đź“š
DLIP
  • Introduction
  • Prerequisite
  • Image Processing Basics
    • Notes
      • Thresholding
      • Spatial Filtering
      • Masking with Bitwise Operation
      • Model n Calibration
    • Tutorial
      • Tutorial: Install OpenCV C++
      • Tutorial: Create OpenCV Project
      • Tutorial: C++ basics
      • Tutorial: OpenCV Basics
      • Tutorial: Image Watch for Debugging
      • Tutorial: Spatial Filter
      • Tutorial: Thresholding and Morphology
      • Tutorial: Camera Calibration
      • Tutorial: Color Image Processing
      • Tutorial: Edge Line Circle Detection
      • Tutorial: Corner Detection and Optical Flow
      • Tutorial: OpenCV C++ Cheatsheet
      • Tutorial: Installation for Py OpenCV
      • Tutorial: OpenCv (Python) Basics
    • LAB
      • Lab Report Template
      • Lab Report Grading Criteria
      • LAB Report Instruction
      • LAB: Grayscale Image Segmentation
        • LAB: Grayscale Image Segmentation -Gear
        • LAB: Grayscale Image Segmentation - Bolt and Nut
      • LAB: Color Image Segmentation
        • LAB: Facial Temperature Measurement with IR images
        • LAB: Magic Cloak
      • LAB: Straight Lane Detection and Departure Warning
      • LAB: Dimension Measurement with 2D camera
      • LAB: Tension Detection of Rolling Metal Sheet
  • Deep Learning for Perception
    • Notes
      • Lane Detection with Deep Learning
      • Overview of Deep Learning
        • Object Detection
        • Deep Learning Basics: Introduction
        • Deep Learning State of the Art
        • CNN, Object Detection
      • Perceptron
      • Activation Function
      • Optimization
      • Convolution
      • CNN Overview
      • Evaluation Metric
      • LossFunction Regularization
      • Bias vs Variance
      • BottleNeck Unit
      • Object Detection
      • DL Techniques
        • Technical Strategy by A.Ng
    • Tutorial - PyTorch
      • Tutorial: Install PyTorch
      • Tutorial: Python Numpy
      • Tutorial: PyTorch Tutorial List
      • Tutorial: PyTorch Example Code
      • Tutorial: Tensorboard in Pytorch
      • Tutorial: YOLO in PyTorch
        • Tutorial: Yolov8 in PyTorch
        • Tutorial: Train Yolo v8 with custom dataset
          • Tutorial: Train Yolo v5 with custom dataset
        • Tutorial: Yolov5 in Pytorch (VS code)
        • Tutorial: Yolov3 in Keras
    • LAB
      • Assignment: CNN Classification
      • Assignment: Object Detection
      • LAB: CNN Object Detection 1
      • LAB: CNN Object Detection 2
      • LAB Grading Criteria
    • Tutorial- Keras
      • Train Dataset
      • Train custom dataset
      • Test model
      • LeNet-5 Tutorial
      • AlexNet Tutorial
      • VGG Tutorial
      • ResNet Tutorial
    • Resource
      • Online Lecture
      • Programming tutorial
      • Books
      • Hardware
      • Dataset
      • Useful sites
  • Must Read Papers
    • AlexNet
    • VGG
    • ResNet
    • R-CNN, Fast-RCNN, Faster-RCNN
    • YOLOv1-3
    • Inception
    • MobileNet
    • SSD
    • ShuffleNet
    • Recent Methods
  • DLIP Project
    • Report Template
    • DLIP 2021 Projects
      • Digital Door Lock Control with Face Recognition
      • People Counting with YOLOv4 and DeepSORT
      • Eye Blinking Detection Alarm
      • Helmet-Detection Using YOLO-V5
      • Mask Detection using YOLOv5
      • Parking Space Management
      • Vehicle, Pedestrian Detection with IR Image
      • Drum Playing Detection
      • Turtle neck measurement program using OpenPose
    • DLIP 2022 Projects
      • BakeryCashier
      • Virtual Mouse
      • Sudoku Program with Hand gesture
      • Exercise Posture Assistance System
      • People Counting Embedded System
      • Turtle neck measurement program using OpenPose
    • DLIP Past Projects
  • Installation Guide
    • Installation Guide for Pytorch
      • Installation Guide 2021
    • Anaconda
    • CUDA cuDNN
      • CUDA 10.2
    • OpenCV
      • OpenCV Install and Setup
        • OpenCV 3.4.13 with VS2019
        • OpenCV3.4.7 VS2017
        • MacOS OpenCV C++ in XCode
      • Python OpenCV
      • MATLAB-OpenCV
    • Framework
      • Keras
      • TensorFlow
        • Cheat Sheet
        • Tutorial
      • PyTorch
    • IDE
      • Visual Studio Community
      • Google Codelab
      • Visual Studio Code
        • Python with VS Code
        • Notebook with VS Code
        • C++ with VS Code
      • Jupyter Notebook
        • Install
        • How to use
    • Ubuntu
      • Ubuntu 18.04 Installation
      • Ubuntu Installation using Docker in Win10
      • Ubuntu Troubleshooting
    • ROS
  • Programming
    • Python_Numpy
      • Python Tutorial - Tips
      • Python Tutorial - For Loop
      • Python Tutorial - List Tuple, Dic, Set
    • Markdown
      • Example: API documentation
    • Github
      • Create account
      • Tutorial: Github basic
      • Tutorial: Github Desktop
    • Keras
      • Tutorial Keras
      • Cheat Sheet
    • PyTorch
      • Cheat Sheet
      • Autograd in PyTorch
      • Simple ConvNet
      • MNIST using LeNet
      • Train ConvNet using CIFAR10
  • Resources
    • Useful Resources
    • Github
Powered by GitBook
On this page
  • Must read papers on CNN
  • Popular Backbone for CNN
  • Object Detection
  • Review on Object Detection
  • Advances in 2D Computer Vision
  • Review: 3D object detection using LiDAR
  • 3D Object Orientation Detection
  • Pseudo LiDAR: from 2D to 3D
  • Time Series Classification

