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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: 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: Facial Temperature Measurement with IR images
      • 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
  • 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
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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

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Must Read Papers

Selected Papers and blogs for Perception Deep Learning

Must read papers on CNN

A brief note on these selected papers:

Popular Backbone for CNN

  • Alexnet: review on alexnet

  • VGGNet: review on VGGNet

  • ResNet 2015

    • An Overview of ResNet and its Variants

    • Understanding and visualizing ResNets

  • Inception: simple guide to Inception

    • v1 2014: GoogLeNet

    • v3: 2015, review on v3

    • v4: paper, review on v4

  • Feature Pyramid Network: review on FPN

Object Detection

  1. Two-Stage Detector

  2. R-CNN: reading on R-CNN and variants

    • R-CNN 2013 , review on RCNN

    • Fast R-CNN: 2015

    • Faster R-CNN: 2015

2.One-Stage Detector

  • YOLO

    • 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

    • v4: reading

    • v5: reading

  • SSD

  • RetinaNet 2017: review on retinanet

  • EfficientDet: 2019

  • EfficientNet: 2019

  • MobileNets

  • Squeezenet

  • ShuffleNet

Review on Object Detection

  • Object Detection in 20 Years: A Survey

Advances in 2D Computer Vision

Review: 3D object detection using LiDAR

  • ****The state of 3D object detection: A review of the state of the art based upon the KITTI leaderboard****

3D Object Orientation Detection

Pseudo LiDAR: from 2D to 3D

Time Series Classification

  • Deep Learning for Time series classification: a review

  • InceptionTime: https://towardsdatascience.com/deep-learning-for-time-series-classification-inceptiontime-245703f422db

PreviousUseful sitesNextAlexNet

Last updated 1 month ago

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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
Figure from PointPillars
LogoRecent Advances in Modern Computer VisionMedium
LogoMaking a Pseudo LiDAR With Cameras and Deep LearningMedium
https://towardsdatascience.com/orientation-estimation-in-monocular-3d-object-detection-f850ace91411towardsdatascience.com
LogoMultimodal Regression — Beyond L1 and L2 LossMedium