In agriculture the main ones are collision avoidance, human-machine safety - detecting and computing distance to an object nearby, then crop analysis - counting, measuring the dimensions and estimating ripeness. Warehouse adds use-cases such as inventory tracking, through volume estimation and barcode reading. Navigation (stereo VSLAM is more robust than mono), bin-picking, palletization, free-space estimation for cargo load. Those and many more require accurate depth estimation and strereo vision is the most universally applicable way of doing it, while being accurate and fast enough!

Other verticals are smart cities, AMRs, manufacturing, security, and retail. Specific uses range from person detection, license plate recognition, demographic analysis, occupancy measurement, hazard zone monitoring, quality inspection, and street/road monitoring.

Hi, there is too much more to OAK than Realsense or ZED so I will just try to tackle the Computer Vision/ML part. ZED and Realsense are basically 'simple' depth cameras, where ZED also requires external GPU to produce depth. With OAK 4 I can Use all sensor data (High-res images, Depth, IMU), run e.g. Object detection with YOLO, fuse it with depth to compute e.g. object distances and finally send those distance values out to my app/platform. You can use well established libraries like OpenCV, PyTorch and build your whole pipeline on-device so you save bandwitdth and do not need any host PC in most situations. OAK 4 is so powerful that you can run e.g. YOLOv8 Large at 85 FPS or DINOv3 at 40FPS. SImple models like Yolov6 and Text recognition or QR code decoding are running at 500+ FPS so there is plenty of room to combine them to get full vision stack.