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},
}
PyTorch source code for Distilling Knowledge by Mimicking Features

LSHFM.detection This is the PyTorch source code for Distilling Knowledge by Mimicking Features. And this project contains code for object detection wi

Guo-Hua Wang 4 Dec 17, 2022
A Sign Language detection project using Mediapipe landmark detection and Tensorflow LSTM's

sign-language-detection A Sign Language detection project using Mediapipe landmark detection and Tensorflow LSTM. The project is built for a vocabular

Hashim 4 Feb 06, 2022
BLEURT is a metric for Natural Language Generation based on transfer learning.

BLEURT: a Transfer Learning-Based Metric for Natural Language Generation BLEURT is an evaluation metric for Natural Language Generation. It takes a pa

Google Research 492 Jan 05, 2023
Learning with Subset Stacking

Learning with Subset Stacking (LESS) LESS is a new supervised learning algorithm that is based on training many local estimators on subsets of a given

S. Ilker Birbil 19 Oct 04, 2022
Scalable Graph Neural Networks for Heterogeneous Graphs

Neighbor Averaging over Relation Subgraphs (NARS) NARS is an algorithm for node classification on heterogeneous graphs, based on scalable neighbor ave

Facebook Research 67 Dec 03, 2022
An ML & Correlation platform for transforming disparate data points of interest into usable intelligence.

SSIDprobeCollector An ML & Correlation platform for transforming disparate data points of interest into usable intelligence. At a High level the platf

Bill Reyor 1 Jan 30, 2022
Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.

Conformal time-series forecasting Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021. If you use our code in yo

Kamilė Stankevičiūtė 36 Nov 21, 2022
Thermal Control of Laser Powder Bed Fusion using Deep Reinforcement Learning

This repository is the implementation of the paper "Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning", linked here. The project makes use of the Deep Reinforcement Library

BaratiLab 11 Dec 27, 2022
Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

Fully Convolutional Networks for Semantic Segmentation This is the reference implementation of the models and code for the fully convolutional network

Evan Shelhamer 3.2k Jan 08, 2023
Face Mask Detection System built with OpenCV, TensorFlow using Computer Vision concepts

Face mask detection Face Mask Detection System built with OpenCV, TensorFlow using Computer Vision concepts in order to detect face masks in static im

Vaibhav Shukla 1 Oct 27, 2021
Official Repository for "Robust On-Policy Data Collection for Data Efficient Policy Evaluation" (NeurIPS 2021 Workshop on OfflineRL).

Robust On-Policy Data Collection for Data-Efficient Policy Evaluation Source code of Robust On-Policy Data Collection for Data-Efficient Policy Evalua

Autonomous Agents Research Group (University of Edinburgh) 2 Oct 09, 2022
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
CellRank's reproducibility repository.

CellRank's reproducibility repository We believe that reproducibility is key and have made it as simple as possible to reproduce our results. Please e

Theis Lab 8 Oct 08, 2022
Unofficial implementation of PatchCore anomaly detection

PatchCore anomaly detection Unofficial implementation of PatchCore(new SOTA) anomaly detection model Original Paper : Towards Total Recall in Industri

Changwoo Ha 268 Dec 22, 2022
TensorFlow Implementation of Unsupervised Cross-Domain Image Generation

Domain Transfer Network (DTN) TensorFlow implementation of Unsupervised Cross-Domain Image Generation. Requirements Python 2.7 TensorFlow 0.12 Pickle

Yunjey Choi 864 Dec 30, 2022
End-to-End Dense Video Captioning with Parallel Decoding (ICCV 2021)

PDVC Official implementation for End-to-End Dense Video Captioning with Parallel Decoding (ICCV 2021) [paper] [valse论文速递(Chinese)] This repo supports:

Teng Wang 118 Dec 16, 2022
🕺Full body detection and tracking

Pose-Detection 🤔 Overview Human pose estimation from video plays a critical role in various applications such as quantifying physical exercises, sign

Abbas Ataei 20 Nov 21, 2022
Homepage of paper: Paint Transformer: Feed Forward Neural Painting with Stroke Prediction, ICCV 2021.

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction [Paper] [PaddlePaddle Implementation] Homepage of paper: Paint Transformer: Fee

442 Dec 16, 2022
This is a work in progress reimplementation of Instant Neural Graphics Primitives

Neural Hash Encoding This is a work in progress reimplementation of Instant Neural Graphics Primitives Currently this can train an implicit representa

Penn 79 Sep 01, 2022
Differentiable molecular simulation of proteins with a coarse-grained potential

Differentiable molecular simulation of proteins with a coarse-grained potential This repository contains the learned potential, simulation scripts and

UCL Bioinformatics Group 44 Dec 10, 2022