BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving

BLVD:构建自主驾驶的大规模5D语义基准

Jianru Xue, Jianwu Fang, Tao Li, Bohua Zhang, Pu Zhang, Zhen Ye and Jian Dou

Abstract—In autonomous driving community, numerous benchmarks have been established to assist the tasks of 3D/2D object detection, stereo vision, semantic/instance segmentation. However, the more meaningful dynamic evolution of the surrounding objects of ego-vehicle is rarely exploited, and lacks a large-scale dataset platform. To address this, we introduce BLVD, a large-scale 5D semantics benchmark which does not concentrate on the static detection or semantic/instance segmentation tasks tackled adequately before. Instead, BLVD aims to provide a platform for the tasks of dynamic 4D (3D+temporal) tracking, 5D (4D+interactive) interactive event recognition and intention prediction.This benchmark will boost the deeper understanding of traffic scenes than ever before. We totally yield 249,129 3D annotations, 4,902 independent individuals for tracking with the length of overall 214,922 points, 6,004 valid fragments for 5D interactive event recognition, and 4,900 individuals for 5D intention prediction. These tasks are contained in four kinds of scenarios depending on the object density (low and high) and light conditions (daytime and nighttime). The benchmark can be downloaded from our project site https://github.com/VCCIV/BLVD/.

在自动驾驶社区中,已经建立了许多基准来辅助3D / 2D物体检测,立体视觉,语义/实例分割的任务。然而,自我车辆周围物体的更有意义的动态演化很少被利用,并且缺乏大规模的数据集平台。为了解决这个问题,我们引入了BLVD,这是一个大规模的5D语义基准测试,它不专注于之前充分处理的静态检测或语义/实例分割任务。相反,BLVD旨在为动态4D(3D +时间)跟踪,5D(4D +交互式)交互式事件识别和意图预测的任务提供平台。该基准将比以往更加深入地了解交通场景。 我们完全产生249,129个3D注释,4,902个独立个体用于跟踪,总长度为214,922个点,6,004个有效片段用于5D交互事件识别,4,900个用于5D意图预测。这些任务包含在四种场景中,具体取决于对象密度(低和高)和光照条件(白天和夜晚)。 基准测试可以从我们的项目站点https://github.com/VCCIV/BLVD/下载。

在本文中,我们为自动驾驶构建了一个大规模的5D语义基准,该基准在各种有趣的场景下被捕获,并且经过有效和准确的校准,同步和整流。与以前的静态检测/分割任务不同,我们专注于对交通场景的更深入理解。具体而言,4D跟踪,5D交互事件识别和5D意图预测的任务在该基准测试中启动。通过仔细的注释,基准产生了249,129个3D注释,4,902个独立实例用于跟踪,总长度为214,922个点,6,004个用于5D交互式事件识别的3D注释,以及4,900个用于5D意图预测的个体。这些注释是在不同的光照条件下(白天和夜晚),不同密度的参与者(低密度和高密度)和不同的驾驶场景(高速公路和城市)收集的。

泡泡一分钟:BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving的更多相关文章

  1. 大规模视觉识别挑战赛ILSVRC2015各团队结果和方法 Large Scale Visual Recognition Challenge 2015

    Large Scale Visual Recognition Challenge 2015 (ILSVRC2015) Legend: Yellow background = winner in thi ...

  2. Lessons learned developing a practical large scale machine learning system

    原文:http://googleresearch.blogspot.jp/2010/04/lessons-learned-developing-practical.html Lessons learn ...

  3. 论文笔记之:Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation

    Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation Google  2016.10.06 官方 ...

  4. 快速高分辨率图像的立体匹配方法Effective large scale stereo matching

    <Effective large scale stereo matching> In this paper we propose a novel approach to binocular ...

  5. Introducing DataFrames in Apache Spark for Large Scale Data Science(中英双语)

    文章标题 Introducing DataFrames in Apache Spark for Large Scale Data Science 一个用于大规模数据科学的API——DataFrame ...

  6. 【原】Coursera—Andrew Ng机器学习—课程笔记 Lecture 17—Large Scale Machine Learning 大规模机器学习

    Lecture17 Large Scale Machine Learning大规模机器学习 17.1 大型数据集的学习 Learning With Large Datasets 如果有一个低方差的模型 ...

  7. [C12] 大规模机器学习(Large Scale Machine Learning)

    大规模机器学习(Large Scale Machine Learning) 大型数据集的学习(Learning With Large Datasets) 如果你回顾一下最近5年或10年的机器学习历史. ...

  8. Computer Vision_33_SIFT:Improving Bag-of-Features for Large Scale Image Search——2010

    此部分是计算机视觉部分,主要侧重在底层特征提取,视频分析,跟踪,目标检测和识别方面等方面.对于自己不太熟悉的领域比如摄像机标定和立体视觉,仅仅列出上google上引用次数比较多的文献.有一些刚刚出版的 ...

  9. 泡泡一分钟:Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization

    Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization 利用回归森林中的点和线进行RGB-D ...

随机推荐

  1. iptable千万不要yum remove iptables

    iptable千万不要运行yum remove iptables,进行卸载打开linux后发现没有firewalld和iptables,建议安装firewall 命令: yum install fir ...

  2. Centos7服务器搭建部署显卡计算环境以及常用软件的安装使用

    安装好anaconda的服务器上会more你已经安装好jupyter notebook,执行下面的命令可以提供链接地址允许远程浏览器打开并访问: jupyter notebook --no-brows ...

  3. lca:异象石(set+dfs序)

    题目:https://loj.ac/problem/10132 #include<bits/stdc++.h> using namespace std; ,N,k=,head[]; str ...

  4. Python应用-完成简单邮件发送功能

    项目中有时候需要用脚本来自动发送邮件,而用Python来发送邮件十分的方便,代码如下: #!/usr/bin/python #coding:utf-8 import smtplib from emai ...

  5. input 时间字段默认值

    背景: 时间字段展示默认值,开始时间为当天 0点,结束时间为当天晚上12点 代码: <input style="Width: 180px;float:left ;" type ...

  6. HBase的二级索引

    使用HBase存储中国好声音数据的案例,业务描述如下: 为了能高效的查询到我们需要的数据,我们在RowKey的设计上下了不少功夫,因为过滤RowKey或者根据RowKey查询数据的效率是最高的,我们的 ...

  7. Permission denied (publickey,gssapi-keyex,gssapi-with-mic).错误的解决

    SSH登录提示 Permission denied (publickey,gssapi-keyex,gssapi-with-mic). 修改被登录的SSH服务器ssh配置,/etc/ssh/sshd_ ...

  8. python - django 控制台输出 sql 语句

    只需要在 settings.py 文件中加入以下配置即可. LOGGING = { 'version': 1, 'disable_existing_loggers': False, 'handlers ...

  9. LeetCode 1239. Maximum Length of a Concatenated String with Unique Characters

    原题链接在这里:https://leetcode.com/problems/maximum-length-of-a-concatenated-string-with-unique-characters ...

  10. LeetCode 1135. Connecting Cities With Minimum Cost

    原题链接在这里:https://leetcode.com/problems/connecting-cities-with-minimum-cost/ 题目: There are N cities nu ...