The parallax effect in computer vision refers to the apparent shift in the position of an object when viewed from two different viewpoints. This phenomenon occurs because objects closer to the observer appear to move more relative to the background when the viewing angle changes. In practical terms, computer vision systems use this displacement (called “disparity”) to estimate depth or 3D structure. For example, stereo cameras capture images from slightly offset positions, and algorithms analyze the differences between these images to calculate distances. The greater the disparity between matching points in the two images, the closer the object is to the camera. This principle is foundational for tasks like depth mapping and 3D reconstruction.
A common application of the parallax effect is in stereo vision systems, such as those used in robotics, autonomous vehicles, or augmented reality. For instance, self-driving cars use dual cameras to detect obstacles by measuring how much objects shift between frames as the vehicle moves. Similarly, smartphones with dual-lens cameras leverage parallax to create depth maps for portrait mode, blurring the background while keeping the subject in focus. Another example is structure-from-motion (SfM), where a single camera moving through a scene captures multiple images. By tracking how features shift (parallax) across these images, algorithms reconstruct the 3D geometry of the environment. These applications rely on precise correspondence matching—identifying the same feature in both images—to compute accurate depth.
Implementing parallax-based depth estimation involves challenges. First, correspondence matching can fail in textureless regions or repetitive patterns, leading to errors. Techniques like semi-global matching (SGM) or deep learning models (e.g., CNNs for disparity prediction) address this by incorporating contextual information. Second, camera calibration is critical: small errors in aligning stereo cameras or estimating their intrinsic parameters (like focal length) can distort depth calculations. OpenCV’s stereo calibration tools are often used to mitigate this. Finally, computational efficiency matters. Real-time systems, like those in drones, optimize algorithms (e.g., block matching) to balance speed and accuracy. By combining parallax with other sensors (LiDAR, IMUs) or using multi-view setups, developers improve robustness in complex scenarios like dynamic scenes or low-light conditions.