Depth estimation from single image is the task of predicting per-pixel depth from a single RGB image — inferring 3D scene geometry from 2D appearance using learned priors about object sizes, perspective, occlusions, and scene layout, enabling 3D understanding without stereo cameras or depth sensors.

What Is Single-Image Depth Estimation?

• Definition: Predict depth map from single RGB image.
• Input: Single RGB image.
• Output: Depth map (distance to camera for each pixel).
• Challenge: Ill-posed problem — infinite 3D scenes project to same 2D image.
• Solution: Learn priors from data to resolve ambiguity.

Why Single-Image Depth?

• Accessibility: Works with any camera, no special hardware.
• Convenience: No stereo calibration, no multiple views needed.
• Ubiquity: Enable depth understanding on billions of existing images.
• Applications: AR, robotics, autonomous vehicles, photography.

Depth Estimation Approaches

Geometric Cues:

• Perspective: Parallel lines converge at vanishing points.
• Occlusion: Closer objects occlude farther objects.
• Relative Size: Known object sizes provide scale.
• Texture Gradient: Texture density increases with distance.

Learning-Based:

• Supervised: Train on images with ground truth depth.
• Self-Supervised: Train on stereo pairs or video sequences.
• Transfer Learning: Pre-train on large datasets, fine-tune.

Depth Estimation Methods

Supervised Learning:

• Training Data: RGB images + ground truth depth (from lidar, depth sensors).
• Network: CNN or Transformer encoder-decoder.
• Loss: L1, L2, or scale-invariant loss.
• Examples: MiDaS, DPT, AdaBins.

Self-Supervised Learning:

• Training Data: Stereo pairs or monocular video.
• Supervision: Photometric consistency.
• Process:

1. Predict depth from left image. 2. Warp right image using predicted depth. 3. Minimize difference between left and warped right.

• Examples: Monodepth, Monodepth2, PackNet.

Depth Estimation Architectures

Encoder-Decoder:

• Encoder: Extract features (ResNet, EfficientNet, ViT).
• Decoder: Upsample to full resolution depth map.
• Skip Connections: Preserve fine details.

Transformer-Based:

• DPT (Dense Prediction Transformer): Vision Transformer for depth.
• Benefit: Better global context, long-range dependencies.

Multi-Scale:

• Predict: Depth at multiple scales.
• Benefit: Capture both coarse structure and fine details.

Applications

Augmented Reality:

• Occlusion: Render AR objects behind real objects.
• Placement: Place virtual objects on real surfaces.
• Interaction: Enable realistic AR interactions.

Autonomous Vehicles:

• Obstacle Detection: Identify obstacles and their distances.
• Path Planning: Plan safe paths using depth information.
• Backup: Complement lidar with camera-based depth.

Robotics:

• Navigation: Avoid obstacles using depth.
• Manipulation: Understand object geometry for grasping.
• Mapping: Build 3D maps from monocular cameras.

Photography:

• Bokeh: Simulate depth-of-field effects.
• Refocusing: Change focus after capture.
• 3D Photos: Create 3D effects from 2D images.

Accessibility:

• Navigation Assistance: Help visually impaired navigate.
• Scene Description: Describe spatial layout of scenes.

Challenges

Scale Ambiguity:

• Problem: Monocular depth has unknown scale.
• Solution: Predict relative depth, or use known object sizes.

Textureless Regions:

• Problem: Smooth surfaces lack features.
• Solution: Learn priors, use global context.

Occlusions:

• Problem: Can't see behind objects.
• Solution: Infer from context, learned priors.

Generalization:

• Problem: Models trained on specific data may not generalize.
• Solution: Train on diverse datasets, domain adaptation.

Depth Estimation Datasets

Indoor:

• NYU Depth V2: Indoor scenes with Kinect depth.
• ScanNet: RGB-D scans of indoor environments.

Outdoor:

• KITTI: Autonomous driving with lidar depth.
• Cityscapes: Urban street scenes.

Mixed:

• MegaDepth: Internet photos with SfM depth.
• Taskonomy: Diverse indoor scenes.

Quality Metrics

Absolute Metrics:

• RMSE: Root mean squared error.
• MAE: Mean absolute error.
• Abs Rel: Mean absolute relative error.

Relative Metrics:

• δ < 1.25: Percentage of pixels with relative error < 25%.
• δ < 1.25²: Within 56% relative error.
• δ < 1.25³: Within 95% relative error.

Scale-Invariant:

• SILog: Scale-invariant logarithmic error.
• Benefit: Robust to scale ambiguity.

Depth Estimation Models

MiDaS:

• Training: Mixed datasets (multiple sources).
• Benefit: Generalizes well to diverse scenes.
• Output: Relative depth (scale ambiguous).

DPT (Dense Prediction Transformer):

• Architecture: Vision Transformer encoder + convolutional decoder.
• Benefit: State-of-the-art accuracy, good generalization.

AdaBins:

• Innovation: Adaptive bins for depth prediction.
• Benefit: Better handling of depth range.

Monodepth2:

• Training: Self-supervised on monocular video.
• Benefit: No ground truth depth needed.

Depth Estimation Techniques

Multi-Task Learning:

• Method: Train depth jointly with other tasks (segmentation, normals).
• Benefit: Shared representations improve all tasks.

Domain Adaptation:

• Method: Adapt model trained on synthetic data to real data.
• Benefit: Leverage large synthetic datasets.

Test-Time Optimization:

• Method: Fine-tune on test image using self-supervision.
• Benefit: Improve accuracy on specific image.

Future of Single-Image Depth

• Zero-Shot: Generalize to any scene without training.
• Metric Depth: Predict absolute depth, not just relative.
• Real-Time: Fast depth estimation for mobile devices.
• Video: Temporally consistent depth for video.
• Semantic: Integrate semantic understanding.
• Foundation Models: Large pre-trained models for depth.

Single-image depth estimation is a fundamental capability in computer vision — it enables 3D understanding from ordinary 2D images, making depth perception accessible without special hardware, supporting applications from augmented reality to robotics to photography.