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nuscenes.yaml
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nuscenes.yaml
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# This file is covered by the LICENSE file in the root of this project.
nbr_classes: 11
grid_dims: [256, 32, 256] # (W, H, D)
labels:
10: "car"
11: "bicycle"
13: "bus"
15: "motorcycle"
18: "truck"
20: "other-vehicle"
30: "person"
40: "road"
48: "sidewalk"
49: "other-ground"
50: "building"
70: "vegetation"
90: "other-object"
99: "other-object2"
color_map: # bgr
10: [245, 150, 100]
11: [245, 230, 100]
13: [250, 80, 100]
15: [150, 60, 30]
18: [180, 30, 80]
20: [255, 0, 0]
30: [30, 30, 255]
40: [255, 0, 255]
48: [75, 0, 75]
49: [75, 0, 175]
50: [0, 200, 255]
70: [0, 175, 0]
90: [255, 255, 50]
99: [255, 255, 50]
content: # as a ratio with the total number of points
0: 1.98785873e-01
10: 5.77638478e-04
11: 3.68250377e-06
15: 5.21755341e-06
18: 1.86707793e-04
20: 2.22423703e-04
30: 5.49772336e-05
40: 8.65070320e-03
48: 1.93335044e-03
49: 1.73705954e-04
50: 4.92616008e-03
70: 4.89254452e-03
99: 2.43197941e-05
# classes that are indistinguishable from single scan or inconsistent in
# ground truth are mapped to their closest equivalent
# learning_map:
# 10: 1 # "car"
# 11: 2 # "bicycle"
# 13: 5 # "bus" mapped to "other-vehicle" --------------------------mapped
# 15: 3 # "motorcycle"
# 18: 4 # "truck"
# 20: 5 # "other-vehicle"
# 30: 6 # "person"
# 40: 7 # "road"
# 48: 8 # "sidewalk"
# 49: 9 # "other-ground"
# 50: 10 # "building"
# 70: 11 # "vegetation"
# 71: 0 # "trunk"
# 99: 12 # "other-object"
# 255: 0 # "moving-motorcyclist" to "unlabeled" ------------------mapped
# learning_map_inv: # inverse of previous map
# 0: 0 # "unlabeled", and others ignored
# 1: 10 # "car"
# 2: 11 # "bicycle"
# 3: 15 # "motorcycle"
# 4: 18 # "truck"
# 5: 20 # "other-vehicle"
# 6: 30 # "person"
# 7: 40 # "road"
# 8: 48 # "sidewalk"
# 9: 49 # "other-ground"
# 10: 50 # "building"
# 11: 70 # "vegetation"
# 12: 99 # "other-object"
# learning_ignore: # Ignore classes
# 0: True # "unlabeled", and others ignored
# 1: False # "car"
# 2: False # "bicycle"
# 3: False # "motorcycle"
# 4: False # "truck"
# 5: False # "other-vehicle"
# 6: False # "person"
# 7: False # "road"
# 8: False # "sidewalk"
# 9: False # "other-ground"
# 10: False # "building"
# 11: False # "vegetation"
# 12: False # "other-object"
learning_map:
10: 1 # "car"
11: 2 # "bicycle"
13: 1 # "bus" mapped to "car" --------------------------mapped
15: 3 # "motorcycle"
18: 1 # "truck" mapped to "car" --------------------------mapped
20: 1 # "other-vehicle" mapped to "car" --------------------------mapped
30: 4 # "person"
40: 5 # "road"
48: 6 # "sidewalk"
49: 7 # "other-ground"
50: 8 # "building"
70: 9 # "vegetation"
71: 9 # "trunk" mapped to "vegetation" --------------------------mapped
90: 10 # "other-object"
99: 10 # "other-object"
255: 0 # "moving-motorcyclist" to "unlabeled" ------------------mapped
learning_map_inv: # inverse of previous map
0: 0 # "unlabeled", and others ignored
1: 10 # "car"
2: 11 # "bicycle"
3: 15 # "motorcycle"
4: 30 # "person"
5: 40 # "road"
6: 48 # "sidewalk"
7: 49 # "other-ground"
8: 50 # "building"
9: 70 # "vegetation"
10: 99 # "other-object"
learning_ignore: # Ignore classes
0: True # "unlabeled", and others ignored
1: False # "car"
2: False # "bicycle"
3: False # "motorcycle"
4: False # "person"
5: False # "road"
6: False # "sidewalk"
7: False # "other-ground"
8: False # "building"
9: False # "vegetation"
10: False # "other-object"