RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving

Related tags

Deep LearningRTS3D
Overview

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving (AAAI2021).

RTS3D is efficiency and accuracy stereo 3D object detection method for autonomous driving.

RTS3D

Introduction

RTS3D is the first true real-time system (FPS>24) for stereo image 3D detection meanwhile achieves 10% improvement in average precision comparing with the previous state-of-the-art method. RTS3D only require RGB images without synthetic data, instance segmentation, CAD model, or depth generator.

Highlights

  • Fast: 33 FPS of single image test speed in KITTI benchmark with 384*1280 resolution
  • Accuracy: SOTA on the KITTI benchmark.
  • Anchor Free: No 2D or 3D anchor are reauired
  • Easy to deploy: RTS3D uses conventional convolution operations and MLP, so it is very easy to deploy and accelerate.

RTS3D Baseline and Model Zoo

All experiments are tested with Ubuntu 16.04, Pytorch 1.0.0, CUDA 9.0, Python 3.6, single NVIDIA 2080Ti

IoU Setting 1: Car IoU > 0.5, Pedestrian IoU > 0.25, Cyclist IoU > 0.25

IoU Setting 2: Car IoU > 0.7, Pedestrian IoU > 0.5, Cyclist IoU > 0.5

  • Training on KITTI train split and evaluation on val split.
Class Iteration FPS AP BEV IoU Setting1 AP 3D IoU Setting1 AP BEV IoU Setting2 AP 3D IoU Setting2
- - - Easy / Moderate / Hard Easy / Moderate / Hard Easy / Moderate / Hard Easy / Moderate / Hard
Car- Recall-11 1 90.9 89.83, 77.05, 68.28 89.27, 70.12, 61.17 73.20, 53.62, 46.44 60.87, 42.38, 36.44
Car- Recall-40 1 90.9 92.92, 76.17, 66.62 90.35, 71.37, 63.52 78.12, 54.75, 47.09 60.34, 39.32, 32.97
Car- Recall-11 2 45.5 90.41, 78.70, 70.03 90.26, 77.23, 68.28 76.56, 56.46, 48.20 63.65, 44.50, 37.48
Car- Recall-40 2 45.5 95.75, 79.61, 69.69 93.57, 76.64, 66.72 78.12, 54.75, 47.09 63.99, 41.78, 34.96
  • Training on KITTI train split and evaluation on val split.
    • FCE Space Resolution: 10 * 10 * 10
    • Recall split: 11
    • Iteration: 2
    • Model: (Google Drive), (Baidu Cloud 提取码:4t4u)
Class AP BEV IoU Setting1 AP 3D IoU Setting1 AP BEV IoU Setting2 AP 3D IoU Setting2
- Easy / Moderate / Hard Easy / Moderate / Hard Easy / Moderate / Hard Easy / Moderate / Hard
Car 90.18, 78.46, 69.76 89.88, 76.64, 67.86 74.95, 54.07, 46.78 58.50, 39.74, 34.83
Pedestrian 57.12, 48.82, 40.88 56.36, 48.29, 40.22 32.16, 26.31, 21.28 26.95, 20.77, 19.74
Cyclist 54.48, 35.78, 30.80 53.86, 30.90, 30.52 33.59, 20.80, 20.14 31.05, 20.26, 18.93

Installation

Please refer to INSTALL.md

Dataset preparation

Please download the official KITTI 3D object detection dataset and organize the downloaded files as follows:

KM3DNet
├── kitti_format
│   ├── data
│   │   ├── kitti
│   │   |   ├── annotations
│   │   │   ├── calib /000000.txt .....
│   │   │   ├── image(left[0-7480] right[7481-14961] input augmentatiom)
│   │   │   ├── label /000000.txt .....
|   |   |   ├── train.txt val.txt trainval.txt
│   │   │   ├── mono_results /000000.txt .....
├── src
├── demo_kitti_format
├── readme
├── requirements.txt

Getting Started

Please refer to GETTING_STARTED.md to learn more usage about this project.

Acknowledgement

License

RTS3D is released under the MIT License (refer to the LICENSE file for details). Portions of the code are borrowed from, CenterNet, iou3d and kitti_eval (KITTI dataset evaluation). Please refer to the original License of these projects (See NOTICE).

Citation

If you find this project useful for your research, please use the following BibTeX entry.

