d3d.dataset.kitti360

class d3d.dataset.kitti360.KITTI360Loader(base_path, phase='training', inzip=False, trainval_split=1, trainval_random=False, trainval_byseq=False, nframes=0, interpolate_pose=True, compression=0)[source]

Bases: d3d.dataset.base.TrackingDatasetBase

Load KITTI-360 dataset into a usable format. The dataset structure should follow the official documents.

  • Zip Files:

    - calibration.zip
- data_3d_bboxes.zip
- data_3d_semantics.zip
- data_poses.zip
- data_timestamps_sick.zip
- data_timestamps_velodyne.zip
- 2013_05_28_drive_0000_sync_sick.zip
- 2013_05_28_drive_0000_sync_velodyne.zip
- ...

  • Unzipped Structure:

    - <base_path directory>
    - calibration
    - data_2d_raw
        - 2013_05_28_drive_0000_sync
        - ...
    - data_2d_semantics
        - 2013_05_28_drive_0000_sync
        - ...
    - data_3d_raw
        - 2013_05_28_drive_0000_sync
        - ...
    - data_3d_semantics
        - 2013_05_28_drive_0000_sync
        - ...

For description of constructor parameters, please refer to d3d.dataset.base.TrackingDatasetBase

Parameters

  • interpolate_pose (bool) – Not all frames contain pose data in KITTI-360. The loader returns interpolated pose if this param is set as True, otherwise returns None

  • compression (int) – The compression type of the created zip for semantic files. It should be one of the compression types specified in zipfile module.

  • base_path – directory containing the zip files, or the required data

  • inzip – whether the dataset is store in original zip archives or unzipped

  • phase – training, validation or testing

  • trainval_split – the ratio to split training dataset. See documentation of split_trainval_seq() for detail.

  • trainval_random – whether select the train/val split randomly. See documentation of split_trainval_seq() for detail.

  • nframes –

    number of consecutive frames returned from the accessors

    • If it’s a positive number, then it returns adjacent frames with total number reduced

    • If it’s a negative number, absolute value of it is consumed

    • If it’s zero, then it act like object detection dataset, which means the methods will return unpacked data

  • trainval_byseq – Whether split trainval partitions by sequences instead of frames

VALID_OBJ_CLASSES

alias of d3d.dataset.kitti360.utils.Kitti360Class

annotation_3dobject(idx, raw=False, visible_range=80)[source]
Parameters

visible_range – range for visible objects. Objects beyond that distance will be removed when reporting

calibration_data(idx)[source]

Return the calibration data. Notices that we assume the calibration is fixed among one squence, so it always return a single object.

Parameters

  • idx – index of requested lidar frames

  • raw – If false, converted d3d.abstraction.TransformSet will be returned, otherwise raw data will be returned in original format

camera_data(idx, names='cam1')[source]

Return the camera image data

Parameters

  • names – name of requested camera sensors. The default sensor is the first element in VALID_CAM_NAMES.

  • idx – index of requested image frames, see description in lidar_data() method.

intermediate_data(idx, names='sick', ninter_frames=None, report_semantic=True)[source]

Return the intermediate data (and annotations) between keyframes. For key frames data, please use corresponding function to load them

Parameters

  • idx – index of requested data frames

  • names – name of requested sensors.

  • ninter_frames – number of intermediate frames. If set to None, then all frames will be returned.

lidar_data(idx, names='velo', formatted=False)[source]

If multiple frames are requested, the results will be a list of list. Outer list corresponds to frame names and inner list corresponds to time sequence. So len(names) × len(frames) data objects will be returned

Parameters

  • names – name of requested lidar sensors. The default frame is the first element in VALID_LIDAR_NAMES.

  • idx – index of requested lidar frames

  • formatted –

    if true, the point cloud wrapped in a numpy record array will be returned

    • If single index is given, then the frame indexing is done on the whole dataset with trainval split

    • If a tuple is given, it’s considered to be a unique id of the frame (from identity() method), trainval split is ignored in this way and nframes offset is not added

pose(idx)[source]

Return (relative) pose of the vehicle for the frame. The base frame should be ground attached which means the base frame will follow a East-North-Up axis order.

Parameters

  • idx – index of requested frame

  • names – specify the sensor whose pose is requested. This option only make sense when the dataset contains separate timestamps for data from each sensor. In this case, the pose either comes from dataset, or from interpolation.

  • raw – if false, targets will be converted to d3d d3d.abstraction.EgoPose format, otherwise raw data will be returned in original format.

property pose_name

Return the sensor frame name whose coordinate the pose is reported in. This frame can be different from the default frame in the calibration TransformSet.

property sequence_ids

Return the list of sequence ids

property sequence_sizes

Return the mapping from sequence id to sequence sizes

timestamp(idx, names='velo')[source]

Return the timestamp of frame specified by the index, represented by Unix timestamp in macroseconds (usually 16 digits integer)

Parameters

  • idx – index of requested frame

  • names – specify the sensor whose pose is requested. This option only make sense when the dataset contains separate timestamps for data from each sensor.

class d3d.dataset.kitti360.Kitti360Class(value)[source]

Bases: enum.IntFlag

Categories and attributes of an annotation in KITTI-360 dataset.

The ids are encoded into 2bytes integer:

0xFF
  │└: category
  └─: label
bicycle = 135
box = 144
bridge = 82
building = 18
bus = 55
car = 23
caravan = 71
construction = 2
dynamic = 96
ego_vehicle = 32
fence = 50
flat = 1
garage = 112
gate = 128
ground = 112
guard_rail = 66
human = 6
lamp = 96
license_plate = 151
motorcycle = 119
nature = 4
object_ = 3
out_of_roi = 64
parking = 49
person = 22
pole = 19
polegroup = 35
rail_track = 65
rectification_border = 48
rider = 38
road = 17
sidewalk = 33
sky = 5
smallpole = 80
static = 80
stop = 144
terrain = 36
traffic_light = 51
traffic_sign = 67
trailer = 87
train = 103
trash_bin = 112
truck = 39
tunnel = 98
unknown_construction = 128
unknown_object = 160
unknown_vehicle = 144
unlabeled = 16
vegetation = 20
vehicle = 7
vending_machine = 128
void = 0
wall = 34