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."

Hiring signal: Understanding of 3d vision — point clouds & nerfs internals

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: Phase 4 Lesson 03 (CNNs), Phase 1 Lesson 12 (Tensor Operations) Time: ~45 minutes

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 It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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