@misc{2012.15072,
Author = {Peixuan Li, Shun Su, Huaici Zhao},
Title = {RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving},
Year = {2020},
Eprint = {arXiv:2012.15072},
}
Official Implementation for the paper DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover’s Distance Improves Out-Of-Distribution Face Identification

DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover’s Distance Improves Out-Of-Distribution Face Identification Official Implementation for the pape

Anh M. Nguyen 36 Dec 28, 2022
The source code for 'Noisy-Labeled NER with Confidence Estimation' accepted by NAACL 2021

Kun Liu*, Yao Fu*, Chuanqi Tan, Mosha Chen, Ningyu Zhang, Songfang Huang, Sheng Gao. Noisy-Labeled NER with Confidence Estimation. NAACL 2021. [arxiv]

30 Nov 12, 2022
On the model-based stochastic value gradient for continuous reinforcement learning

On the model-based stochastic value gradient for continuous reinforcement learning This repository is by Brandon Amos, Samuel Stanton, Denis Yarats, a

Facebook Research 46 Dec 15, 2022
A Player for Kanye West's Stem Player. Sort of an emulator.

Stem Player Player Stem Player Player Usage Download the latest release here Optional: install ffmpeg, instructions here NOTE: DOES NOT ENABLE DOWNLOA

119 Dec 28, 2022
Dirty Pixels: Towards End-to-End Image Processing and Perception

Dirty Pixels: Towards End-to-End Image Processing and Perception This repository contains the code for the paper Dirty Pixels: Towards End-to-End Imag

50 Nov 18, 2022
Erpnext app for make employee salary on payroll entry based on one or more project with percentage for all project equal 100 %

Project Payroll this app for make payroll for employee based on projects like project on 30 % and project 2 70 % as account dimension it makes genral

Ibrahim Morghim 8 Jan 02, 2023
Spatial Contrastive Learning for Few-Shot Classification (SCL)

This repo contains the official implementation of Spatial Contrastive Learning for Few-Shot Classification (SCL), which presents of a novel contrastive learning method applied to few-shot image class

Yassine 34 Dec 25, 2022
Apply our monocular depth boosting to your own network!

MergeNet - Boost Your Own Depth Boost custom or edited monocular depth maps using MergeNet Input Original result After manual editing of base You can

Computational Photography Lab @ SFU 142 Dec 17, 2022
Heat transfer problemas solved using python

heat-transfer Heat transfer problems solved using python isolation-convection.py compares the temperature distribution on the problem as shown in the

2 Nov 14, 2021
A collection of resources on GAN Inversion.

This repo is a collection of resources on GAN inversion, as a supplement for our survey

Covid19-Forecasting - An interactive website that tracks, models and predicts COVID-19 Cases

Covid-Tracker This is an interactive website that tracks, models and predicts CO

Adam Lahmadi 1 Feb 01, 2022
This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, Alan Yuille).

TransFG: A Transformer Architecture for Fine-grained Recognition Official PyTorch code for the paper: TransFG: A Transformer Architecture for Fine-gra

Ju He 307 Jan 03, 2023
Vehicle Detection Using Deep Learning and YOLO Algorithm

VehicleDetection Vehicle Detection Using Deep Learning and YOLO Algorithm Dataset take or find vehicle images for create a special dataset for fine-tu

Maryam Boneh 96 Jan 05, 2023
torchlm is aims to build a high level pipeline for face landmarks detection, it supports training, evaluating, exporting, inference(Python/C++) and 100+ data augmentations

💎A high level pipeline for face landmarks detection, supports training, evaluating, exporting, inference and 100+ data augmentations, compatible with torchvision and albumentations, can easily instal

DefTruth 142 Dec 25, 2022
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m

The Apache Software Foundation 20.2k Jan 05, 2023
Neural Magic Eye: Learning to See and Understand the Scene Behind an Autostereogram, arXiv:2012.15692.

Neural Magic Eye Preprint | Project Page | Colab Runtime Official PyTorch implementation of the preprint paper "NeuralMagicEye: Learning to See and Un

Zhengxia Zou 56 Jul 15, 2022
RealTime Emotion Recognizer for Machine Learning Study Jam's demo

Emotion recognizer Table of contents Clone project Dataset Install dependencies Main program Demo 1. Clone project git clone https://github.com/GDSC20

Google Developer Student Club - UIT 1 Oct 05, 2021
Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments

Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments Paper: arXiv (ICRA 2021) Video : https://youtu.be/CC

Sachini Herath 68 Jan 03, 2023
CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection

CLOCs is a novel Camera-LiDAR Object Candidates fusion network. It provides a low-complexity multi-modal fusion framework that improves the performance of single-modality detectors. CLOCs operates on

Su Pang 254 Dec 16, 2022
Pytorch implementation of the paper "Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization"

Pytorch implementation of the paper "Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization"

Dongkyu Lee 4 Sep 18, 2022