depthschatting

Depthschatting, also known as depth estimation, is the process of determining the distance from a viewpoint to surfaces in a scene, producing a depth map that encodes per-pixel depth or a 3D representation such as a point cloud. Depthschatting can be performed from images, video, or dedicated sensors.

Geometric methods include stereo matching, where depth is triangulated from pairs of images with a known baseline; multi-view stereo, which uses many views to reconstruct dense depth; and structure from motion, which recovers camera motion and sparse-to-dense depth. Direct depth measurements are provided by time-of-flight and structured-light sensors. Monocular depth estimation uses learned models to predict depth from a single image, typically trained on large annotated or self-supervised datasets.

Sensor fusion combines data from cameras with LiDAR or other ToF/structured-light sensors to improve accuracy, robustness, and scale estimation, especially in challenging environments.

Representations include depth maps (per-pixel depth), disparity maps (inverse depth from stereo), point clouds, and 3D meshes or voxel grids used for visualization and analysis.

Applications span robotics, autonomous driving, augmented reality and virtual reality, 3D reconstruction, geographic surveying, and navigation. Depthschatting is essential for obstacle avoidance, scene understanding, and metric measurements in 3D space.

Evaluation and datasets use metrics such as RMSE, mean absolute error, and absolute relative error, along with accuracy thresholds (e.g., delta < 1.25). Standard benchmarks include KITTI for outdoor scenes, NYU Depth V2 and SUN RGB-D for indoor scenes, and Middlebury for stereo. Challenges include scale ambiguity in monocular methods, occlusions, reflective or textureless surfaces, noise, and real-time computation demands.