Vision AI for RealSense Depth Cameras

RealSense cameras see in 3D. Roboflow tells them what they are looking at: detection, segmentation, and tracking fused with depth.

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A warehouse aisle with tall shelves and boxes, featuring a single cardboard box on the floor highlighted with a purple bounding box and labeled "obstacle - 1.8 m".

RealSense Cameras AI Vision Solution

RealSense makes the depth cameras robotics teams reach for first. Depth tells a robot how far away something is; a Roboflow model tells it what that something is. Together they cover the two questions every picking, navigation, and inspection application has to answer.

The D555 PoE (opens in a new tab) is the newest depth camera in the line, bringing Power over Ethernet and tight NVIDIA integration to the stereo depth platform robotics developers already know.

• Flagship: D555 PoE depth camera

• Attributes: Stereo depth, mature SDK, large robotics ecosystem

• Connectivity: PoE, USB

Run a Roboflow detection or segmentation model on the RGB stream, fuse results with the depth map, and your application knows both what it sees and where it is in space.

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Want AI without the integration work? Roboflow’s AI1 deployment device (opens in a new tab) packages onboard AI compute, integrated lighting, and an industrial camera into a plug-and-play system for real-time computer vision - no separate camera, lighting, or edge computer to spec.

AI for RealSense Cameras Workflows

Use RealSense cameras? Explore Workflows you can get started with to create computer vision apps in minutes. Run detection and segmentation models with Roboflow Inference on the RGB stream and fuse results with depth for robotics, picking, and measurement applications.

Deploy Object Detection Models on Edge Devices

How to Setup RealSense Cameras with AI

RealSense cameras can be used for computer vision tasks such as object detection and image segmentation alongside their depth sensing. They integrate through the RealSense SDK and pair naturally with an NVIDIA Jetson for running computer vision models at the edge.

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