup time and eliminates much of the manual iteration previously required. From a data quality perspective, the impact is even more substantial. Improved intrinsic calibration and better alignment between depth and color sensors result in:

• More consistent geometry across captures

• Reduced artifacts in reconstructed sequences

• Improved temporal stability in dynamic scenes

These improvements translate directly into more reliable datasets. Consistency across captures is critical when training models for robotics and physical AI, where even small variations can introduce noticeable noise and degrade performance.

Overall, MetriCal strengthened a foundational layer of Evercoast’s system by making calibration more predictable, scalable, and production-ready. Now Evercoast can move forward confidently into more and greater reconstruction projects.