Delving into Localization Errors for Monocular 3D Object Detection, CVPR'2021

Related tags

Deep Learningmonodle
Overview

Delving into Localization Errors for Monocular 3D Detection

By Xinzhu Ma, Yinmin Zhang, Dan Xu, Dongzhan Zhou, Shuai Yi, Haojie Li, Wanli Ouyang.

Introduction

This repository is an official implementation of the paper 'Delving into Localization Errors for Monocular 3D Detection'. In this work, by intensive diagnosis experiments, we quantify the impact introduced by each sub-task and found the ‘localization error’ is the vital factor in restricting monocular 3D detection. Besides, we also investigate the underlying reasons behind localization errors, analyze the issues they might bring, and propose three strategies.

vis

Usage

Installation

This repo is tested on our local environment (python=3.6, cuda=9.0, pytorch=1.1), and we recommend you to use anaconda to create a vitural environment:

conda create -n monodle python=3.6

Then, activate the environment:

conda activate monodle

Install Install PyTorch:

conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=9.0 -c pytorch

and other requirements:

pip install -r requirements.txt

Data Preparation

Please download KITTI dataset and organize the data as follows:

#ROOT
  |data/
    |KITTI/
      |ImageSets/ [already provided in this repo]
      |object/			
        |training/
          |calib/
          |image_2/
          |label/
        |testing/
          |calib/
          |image_2/

Training & Evaluation

Move to the workplace and train the network:

 cd #ROOT
 cd experiments/example
 python ../../tools/train_val.py --config config_patchnet.yaml

The model will be evaluated automatically if the training completed. If you only want evaluate your trained model (or the provided pretrained model) , you can modify the test part configuration in the .yaml file and use the following command:

python ../../tools/train_val.py --config config_patchnet.yaml --e

For ease of use, we also provide a pre-trained checkpoint, which can be used for evaluation directly. See the below table to check the performance.

[email protected] [email protected]. [email protected]
In original paper 17.45 13.66 11.68
In this repo 17.94 13.72 12.10

Citation

If you find our work useful in your research, please consider citing:

@InProceedings{Ma_2021_CVPR,
author = {Ma, Xinzhu and Zhang, Yinmin, and Xu, Dan and Zhou, Dongzhan and Yi, Shuai and Li, Haojie and Ouyang, Wanli},
title = {Delving into Localization Errors for Monocular 3D Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}}

Acknowlegment

This repo benefits from the excellent work CenterNet. Please also consider citing it.

License

This project is released under the MIT License.

Contact

If you have any question about this project, please feel free to contact [email protected].

Owner
XINZHU.MA
PhD student at the University of Sydney.
XINZHU.MA
A python software that can help blind people find things like laptops, phones, etc the same way a guide dog guides a blind person in finding his way.

GuidEye A python software that can help blind people find things like laptops, phones, etc the same way a guide dog guides a blind person in finding h

Munal Jain 0 Aug 09, 2022
Air Pollution Prediction System using Linear Regression and ANN

AirPollution Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living Publication Link:

Dr Sharnil Pandya, Associate Professor, Symbiosis International University 19 Feb 07, 2022
LiDAR R-CNN: An Efficient and Universal 3D Object Detector

LiDAR R-CNN: An Efficient and Universal 3D Object Detector Introduction This is the official code of LiDAR R-CNN: An Efficient and Universal 3D Object

TuSimple 295 Jan 05, 2023
Data augmentation for NLP, accepted at EMNLP 2021 Findings

AEDA: An Easier Data Augmentation Technique for Text Classification This is the code for the EMNLP 2021 paper AEDA: An Easier Data Augmentation Techni

Akbar Karimi 81 Dec 09, 2022
Simple embedding based text classifier inspired by fastText, implemented in tensorflow

FastText in Tensorflow This project is based on the ideas in Facebook's FastText but implemented in Tensorflow. However, it is not an exact replica of

Alan Patterson 306 Dec 02, 2022
Code and model benchmarks for "SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology"

NeurIPS 2020 SEVIR Code for paper: SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology Requirement

USAF - MIT Artificial Intelligence Accelerator 46 Dec 15, 2022
UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation

UnivNet UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation. Training python train.py --c

Rishikesh (ऋषिकेश) 55 Dec 26, 2022
TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios

TPH-YOLOv5 This repo is the implementation of "TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured

cv516Buaa 439 Dec 22, 2022
torchbearer: A model fitting library for PyTorch

Note: We're moving to PyTorch Lightning! Read about the move here. From the end of February, torchbearer will no longer be actively maintained. We'll

632 Dec 13, 2022
ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection

ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection This repository contains implementation of the

Visual Understanding Lab @ Samsung AI Center Moscow 190 Dec 30, 2022
A program that can analyze videos according to the weights you select

MaskMonitor A program that can analyze videos according to the weights you select 下載 訓練完的 weight檔案 執行 MaskDetection.py 內部可更改 輸入來源(鏡頭, 影片, 圖片) 以及輸出條件(人

Patrick_star 1 Nov 07, 2021
CC-GENERATOR - A python script for generating CC

CC-GENERATOR A python script for generating CC NOTE: This tool is for Educationa

Lêkzï 6 Oct 14, 2022
Object detection (YOLO) with pytorch, OpenCV and python

Real Time Object/Face Detection Using YOLO-v3 This project implements a real time object and face detection using YOLO algorithm. You only look once,

1 Aug 04, 2022
This repository contains the code for using the H3DS dataset introduced in H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction

H3DS Dataset This repository contains the code for using the H3DS dataset introduced in H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction Access

Crisalix 72 Dec 10, 2022
Myia prototyping

Myia Myia is a new differentiable programming language. It aims to support large scale high performance computations (e.g. linear algebra) and their g

Mila 456 Nov 07, 2022
Implementation of Bidirectional Recurrent Independent Mechanisms (Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules)

BRIMs Bidirectional Recurrent Independent Mechanisms Implementation of the paper Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neura

Sarthak Mittal 26 May 26, 2022
Official PyTorch implementation of "Improving Face Recognition with Large AgeGaps by Learning to Distinguish Children" (BMVC 2021)

Inter-Prototype (BMVC 2021): Official Project Webpage This repository provides the official PyTorch implementation of the following paper: Improving F

Jungsoo Lee 16 Jun 30, 2022
Generative Query Network (GQN) in PyTorch as described in "Neural Scene Representation and Rendering"

Update 2019/06/24: A model trained on 10% of the Shepard-Metzler dataset has been added, the following notebook explains the main features of this mod

Jesper Wohlert 313 Dec 27, 2022
Secure Distributed Training at Scale

Secure Distributed Training at Scale This repository contains the implementation of experiments from the paper "Secure Distributed Training at Scale"

Yandex Research 9 Jul 11, 2022
Learned Token Pruning for Transformers

LTP: Learned Token Pruning for Transformers Check our paper for more details. Installation We follow the same installation procedure as the original H

Sehoon Kim 52 Dec 29, 2022