PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (PAPER EXPLAINED)

GitHub: GitHub: https://github.com/aldipiroli/pointnet Blog: https://minimal-debug.github.io/paper... Arxiv: https://arxiv.org/abs/1612.00593 --- Abstract: Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds and well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Though simple, PointNet is highly efficient and effective. Empirically, it shows strong performance on par or even better than state of the art. Theoretically, we provide analysis towards understanding of what the network has learnt and why the network is robust with respect to input perturbation and corruption.

Join Today
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object (PAPER EXPLAINED)
▶︎

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object (PAPER EXPLAINED)

PointNet | Lecture 43 (Part 1) | Applied Deep Learning
▶︎

PointNet | Lecture 43 (Part 1) | Applied Deep Learning

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
▶︎

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Transformers, the tech behind LLMs | Deep Learning Chapter 5
▶︎

Transformers, the tech behind LLMs | Deep Learning Chapter 5

[SGP-2022] Deep Learning on Point Clouds
▶︎

[SGP-2022] Deep Learning on Point Clouds

2. How PointNet works as the pioneer of 3D point cloud backbone
▶︎

2. How PointNet works as the pioneer of 3D point cloud backbone

Iterative Closest Point (ICP) - Computerphile
▶︎

Iterative Closest Point (ICP) - Computerphile

Current Approaches and Future Directions for Point Cloud Object Detection in Intelligent Agents
▶︎

Current Approaches and Future Directions for Point Cloud Object Detection in Intelligent Agents

The Man Who Worked At Subway, Then Solved An "Impossible" Problem
▶︎

The Man Who Worked At Subway, Then Solved An "Impossible" Problem

Something is jamming GPS over Europe. Here's what we found
▶︎

Something is jamming GPS over Europe. Here's what we found

Archaeology Warning: They May Have Secretly Found Antarctica 300 Years Before Us! - Graham Hancock
▶︎

Archaeology Warning: They May Have Secretly Found Antarctica 300 Years Before Us! - Graham Hancock

3. How PointNet++ works on improving 3D point cloud backbone
▶︎

3. How PointNet++ works on improving 3D point cloud backbone

Point Net - An intuitive Introduction
▶︎

Point Net - An intuitive Introduction

The moment we stopped understanding AI [AlexNet]
▶︎

The moment we stopped understanding AI [AlexNet]

Deep Learning for “Exotic” Data Like 3D Meshes and Point-Clouds
▶︎

Deep Learning for “Exotic” Data Like 3D Meshes and Point-Clouds

But what is quantum computing? (Grover's Algorithm)
▶︎

But what is quantum computing? (Grover's Algorithm)

SampleNet: Learning a Differentiable Point Cloud Sampling Network
▶︎

SampleNet: Learning a Differentiable Point Cloud Sampling Network

Deep learning for 3D point clouds by Dr Min Wang - UNSW.ai Workshop
▶︎

Deep learning for 3D point clouds by Dr Min Wang - UNSW.ai Workshop

How might LLMs store facts | Deep Learning Chapter 7
▶︎

How might LLMs store facts | Deep Learning Chapter 7