KITTI和Cityscapes是两大自动驾驶视觉算法评测数据集。KITTI覆盖多种驾驶场景，侧重于目标检测与追踪；Cityscapes则专注于城区场景的图像分割，提供了丰富多样的标注类别。
KITTI数据集由德国卡尔斯鲁厄理工学院和丰田美国技术研究院联合创办，是目前国际上最大的自动驾驶场景下的计算机视觉算法评测数据集。用于评测目标（机动车、非机动车、行人等）检测、目标跟踪、路面分割等计算机视觉技术在车载环境下的性能。
KITTI包含市区、乡村和高速公路等场景采集的真实图像数据，每张图像中多达15辆车和30个行人，还有各种程度的遮挡。KITTI数据集中，目标检测包括了车辆检测、行人检测、自行车等三个单项，目标追踪包括车辆追踪、行人追踪等两个单项，道路分割包括urban unmarked、urban marked、urban multiple marked三个场景及前三个场景的平均值urban road等四个单项。
总体上看，原始数据集被分类为’Road’, ’City’, ’Residential’, ’Campus’ 和 ’Person’。对于3D物体检测，label细分为car, van, truck, pedestrian, pedestrian(sitting), cyclist, tram以及misc组成。
The label files contain the following information, which can be read and
written using the matlab tools (readLabels.m, writeLabels.m) provided within
this devkit. All values (numerical or strings) are separated via spaces,
each row corresponds to one object. The 15 columns represent:
#Values Name Description
----------------------------------------------------------------------------
1 type Describes the type of object: 'Car', 'Van', 'Truck',
'Pedestrian', 'Person_sitting', 'Cyclist', 'Tram',
'Misc' or 'DontCare'
1 truncated Float from 0 (non-truncated) to 1 (truncated), where
truncated refers to the object leaving image boundaries
1 occluded Integer (0,1,2,3) indicating occlusion state:
0 = fully visible, 1 = partly occluded
2 = largely occluded, 3 = unknown
1 alpha Observation angle of object, ranging [-pi..pi]
4 bbox 2D bounding box of object in the image (0-based index):
contains left, top, right, bottom pixel coordinates
3 dimensions 3D object dimensions: height, width, length (in meters)
3 location 3D object location x,y,z in camera coordinates (in meters)
1 rotation_y Rotation ry around Y-axis in camera coordinates [-pi..pi]
1 score Only for results: Float, indicating confidence in
detection, needed for p/r curves, higher is better.
Cityscapes数据集则是由奔驰主推，提供无人驾驶环境下的图像分割数据集。用于评估视觉算法在城区场景语义理解方面的性能。Cityscapes包含50个城市不同场景、不同背景、不同季节的街景，提供5000张精细标注的图像、20000张粗略标注的图像、30类标注物体。用PASCAL VOC标准的 intersection-over-union （IoU）得分来对算法性能进行评价。 Cityscapes数据集共有fine和coarse两套评测标准，前者提供5000张精细标注的图像，后者提供5000张精细标注外加20000张粗糙标注的图像。
cityscapes数据集有30多类标注物体
List of cityscapes labels:
# Please adapt the train IDs as appropriate for your approach.
# Note that you might want to ignore labels with ID 255 during training.
# Further note that the current train IDs are only a suggestion. You can use whatever you like.
# Make sure to provide your results using the original IDs and not the training IDs.
# Note that many IDs are ignored in evaluation and thus you never need to predict these!
name | id | trainId | category | categoryId | hasInstances | ignoreInEval| color
--------------------------------------------------------------------------------------------------
unlabeled | 0 | 255 | void | 0 | 0 | 1 | (0, 0, 0)
ego vehicle | 1 | 255 | void | 0 | 0 | 1 | (0, 0, 0)
rectification border | 2 | 255 | void | 0 | 0 | 1 | (0, 0, 0)
out of roi | 3 | 255 | void | 0 | 0 | 1 | (0, 0, 0)
static | 4 | 255 | void | 0 | 0 | 1 | (0, 0, 0)
dynamic | 5 | 255 | void | 0 | 0 | 1 | (111, 74, 0)
ground | 6 | 255 | void | 0 | 0 | 1 | (81, 0, 81)
road | 7 | 0 | flat | 1 | 0 | 0 | (128, 64, 128)
sidewalk | 8 | 1 | flat | 1 | 0 | 0 | (244, 35, 232)
parking | 9 | 255 | flat | 1 | 0 | 1 | (250, 170, 160)
rail track | 10 | 255 | flat | 1 | 0 | 1 | (230, 150, 140)
building | 11 | 2 | construction | 2 | 0 | 0 | (70, 70, 70)
wall | 12 | 3 | construction | 2 | 0 | 0 | (102, 102, 156)
fence | 13 | 4 | construction | 2 | 0 | 0 | (190, 153, 153)
guard rail | 14 | 255 | construction | 2 | 0 | 1 | (180, 165, 180)
bridge | 15 | 255 | construction | 2 | 0 | 1 | (150, 100, 100)
tunnel | 16 | 255 | construction | 2 | 0 | 1 | (150, 120, 90)
pole | 17 | 5 | object | 3 | 0 | 0 | (153, 153, 153)
polegroup | 18 | 255 | object | 3 | 0 | 1 | (153, 153, 153)
traffic light | 19 | 6 | object | 3 | 0 | 0 | (250, 170, 30)
traffic sign | 20 | 7 | object | 3 | 0 | 0 | (220, 220, 0)
vegetation | 21 | 8 | nature | 4 | 0 | 0 | (107, 142, 35)
terrain | 22 | 9 | nature | 4 | 0 | 0 | (152, 251, 152)
sky | 23 | 10 | sky | 5 | 0 | 0 | (70, 130, 180)
person | 24 | 11 | human | 6 | 1 | 0 | (220, 20, 60)
rider | 25 | 12 | human | 6 | 1 | 0 | (255, 0, 0)
car | 26 | 13 | vehicle | 7 | 1 | 0 | (0, 0, 142)
truck | 27 | 14 | vehicle | 7 | 1 | 0 | (0, 0, 70)
bus | 28 | 15 | vehicle | 7 | 1 | 0 | (0, 60, 100)
caravan | 29 | 255 | vehicle | 7 | 1 | 1 | (0, 0, 90)
trailer | 30 | 255 | vehicle | 7 | 1 | 1 | (0, 0, 110)
train | 31 | 16 | vehicle | 7 | 1 | 0 | (0, 80, 100)
motorcycle | 32 | 17 | vehicle | 7 | 1 | 0 | (0, 0, 230)
bicycle | 33 | 18 | vehicle | 7 | 1 | 0 | (119, 11, 32)
license plate | -1 | -1 | vehicle | 7 | 0 | 1 | (0, 0, 142)
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