Camerasranging

Camerasranging is the field of estimating distance and 3D structure from camera data alone or in combination with other sensors. It includes passive techniques that derive depth from imagery and active techniques that project light to aid ranging. In stereo cameras, depth is computed from corresponding points in left and right images using epipolar geometry; the result is a disparity map and a depth map once calibration is known. Structure-from-Motion reconstructs 3D structure from multiple images, often with moving cameras, and can produce scalable depth estimates. Monocular camera ranging uses cues from a single view or learned priors to infer depth, but often requires scale estimation and may be less accurate without additional constraints.

Active camera ranging methods include structured-light, where a known pattern is projected and deformations observed by the camera, and time-of-flight, which measures return time of light to compute distance. Photometric and geometric cues, as well as learned priors, are used to improve depth in challenging scenes.

The accuracy of camerasranging depends on factors such as camera calibration, baseline, image resolution, focal length, lighting, and scene texture. Challenges include occlusions, motion blur, reflective or transparent surfaces, and non-Lambertian reflectance. Applications span autonomous vehicles, robotics, augmented and virtual reality, architectural surveying, and industrial inspection. In practice, many systems fuse cameras with other sensors such as LiDAR or radar to enhance robustness and scale estimation. Ongoing research emphasizes deep learning-based depth estimation, self-supervised training, real-time processing, and robust cross-domain generalization.

See also: depth estimation, stereo matching, structure from motion, simultaneous localization and mapping, multimodal sensing.