Depth estimation predicts distance or relative scene geometry for pixels or image regions. Depth enables collision avoidance, mapping, AR occlusion, robotic grasping, autonomous driving, portrait effects, 3D photography, inspection, and scene-scale measurement. Metric depth uses physical units, relative depth preserves ordering or shape, and disparity is inverse-depth-like geometry for stereo. Valid range, scale ambiguity, camera intrinsics, missing values, confidence, and surface convention must be stated. A production perception claim specifies the sensor, scene distribution, label ontology, spatial and temporal resolution, operating range, latency deadline, target hardware, confidence policy, and consequence of a miss or false alarm. Dataset accuracy alone is insufficient when lighting, weather, motion, occlusion, calibration, geography, demographics, and sensor aging differ from the benchmark.

Architecture, representation, and operating mechanism. Stereo matches rectified views and triangulates disparity; monocular networks infer learned geometric priors; structured-light systems project known patterns; time-of-flight measures modulated or pulsed delay; lidar samples direct ranges. MiDaS, DPT, and Depth Anything emphasize broadly pretrained monocular depth. Stereo searches correspondence along epipolar lines, ToF estimates phase or travel time, structured light decodes pattern deformation, and monocular models map RGB features to depth distributions. Fusion and completion combine sparse active ranges with dense image predictions. Absolute relative error, RMSE, scale-invariant error, threshold accuracy, bad-pixel disparity, completeness, edge accuracy, temporal consistency, range, precision, confidence calibration, latency, power, and depth-to-point-cloud geometry matter. Cameras, lidar, radar, IMUs, optics, illumination, clocks, mounts, compute, memory, interconnect, thermal limits, middleware, trackers, maps, planning, UI, and human escalation form one system. A faster neural network may not reduce end-to-end latency if decode, transfer, synchronization, or postprocessing dominates. Evaluation reports task quality, calibration, subgroup and condition slices, robustness, tail latency, throughput, memory, power, model size, preprocessing and postprocessing cost, and uncertainty across runs. Leakage-resistant splits separate locations, subjects, devices, and time where needed; confidence intervals and error taxonomies expose whether a headline score represents deployable behavior.

Implementation, hardware, and failure modes. Camera calibration and rectification, cost volumes, multiscale encoders, ordinal or distributional losses, self-supervised reprojection, sparse-depth completion, confidence heads, filtering, hole filling, quantization, and tile overlap affect quality. Stereo cost volumes and high-resolution decoders consume memory; ToF needs timing and modulation electronics; lidar adds optics and scanning; monocular models favor tensor compute. ISP integration, DMA, synchronized cameras, and point-cloud conversion influence end-to-end cost. Textureless and repeated patterns break stereo, reflective or transparent materials disturb active sensors, sunlight interferes with infrared, monocular scale drifts, thin structures bleed, dynamic objects violate reprojection, and camera/temperature changes shift calibration. Engineering must include data movement, finite precision, resource contention, numerical or physical limits, error propagation, and deterministic behavior when assumptions are violated. The pipeline includes sensing, synchronization, calibration, ingestion, annotation, augmentation, training, evaluation, compilation, quantization, serving, monitoring, feedback, rollback, and dataset/model retirement. Raw data, labels, ontology versions, transforms, checkpoints, compiler artifacts, thresholds, and hardware profiles are traceable so a field failure can be reproduced.

Evaluation, verification, and deployment. Use indoor/outdoor, near/far, low texture, reflective, transparent, weather, night, dynamic, thin-object, and cross-camera sets; report valid-pixel masks and scale alignment; compare raw and filtered output; evaluate downstream stopping or overlay error. Depth combines with RGB semantics, IMU, radar, lidar, odometry, maps, and planning. Timestamp offset, rolling shutter, baseline flex, extrinsic drift, and frame transforms can dominate geometric error. Depth cameras can reconstruct private spaces and people. Collection, retention, on-device processing, consent, cloud transfer, and safety of active illumination require review. Verification combines held-out and out-of-distribution sets, synthetic stress with real validation, adversarial and corruption tests, calibration analysis, edge-case replay, hardware-in-the-loop timing, long-duration soak, human review, and shadow or canary deployment. Failures feed collection and labeling rather than being hidden by aggregate averages. The pipeline includes sensing, synchronization, calibration, ingestion, annotation, augmentation, training, evaluation, compilation, quantization, serving, monitoring, feedback, rollback, and dataset/model retirement. Raw data, labels, ontology versions, transforms, checkpoints, compiler artifacts, thresholds, and hardware profiles are traceable so a field failure can be reproduced. Evaluation reports task quality, calibration, subgroup and condition slices, robustness, tail latency, throughput, memory, power, model size, preprocessing and postprocessing cost, and uncertainty across runs. Leakage-resistant splits separate locations, subjects, devices, and time where needed; confidence intervals and error taxonomies expose whether a headline score represents deployable behavior.

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Selection and practical application. Stereo offers passive metric geometry with baseline, monocular offers low sensor cost and broad density, ToF/structured light offer active short-range depth, and lidar offers accurate sparse-to-dense 3D at higher cost. Mobile AR, robot navigation, driver assistance, warehouse picking, drones, construction measurement, human interaction, and image effects use complementary depth methods. Cameras, lidar, radar, IMUs, optics, illumination, clocks, mounts, compute, memory, interconnect, thermal limits, middleware, trackers, maps, planning, UI, and human escalation form one system. A faster neural network may not reduce end-to-end latency if decode, transfer, synchronization, or postprocessing dominates. A production perception claim specifies the sensor, scene distribution, label ontology, spatial and temporal resolution, operating range, latency deadline, target hardware, confidence policy, and consequence of a miss or false alarm. Dataset accuracy alone is insufficient when lighting, weather, motion, occlusion, calibration, geography, demographics, and sensor aging differ from the benchmark. CFS connects this topic to semiconductor architecture, implementation, verification, manufacturing, packaging, test, and deployed AI-system tradeoffs across the platform.