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SSCBench / configs /kitti360.yaml
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Create configs/kitti360.yaml
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# This file is covered by the LICENSE file in the root of this project.
nbr_classes: 19
grid_dims: [256, 32, 256] # (W, H, D)
labels:
0 : "unlabeled"
1 : "outlier"
10: "car"
11: "bicycle"
13: "bus"
15: "motorcycle"
16: "on-rails"
18: "truck"
20: "other-vehicle"
30: "person"
31: "bicyclist"
32: "motorcyclist"
40: "road"
44: "parking"
48: "sidewalk"
49: "other-ground"
50: "building"
51: "fence"
52: "other-structure"
60: "lane-marking"
70: "vegetation"
71: "trunk"
72: "terrain"
80: "pole"
81: "traffic-sign"
99: "other-object"
252: "moving-car"
253: "moving-bicyclist"
254: "moving-person"
255: "moving-motorcyclist"
256: "moving-on-rails"
257: "moving-bus"
258: "moving-truck"
259: "moving-other-vehicle"
color_map: # bgr
0 : [0, 0, 0]
1 : [0, 0, 255]
10: [245, 150, 100]
11: [245, 230, 100]
13: [250, 80, 100]
15: [150, 60, 30]
16: [255, 0, 0]
18: [180, 30, 80]
20: [255, 0, 0]
30: [30, 30, 255]
31: [200, 40, 255]
32: [90, 30, 150]
40: [255, 0, 255]
44: [255, 150, 255]
48: [75, 0, 75]
49: [75, 0, 175]
50: [0, 200, 255]
51: [50, 120, 255]
52: [0, 150, 255]
60: [170, 255, 150]
70: [0, 175, 0]
71: [0, 60, 135]
72: [80, 240, 150]
80: [150, 240, 255]
81: [0, 0, 255]
99: [255, 255, 50]
252: [245, 150, 100]
256: [255, 0, 0]
253: [200, 40, 255]
254: [30, 30, 255]
255: [90, 30, 150]
257: [250, 80, 100]
258: [180, 30, 80]
259: [255, 0, 0]
content: # as a ratio with the total number of points
0: 0.2848417452056936
1: 0.0
10: 0.01983749599411903
11: 9.719043492190106e-05
13: 8.812172901085409e-05
15: 0.00010387247418585811
16: 0.003498548779509923
18: 0.0022174305343550995
20: 0.0012293279317961631
30: 0.00019936933118689005
31: 8.34775248250103e-05
32: 0.0
40: 0.18301380891811173
44: 0.022222770320975044
48: 0.10658338271700511
49: 0.013312019108448078
50: 0.08266303552442433
51: 0.008460835861454078
52: 0.033257320133170704
60: 0.0
70: 0.16860900009553803
71: 0.0
72: 0.04732251443431639
80: 0.00032178137333487255
81: 8.959468958873611e-05
99: 0.0015188036331984448
252: 0.0
253: 0.0
254: 0.0
255: 0.0
256: 0.0
257: 0.0
258: 0.0
259: 0.0
# classes that are indistinguishable from single scan or inconsistent in
# ground truth are mapped to their closest equivalent
learning_map:
0 : 0 # "unlabeled"
1 : 0 # "outlier" mapped to "unlabeled" --------------------------mapped
10: 1 # "car"
11: 2 # "bicycle"
13: 5 # "bus" mapped to "other-vehicle" --------------------------mapped
15: 3 # "motorcycle"
16: 5 # "on-rails" mapped to "other-vehicle" ---------------------mapped
18: 4 # "truck"
20: 5 # "other-vehicle"
30: 6 # "person"
31: 0 # "bicyclist" mapped to "unlabeled" --------------------mapped
32: 0 # "motorcyclist" mapped to "unlabeled" -----------------mapped
40: 7 # "road"
44: 8 # "parking"
48: 9 # "sidewalk"
49: 10 # "other-ground"
50: 11 # "building"
51: 12 # "fence"
52: 17 # "other-structure"
60: 7 # "lane-marking" to "road" ---------------------------------mapped
70: 13 # "vegetation"
71: 0 # "trunk"
72: 14 # "terrain"
80: 15 # "pole"
81: 16 # "traffic-sign"
99: 18 # "other-object"
252: 1 # "moving-car" to "car" ------------------------------------mapped
253: 0 # "moving-bicyclist" to "unlabeled" ------------------------mapped
254: 6 # "moving-person" to "person" ------------------------------mapped
255: 0 # "moving-motorcyclist" to "unlabeled" ------------------mapped
256: 5 # "moving-on-rails" mapped to "other-vehicle" --------------mapped
257: 5 # "moving-bus" mapped to "other-vehicle" -------------------mapped
258: 4 # "moving-truck" to "truck" --------------------------------mapped
259: 5 # "moving-other"-vehicle to "other-vehicle" ----------------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: 44 # "parking"
9: 48 # "sidewalk"
10: 49 # "other-ground"
11: 50 # "building"
12: 51 # "fence"
13: 70 # "vegetation"
14: 72 # "terrain"
15: 80 # "pole"
16: 81 # "traffic-sign"
17: 52 # "other-structure"
18: 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 # "parking"
9: False # "sidewalk"
10: False # "other-ground"
11: False # "building"
12: False # "fence"
13: False # "vegetation"
14: False # "terrain"
15: False # "pole"
16: False # "traffic-sign"
17: False # "other-structure"
18: False # "other-object"
split: # sequence numbers
train:
- 0
- 2
- 3
- 4
- 5
- 7
- 10
valid:
- 6
test:
- 9