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Master the techniques for recovering depth from images and sensors: monocular depth estimation with deep networks, stereo and multi-view approaches, active sensing technologies like LiDAR and structured light, and the metrics used to evaluate depth quality. Understand the trade-offs between passive and active methods, and learn how to refine sparse depth into dense, accurate maps.
Depth estimation is the problem of assigning a distance value to every pixel or point in a scene. It is a cornerstone of 3D computer vision because depth bridges the gap between 2D images and 3D geometry---once you have depth, you can reconstruct surfaces, measure distances, and reason about spatial relationships.
What is this chapter about? We cover the full spectrum of depth estimation methods. We begin with monocular depth estimation, where a single RGB image is used to predict depth using learned cues. We then examine state-of-the-art architectures like MiDaS and DPT that use vision transformers for robust relative depth. Multi-view stereo extends depth estimation to dense reconstruction from many calibrated images. On the hardware side, we explore active sensors---LiDAR, time-of-flight cameras, and structured light systems---that directly measure depth. Finally, we address depth completion (filling in sparse measurements) and the evaluation metrics used to benchmark all these methods.
Why does this matter? Autonomous vehicles need dense, accurate depth maps for obstacle avoidance. AR/VR applications require real-time depth for scene understanding and occlusion handling. Robotics depends on depth for manipulation and navigation. Every downstream 3D task---reconstruction, detection, segmentation---benefits from better depth estimation.
How the topics connect: We start with monocular depth as the most accessible entry point (just one image), then explore MiDaS/DPT as the leading architectures. Multi-view stereo provides denser and more accurate depth when multiple images are available. Active sensors (LiDAR, ToF, structured light) offer direct physical measurements. Depth completion bridges sparse sensor data with dense predictions. We close with evaluation metrics that let you quantitatively compare all these approaches.
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Predicting dense depth from a single RGB image using learned monocular cues and encoder-decoder networks.
Learned architectures and geometric approaches
State-of-the-art architectures that combine mixed-dataset training with Vision Transformers for zero-shot depth.
Dense depth from multiple calibrated images using photometric consistency and plane-sweep volumes.
Active sensing — direct measurement without learned inference
Active sensors that directly measure depth using time-of-flight or spinning laser principles.
Projecting coded patterns onto scenes and recovering depth from pattern deformation via triangulation.
Sparse-to-dense refinement and benchmarking
Filling sparse sensor depth into dense maps using guided upsampling and self-supervised refinement.
Quantitative metrics for depth quality — absolute error, threshold accuracy, and scale-invariant measures.
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