What is Depth Estimation?

What is Depth Estimation?

Depth Estimation is a computer vision capability that calculates how far away objects are from a camera, effectively reconstructing three-dimensional spatial information from flat images or video. While human eyes naturally perceive depth through binocular vision and visual cues, AI systems use learned patterns and mathematical techniques to estimate distance from standard camera footage.

This capability bridges the gap between what a camera captures — a flat, two-dimensional image — and the three-dimensional world it depicts. By understanding depth, AI systems can reason about the physical layout of spaces, the size of objects, and the navigable paths through environments.

How Depth Estimation Works

There are several approaches to depth estimation, each with different trade-offs:

Monocular Depth Estimation

This approach estimates depth from a single camera image using deep learning models trained on large datasets of images with known depth values. The model learns to use visual cues such as:

• Perspective and vanishing points — parallel lines converging in the distance
• Object size — known objects appearing smaller when further away
• Texture gradients — surface patterns becoming finer with distance
• Atmospheric effects — distant objects appearing hazier
• Occlusion patterns — nearer objects blocking those behind them

Models like MiDaS, DPT (Dense Prediction Transformer), and Depth Anything have achieved remarkable accuracy at estimating depth from single images. These models are particularly valuable because they work with any standard camera.

Stereo Depth Estimation

Using two cameras positioned like human eyes, stereo systems calculate depth by measuring the disparity between corresponding points in the left and right images. Objects closer to the cameras have greater disparity. This approach produces more accurate depth maps than monocular methods but requires calibrated dual-camera setups.

Structured Light and Time-of-Flight

These active methods project patterns (structured light) or pulses (time-of-flight) onto the scene and measure how they return. While highly accurate, they require specialised hardware and are typically limited in range. Apple's Face ID and Microsoft's Kinect use these approaches.

Multi-View Depth Estimation

By capturing images from multiple viewpoints — either through camera movement or multiple fixed cameras — algorithms reconstruct three-dimensional geometry through triangulation. This is the principle behind photogrammetry and visual SLAM (Simultaneous Localisation and Mapping).

Business Applications

Robotics and Warehouse Automation

Autonomous robots in warehouses and manufacturing facilities use depth estimation to navigate safely, avoid obstacles, and manipulate objects. In Southeast Asia's rapidly growing e-commerce logistics sector, depth-aware robots can sort packages, navigate warehouse aisles, and load delivery vehicles with increasing independence.

Augmented Reality

AR applications overlay digital content onto the physical world, which requires accurate depth understanding to place virtual objects correctly. Retail applications let customers visualise furniture in their homes, while industrial AR guides workers through maintenance procedures with spatially-aware instructions.

Autonomous Vehicles

Self-driving vehicles combine depth estimation from cameras with data from LiDAR and radar to build a complete understanding of the surrounding environment. Camera-based depth estimation provides a cost-effective complement to expensive LiDAR sensors.

Construction and Real Estate

Depth estimation enables the creation of three-dimensional models of buildings and spaces from camera footage. This supports construction progress monitoring, property virtual tours, and spatial planning. In Southeast Asia's booming construction sector, this reduces the need for expensive laser scanning equipment.

Agriculture

Drone-mounted cameras combined with depth estimation create three-dimensional crop canopy models, enabling precise measurement of plant height, volume, and growth patterns. This supports precision agriculture practices across Southeast Asian plantations.

Retail Space Planning

Understanding the three-dimensional layout of retail spaces helps optimise product placement, aisle configuration, and display positioning. Depth-aware analytics provide insights into how customers navigate three-dimensional retail environments.

Depth Estimation in Southeast Asia

The technology is particularly relevant for the region:

• Logistics and warehousing operations supporting the region's e-commerce growth benefit from depth-aware robotics and automation
• Construction monitoring across rapid urban development projects in cities like Ho Chi Minh City, Jakarta, and Manila uses depth estimation for progress tracking
• Agriculture across palm oil, rubber, and rice farming regions uses drone-based depth estimation for precision farming
• Smart building management in Singapore and other developed markets uses depth-aware sensors for occupancy analysis and energy optimisation

Technical Considerations

Accuracy and Limitations

Monocular depth estimation provides relative depth ordering (near versus far) more reliably than absolute distance measurements. For applications requiring precise measurements, stereo or active sensor approaches are preferable. However, the accuracy of monocular methods continues to improve rapidly with advances in deep learning.

Computational Requirements

Modern depth estimation models can run on:

• Mobile devices — lightweight models like MiDaS Small process images in milliseconds on smartphones
• Edge devices — NVIDIA Jetson and similar platforms support real-time depth estimation for robotics and embedded applications
• Cloud infrastructure — for batch processing of large image collections

Integration Opportunities

Depth estimation becomes more valuable when combined with:

• Object detection — knowing both what objects are and how far away they are
• Semantic segmentation — understanding the type and distance of every surface in a scene
• Pose estimation — three-dimensional body position understanding for more accurate activity analysis

Getting Started

1. Evaluate whether monocular or stereo depth is needed — monocular works with existing cameras, stereo requires hardware investment but provides higher accuracy
2. Start with pre-trained models like MiDaS or Depth Anything, which work across many environments without custom training
3. Define accuracy requirements — relative depth ordering is easier than precise distance measurement
4. Consider edge deployment for real-time applications, especially in robotics and navigation
5. Plan for environmental variability — test across different lighting conditions, distances, and scene types