The document discusses deep learning on point sets. Point sets have properties of being unordered, invariant to transformations, and having interactions among points. The PointNet architecture uses three key techniques: 1) a max pooling layer as a symmetric function to aggregate point information, 2) a structure to combine local and global information, and 3) two joint alignment networks to align input points and point features. The architecture provides permutation invariance and is robust to data corruption.

2. Introduction
Introduction

3. Introduction

4. Introduction

5. Related Work
Introduction

6. Introduction

7. Problem Statement
Introduction

8. Introduction

9. Deep learning on Point sets
Deep learning on Point Sets

10. Deep learning on Point Sets – Properties of pointsets
Our input is a subset of points from an Euclidean space
3 main properties • Unordered → Permutation invariance
• Invariance under transformations
• Interaction among points
2D array representation
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11. Deep learning on Point Sets – PointNet Architecture

12. 1. the max pooling layer as a symmetric function to aggregate information from
all the points
2. a local and global information combination structure
3. two joint alignment networks that align both input points and point features.
3 key modules
Deep learning on Point Sets – PointNet Architecture

13. 1. the max pooling layer as a symmetric function to aggregate information from
all the points
Unordered input
RNN training
Symmetric function
Deep learning on Point Sets – PointNet Architecture
Permutation
invariance

14. 1. the max pooling layer as a symmetric function to aggregate information from
all the points
Deep learning on Point Sets – PointNet Architecture

15. Deep learning on Point Sets – PointNet Architecture
1. the max pooling layer as a symmetric function to aggregate information from
all the points – Q: What symmetric functions can be constructed by PointNet?
시멘코 정리?

16. Deep learning on Point Sets – PointNet Architecture

17. 2. a local and global information combination structure
Deep learning on Point Sets – PointNet Architecture

18. 3. two joint alignment networks that align both input points and point features.
Deep learning on Point Sets – PointNet Architecture

19. 3. two joint alignment networks that align both input points and point features.
Deep learning on Point Sets – PointNet Architecture

20. 3. two joint alignment networks that align both input points and point features.
Deep learning on Point Sets – PointNet Architecture
STN

21. Result
Deep learning on Point Sets

22. Deep learning on Point Sets

23. Deep learning on Point Sets
Robustness to data corruption

24. Deep learning on Point Sets
Visualizing PointNet