Phase 5: CV Deployment & Applications · ~60 minutes · Python

Monocular Depth & Geometry Estimation

A depth map is a single-channel image where each pixel is a distance from the camera. Predicting it from one RGB frame used to be impossible without stereo or LiDAR. In 2026 a frozen ViT encoder plus a lightweight head gets within a few percent of ground truth.

Hiring signal: Understanding of monocular depth & geometry estimation internals

What you will learn

Distinguish relative and metric depth and state which one each production model (MiDaS, Marigold, Depth Anything V3, ZoeDepth) solves
Use Depth Anything V3 (DINOv2 backbone) to predict depth for arbitrary single images with no calibration
Explain why monocular depth works at all from a single image (perspective cues, texture gradients, learned priors) and what it cannot recover (absolute scale, occluded geometry)
Lift 2D detections to 3D points using a depth map and pinhole camera intrinsics

Introduction

Type: Build + Use Languages: Python Prerequisites: Phase 4 Lesson 14 (ViT), Phase 4 Lesson 17 (Self-Supervised Vision), Phase 4 Lesson 07 (U-Net) Time: ~60 minutes

Learning Objectives

Distinguish relative and metric depth and state which one each production model (MiDaS, Marigold, Depth Anything V3, ZoeDepth) solves
Use Depth Anything V3 (DINOv2 backbone) to predict depth for arbitrary single images with no calibration
Explain why monocular depth works at all from a single image (perspective cues, texture gradients, learned priors) and what it cannot recover (absolute scale, occluded geometry)
Lift 2D detections to 3D points using a depth map and pinhole camera intrinsics

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You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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