KITTI-Sceneflow

The sceneflow 2015 benchmark consists of 200 training scenes and 200 test scenes (4 color images per scene, saved in loss less png format). It comprises dynamic scenes for which the ground truth has been established in a semi-automatic process. Our evaluation server computes the percentage of bad pixels averaged over all ground truth pixels of all 200 test images. For this benchmark, we consider a pixel to be correctly estimated if the disparity or flow end-point error is <3px or <5% (for scene flow this criterion needs to be fulfilled for both disparity maps and the flow map). We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing disparity maps and flow fields. More details can be found in Object Scene Flow for Autonomous Vehicles (CVPR 2015). We equipped a standard station wagon with two high-resolution color and grayscale video cameras. Accurate ground truth is provided by a Velodyne laser scanner and a GPS localization system. Our datsets are captured by driving around the mid-size city of Karlsruhe, in rural areas and on highways. Up to 15 cars and 30 pedestrians are visible per image.

SceneFlow 2015基准测试集包含200个训练场景与200个测试场景，每个场景配有4张彩色图像，以无损PNG（lossless PNG）格式存储。该数据集涵盖动态场景，其真实标签（ground truth）通过半自动流程生成。本数据集的评估服务器会针对全部200张测试图像的所有真实标签像素，计算平均误匹配像素占比。对于该基准任务，若视差（disparity）或光流（flow）的端点误差小于3像素或小于5%，则认为该像素被正确估计；针对场景流任务，该判定标准需同时适用于视差图与光流图。我们要求所有方法在所有测试图像对上使用统一的参数集。本开发套件提供了数据格式的详细说明，以及用于读写视差图与光流场的MATLAB/C++实用工具函数。更多细节可参阅论文《面向自动驾驶车辆的目标场景流》（Object Scene Flow for Autonomous Vehicles, CVPR 2015）。我们在一款标准旅行车上搭载了两台高分辨率彩色与灰度摄像机，精确的真实标签由Velodyne激光扫描仪与GPS定位系统提供。本数据集采集于德国卡尔斯鲁厄这座中等城市的周边、乡村区域及高速公路，每张图像中最多可同时出现15辆汽车与30名行人。

搜集汇总

背景与挑战

背景概述

KITTI-Sceneflow是一个用于自动驾驶研究的动态场景数据集，包含400个场景（200训练/200测试）的光流和视差数据，采集于多种驾驶环境，并配备精确的激光扫描和GPS定位数据。数据集主要用于评估场景流算法的性能，要求算法在视差和光流估计上达到特定精度标准。