Phase 2: Object Detection & Segmentation · ~45 minutes · Python

3D Vision — Point Clouds & NeRFs

3D vision comes in two flavours. Point clouds are the sensor's raw output. NeRFs are the learned volumetric field. Both answer "what is where in space."

What you will learn

Distinguish explicit (point cloud, mesh, voxel) and implicit (signed distance field, NeRF) 3D representations and when each is used
Understand PointNet's symmetric-function trick that makes a neural network permutation-invariant over an unordered set of points
Trace a NeRF forward pass: ray casting, volumetric rendering, positional encoding, MLP density+colour head
Use `nerfstudio` or `instant-ngp` for pretrained 3D reconstruction from a small set of posed images

Introduction

Type: Learn + Build
Languages: Python
Prerequisites: the Math for AI & ML course, Tensor Operations
Time: ~45 minutes

Objective

Learning objectives

Distinguish explicit (point cloud, mesh, voxel) and implicit (signed distance field, NeRF) 3D representations and when each is used
Understand PointNet's symmetric-function trick that makes a neural network permutation-invariant over an unordered set of points
Trace a NeRF forward pass: ray casting, volumetric rendering, positional encoding, MLP density+colour head
Use nerfstudio or instant-ngp for pretrained 3D reconstruction from a small set of posed images

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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