lost_and_found

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The LostAndFound Dataset addresses the problem of detecting unexpected small obstacles on the road often caused by lost cargo. The dataset comprises 112 stereo video sequences with 2104 annotated frames (picking roughly every tenth frame from the recorded data).

The dataset is designed analogous to the 'Cityscapes' dataset. The datset provides: - stereo image pairs in either 8 or 16 bit color resolution - precomputed disparity maps - coarse semantic labels for objects and street

Descriptions of the labels are given here: http://www.6d-vision.com/laf_table.pdf

Split Examples
'test' 1,203
'train' 1,036
@inproceedings{pinggera2016lost,
  title={Lost and found: detecting small road hazards for self-driving vehicles},
  author={Pinggera, Peter and Ramos, Sebastian and Gehrig, Stefan and Franke, Uwe and Rother, Carsten and Mester, Rudolf},
  booktitle={2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2016}
}

lost_and_found/semantic_segmentation (default config)

  • Config description: Lost and Found semantic segmentation dataset.

  • Download size: 5.44 GiB

  • Feature structure:

FeaturesDict({
    'image_id': Text(shape=(), dtype=tf.string),
    'image_left': Image(shape=(1024, 2048, 3), dtype=tf.uint8),
    'segmentation_label': Image(shape=(1024, 2048, 1), dtype=tf.uint8),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
image_id Text tf.string
image_left Image (1024, 2048, 3) tf.uint8
segmentation_label Image (1024, 2048, 1) tf.uint8

lost_and_found/stereo_disparity

  • Config description: Lost and Found stereo images and disparity maps.

  • Download size: 12.16 GiB

  • Feature structure:

FeaturesDict({
    'disparity_map': Image(shape=(1024, 2048, 1), dtype=tf.uint8),
    'image_id': Text(shape=(), dtype=tf.string),
    'image_left': Image(shape=(1024, 2048, 3), dtype=tf.uint8),
    'image_right': Image(shape=(1024, 2048, 3), dtype=tf.uint8),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
disparity_map Image (1024, 2048, 1) tf.uint8
image_id Text tf.string
image_left Image (1024, 2048, 3) tf.uint8
image_right Image (1024, 2048, 3) tf.uint8

lost_and_found/full

  • Config description: Full Lost and Found dataset.

  • Download size: 12.19 GiB

  • Feature structure:

FeaturesDict({
    'disparity_map': Image(shape=(1024, 2048, 1), dtype=tf.uint8),
    'image_id': Text(shape=(), dtype=tf.string),
    'image_left': Image(shape=(1024, 2048, 3), dtype=tf.uint8),
    'image_right': Image(shape=(1024, 2048, 3), dtype=tf.uint8),
    'instance_id': Image(shape=(1024, 2048, 1), dtype=tf.uint8),
    'segmentation_label': Image(shape=(1024, 2048, 1), dtype=tf.uint8),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
disparity_map Image (1024, 2048, 1) tf.uint8
image_id Text tf.string
image_left Image (1024, 2048, 3) tf.uint8
image_right Image (1024, 2048, 3) tf.uint8
instance_id Image (1024, 2048, 1) tf.uint8
segmentation_label Image (1024, 2048, 1) tf.uint8

lost_and_found/full_16bit

  • Config description: Full Lost and Found dataset.

  • Download size: 34.90 GiB

  • Feature structure:

FeaturesDict({
    'disparity_map': Image(shape=(1024, 2048, 1), dtype=tf.uint8),
    'image_id': Text(shape=(), dtype=tf.string),
    'image_left': Image(shape=(1024, 2048, 3), dtype=tf.uint8),
    'image_right': Image(shape=(1024, 2048, 3), dtype=tf.uint8),
    'instance_id': Image(shape=(1024, 2048, 1), dtype=tf.uint8),
    'segmentation_label': Image(shape=(1024, 2048, 1), dtype=tf.uint8),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
disparity_map Image (1024, 2048, 1) tf.uint8
image_id Text tf.string
image_left Image (1024, 2048, 3) tf.uint8
image_right Image (1024, 2048, 3) tf.uint8
instance_id Image (1024, 2048, 1) tf.uint8
segmentation_label Image (1024, 2048, 1) tf.uint8