Was this helpful?

Must Read Papers

Selected Papers and blogs for Perception Deep Learning

PreviousUseful sitesNextAlexNet

Last updated 3 months ago

Was this helpful?

Must read papers on CNN

Popular Backbone for CNN

  • :

  • :

  • 2015

  • Inception:

    • v1 2014: GoogLeNet

    • review on v4

  • Feature Pyramid Network:

Object Detection

  1. Two-Stage Detector

  2. R-CNN:

    • ,

2.One-Stage Detector

  • YOLO

    • v4: reading

    • v5: reading

  • MobileNets

  • Squeezenet

  • ShuffleNet

Review on Object Detection

Advances in 2D Computer Vision

Review: 3D object detection using LiDAR

3D Object Orientation Detection

Pseudo LiDAR: from 2D to 3D

Time Series Classification

, ,

:

, ,

,

2017:

Image from zou2019

********

Detector (LIDAR only) latency vs vehicle AP
Image General approaches for LIDAR+RGB fusion. Images are adapted from MV3D (Chen et. at. 2016), F-Pointnet (Qi et. al. 2017), ContFuse (Liang et. al. 2018), and LaserNet (Meyer et. al. 2018).for post
Detector (LIDAR+RGB fusion labeled) latency vs vehicle APost
Trade-offs between RV and BEV projectionspost

InceptionTime:

A brief note on these selected papers:
Alexnet
review on alexnet
VGGNet
review on VGGNet
ResNet
An Overview of ResNet and its Variants
Understanding and visualizing ResNets
simple guide to Inception
v3: 2015,
review on v3
v4: paper,
review on FPN
reading on R-CNN and variants
R-CNN 2013
review on RCNN
Fast R-CNN: 2015
Faster R-CNN: 2015
v1 2016:
v2
YOLO explained
YOLO in python
YOLO simpler explanation
YOLOv1 CVPR2016 presentation
Official slides, resource for YOLO
YOLOv2 simple explain
v3 2018
beginner guide
theory explained
whats new inYOLOv3
YOLOv3 in Keras
YOLOv3 in PyTorch
SSD
RetinaNet
review on retinanet
EfficientDet: 2019
EfficientNet: 2019
Object Detection in 20 Years: A Survey
The state of 3D object detection: A review of the state of the art based upon the KITTI leaderboard
Deep Learning for Time series classification: a review
https://towardsdatascience.com/deep-learning-for-time-series-classification-inceptiontime-245703f422db
Recent Advances in Modern Computer VisionMedium
Multimodal Regression — Beyond L1 and L2 LossMedium
https://towardsdatascience.com/orientation-estimation-in-monocular-3d-object-detection-f850ace91411towardsdatascience.com
Making a Pseudo LiDAR With Cameras and Deep LearningMedium
Logo
Logo
Logo
Figure from PointPillars