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},
}
CLIP: Connecting Text and Image (Learning Transferable Visual Models From Natural Language Supervision)

CLIP (Contrastive Language–Image Pre-training) Experiments (Evaluation) Model Dataset Acc (%) ViT-B/32 (Paper) CIFAR100 65.1 ViT-B/32 (Our) CIFAR100 6

Myeongjun Kim 52 Jan 07, 2023
Self-labelling via simultaneous clustering and representation learning. (ICLR 2020)

Self-labelling via simultaneous clustering and representation learning 🆗 🆗 🎉 NEW models (20th August 2020): Added standard SeLa pretrained torchvis

Yuki M. Asano 469 Jan 02, 2023
PyTorch Implement for Path Attention Graph Network

SPAGAN in PyTorch This is a PyTorch implementation of the paper "SPAGAN: Shortest Path Graph Attention Network" Prerequisites We prefer to create a ne

Yang Yiding 38 Dec 28, 2022
Public scripts, services, and configuration for running a smart home K3S network cluster

makerhouse_network Public scripts, services, and configuration for running MakerHouse's home network. This network supports: TODO features here For mo

Scott Martin 1 Jan 15, 2022
Official repository of Semantic Image Matting

Semantic Image Matting This is the official repository of Semantic Image Matting (CVPR2021). Overview Natural image matting separates the foreground f

192 Dec 29, 2022
Repository for the Bias Benchmark for QA dataset.

BBQ Repository for the Bias Benchmark for QA dataset. Authors: Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Tho

ML² AT CILVR 18 Nov 18, 2022
Experiments on continual learning from a stream of pretrained models.

Ex-model CL Ex-model continual learning is a setting where a stream of experts (i.e. model's parameters) is available and a CL model learns from them

Antonio Carta 6 Dec 04, 2022
Representing Long-Range Context for Graph Neural Networks with Global Attention

Graph Augmentation Graph augmentation/self-supervision/etc. Algorithms gcn gcn+virtual node gin gin+virtual node PNA GraphTrans Augmentation methods N

UC Berkeley RISE 67 Dec 30, 2022
Predicting Price of house by considering ,house age, Distance from public transport

House-Price-Prediction Predicting Price of house by considering ,house age, Distance from public transport, No of convenient stores around house etc..

Musab Jaleel 1 Jan 08, 2022
The project was to detect traffic signs, based on the Megengine framework.

trafficsign 赛题 旷视AI智慧交通开源赛道,初赛1/177,复赛1/12。 本赛题为复杂场景的交通标志检测,对五种交通标志进行识别。 框架 megengine 算法方案 网络框架 atss + resnext101_32x8d 训练阶段 图片尺寸 最终提交版本输入图片尺寸为(1500,2

20 Dec 02, 2022
PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

SDEdit: Image Synthesis and Editing with Stochastic Differential Equations Project | Paper | Colab PyTorch implementation of SDEdit: Image Synthesis a

536 Jan 05, 2023
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation (CVPR 2021)

ASGNet The code is for the paper "Adaptive Prototype Learning and Allocation for Few-Shot Segmentation" (accepted to CVPR 2021) [arxiv] Overview data/

Gen Li 91 Dec 23, 2022
Differentiable Annealed Importance Sampling (DAIS)

Differentiable Annealed Importance Sampling (DAIS) This repository contains the code to reproduce the DAIS results from the paper Differentiable Annea

Guodong Zhang 6 Dec 26, 2021
Implemented fully documented Particle Swarm Optimization algorithm (basic model with few advanced features) using Python programming language

Implemented fully documented Particle Swarm Optimization (PSO) algorithm in Python which includes a basic model along with few advanced features such as updating inertia weight, cognitive, social lea

9 Nov 29, 2022
Deep Anomaly Detection with Outlier Exposure (ICLR 2019)

Outlier Exposure This repository contains the essential code for the paper Deep Anomaly Detection with Outlier Exposure (ICLR 2019). Requires Python 3

Dan Hendrycks 464 Dec 27, 2022
Official PyTorch implementation of Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval.

Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval PyTorch This is the PyTorch implementation of Retrieve in Style: Unsupervised Fa

60 Oct 12, 2022
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

LSTM Neural Network for Time Series Prediction LSTM built using the Keras Python package to predict time series steps and sequences. Includes sine wav

Jakob Aungiers 4.1k Jan 02, 2023
Reporting and Visualization for Hazardous Events

Reporting and Visualization for Hazardous Events

Jv Kyle Eclarin 2 Oct 03, 2021
PyTorch Implementation of Spatially Consistent Representation Learning(SCRL)

Spatially Consistent Representation Learning (CVPR'21) Official PyTorch implementation of Spatially Consistent Representation Learning (SCRL). This re

Kakao Brain 102 Nov 03, 2022
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

Overview PyTorch 0.4.1 | Python 3.6.5 Annotated implementations with comparative introductions for minimax, non-saturating, wasserstein, wasserstein g

Shayne O'Brien 471 Dec 16, 2022