Stereo vision is a computer-vision technique that compares two images taken from slightly different viewpoints to estimate depth. The system measures the shift between matching points in each image, called disparity, and uses that difference to infer how far away an object is. Accurate stereo depth depends on calibration, alignment, stable timing, and enough visual detail for matching.
In cyber security, stereo vision matters whenever software must judge whether something is physically present and where it is in space, such as in surveillance, access control, drones, and robotics. It can help detect intrusions, track moving subjects, and add depth information to fraud or spoofing checks. Attackers may try to confuse such systems with blur, occlusion, reflective surfaces, or flat replicas that weaken depth cues. Defenders improve reliability by combining stereo data with other sensors, checking liveness, and monitoring for inconsistent depth measurements.