KITTI-stereo-2015

The stero 2015 benchmark consists of 200 training scenes and 200 test scenes (4 color images per scene, saved in loss less png format). Compared to the stereo 2012 and flow 2012 benchmarks, 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.

立体视觉2015基准数据集（Stereo 2015 Benchmark）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格式存储。相较于立体视觉2012基准数据集与光流2012基准数据集，该数据集涵盖动态场景，其真值（ground truth）通过半自动流程生成。
我们的评估服务器会针对全部200张测试图像的所有真值像素，计算坏像素的平均占比。针对该基准数据集，若视差（disparity）或光流端点误差小于3像素或小于5%，则认为该像素的估计结果正确；对于场景光流任务，需同时满足视差图（disparity maps）与光流图（flow map）均符合该判定标准。
我们要求所有方法在所有测试图像对上使用同一套参数。本数据集的开发工具包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《面向自动驾驶车辆的目标场景光流》（Object Scene Flow for Autonomous Vehicles，CVPR 2015）。
我们为一辆标准旅行车搭载了两台高分辨率彩色与灰度摄像机。准确的真值由Velodyne激光扫描仪与GPS定位系统提供。本数据集通过在中等城市卡尔斯鲁厄周边、乡村区域以及高速公路上行车采集得到。每张图像中最多可出现15辆汽车与30名行人。

搜集汇总

背景与挑战

背景概述

KITTI-stereo-2015是一个用于自动驾驶研究的立体视觉数据集，包含400个动态场景（200训练/200测试），每个场景提供4张彩色图像。数据集通过高精度传感器（激光扫描仪和GPS）采集，主要用于评估视差和场景流算法的性能，误差标准为<3像素或<5%。