InScope: A New Real-world 3D Infrastructure-side Collaborative Perception Dataset for Open Traffic Scenarios
This is the official implementation of InScope dataset. "InScope: A New Real-world 3D Infrastructure-side Collaborative Perception Dataset for Open Traffic Scenarios".
Xiaofei Zhang , Yining Li , Jinping Wang , Xiangyi Qin , Ying Shen , Zhengping Fan , Xiaojun Tan†
The ground truth of sequence 0000.
Due to project restrictions, the InScope dataset is made conditionally public. If you need to use the InScope dataset, please fill in the following ./assets/InScope_Dataset_Release_Agreement.docx file and email your full name and affiliation to the contact person. We ask for your information only to ensure the dataset is used for non-commercial purposes.
After downloading the data, please put the data in the following structure:
├── InScope-Sec, InScope_Pri, and InScope datasets
│ ├── ImageSets
| |── train.txt
| |── test.txt
| |── val.txt
│ ├── labels
| |── 000000.txt
| |── 000001.txt
| |── 000002.txt
| |── ...
│ ├── points
| |── 000000.npy
| |── 000001.npy
| |── 000002.npy
| |── ...
├── InScope_track
│ ├── label_02
| |── 0000.txt
| |── 0001.txt
| |── 0002.txt
| |── ...
│ ├── points
| |── 0000
| |── 000000.bin
| |── 000001.bin
| |── 000002.bin
| |── ...
| |── 0001
| |── 0002
| |── ...
│ ├── evaluate_tracking.seqmap
│ ├── evaluate_tracking.seqmap.test
│ ├── evaluate_tracking.seqmap.training
│ ├── evaluate_tracking.seqmap.val
To facilitate researchers' use and understanding, we adapted the InScope dataset to the OpenPCDet framework and provided the corresponding dataset configuration file ./InScope.config
For detection training & inference, you can find instructions in detection_code/openpcdet/README_InScope.md in detail.
All the checkpoints are released in link in the tabels below, you can save them in codes/ckpts/ .
Results of 3D object detection based on the InScope dataset
Methods
Car AP@0.7
Pedestrian AP@0.5
Cyclist AP@0.5
Truck AP@0.7
mAP40
FPS
Download Link
PointRCNN
71.75
68.13
62.91
94.50
74.32
4.58
[URL ]
3DSSD
68.00
13.88
36.58
95.08
53.38
11.35
[URL ]
SECOND
72.82
47.95
59.91
95.98
69.17
20.58
[URL ]
Pointpillar
78.04
35.34
58.46
95.86
66.93
24.51
[URL ]
PV-RCNN
75.05
48.37
56.31
94.52
68.56
4.35
[URL ]
PV-RCNN++
80.55
53.31
70.92
95.92
75.18
14.66
[URL ]
CenterPoint
77.24
70.45
74.74
96.12
79.64
30.49
[URL ]
CenterPoint_RCNN
78.33
71.13
75.23
96.48
80.29
6.55
[URL ]
Results of 3D object detection based on the InScope-Sec, InScope_Pri, and InScope datasets Detection result based on the InScope-Sec Only
Methods
Car AP@0.7
Pedestrian AP@0.5
Cyclist AP@0.5
Truck AP@0.7
mAP40
FPS
Download Link
PointRCNN
14.12
23.66
20.62
45.36
25.94
22.94
[URL ]
Pointpillar
44.77
33.18
31.42
82.52
47.97
87.72
[URL ]
PV-RCNN++
43.49
34.60
39.94
76.04
48.52
16.67
[URL ]
CenterPoint
35.92
37.40
38.24
68.78
45.08
107.53
[URL ]
Detection result based on the InScope_Pri Only
Methods
Car AP@0.7
Pedestrian AP@0.5
Cyclist AP@0.5
Truck AP@0.7
mAP40
FPS
Download Link
PointRCNN
61.14
88.80
61.99
48.96
65.22
4.67
[URL ]
Pointpillar
67.34
23.82
43.51
91.59
56.57
25.25
[URL ]
PV-RCNN++
72.59
45.26
61.21
91.02
67.52
13.81
[URL ]
CenterPoint
61.31
49.62
52.73
82.02
61.42
33.90
[URL ]
Detection result based on the Early Fusion (InScope) Mechanism
Methods
Car AP@0.7
Pedestrian AP@0.5
Cyclist AP@0.5
Truck AP@0.7
mAP40
FPS
Download Link
PointRCNN
71.75
68.13
62.91
94.50
74.32
4.58
[URL ]
Pointpillar
78.04
35.34
58.46
95.86
66.93
24.33
[URL ]
PV-RCNN++
80.55
53.31
70.92
95.92
75.18
12.45
[URL ]
CenterPoint
77.24
70.45
74.74
96.12
79.64
30.49
[URL ]
Detection result based on the Late Fusion Mechanism
Methods
Car AP@0.7
Pedestrian AP@0.5
Cyclist AP@0.5
Truck AP@0.7
mAP40
FPS
Download Link
PointRCNN
62.69
61.31
52.31
90.93
66.81
1.32
[URL ]+[URL ]
Pointpillar
68.65
31.81
49.92
93.48
60.96
1.81
[URL ]+[URL ]
PV-RCNN++
68.01
53.47
56.95
92.65
67.77
1.21
[URL ]+[URL ]
CenterPoint
58.13
50.03
56.01
85.65
62.45
6.40
[URL ]+[URL ]
Detection result based on the Middle Fusion Mechanism
Methods
Car AP@0.7
Pedestrian AP@0.5
Cyclist AP@0.5
Truck AP@0.7
mAP40
FPS
Download Link
Point-RCNN
-
-
-
-
-
-
Pointpillar
-
-
-
-
-
-
PV-RCNN++
73.78
52.06
62.06
91.89
69.95
13.02
[URL ]
CenterPoint
52.74
38.95
51.19
81.73
56.15
15.85
[URL ]
Results of data domain transfer on the car class
Source→Target
DAIR-V2X-I→KITTI
ONCE→KITTI
InScope→KITTI
InScope→DAIR-V2X-I
DAIR-V2X-I→InScope
mAP40
mAP40
mAP40
mAP40
AP40
Source Domain
37.98[URL ]
41.65[URL ]
52.97[URL ]
31.05[URL ]
32.16[URL ]
SN
44.80[URL ]
49.34[URL ]
61.87[URL ]
31.81[URL ]
33.25[URL ]
ST3D
65.35[URL ]
58.19[URL ]
74.63[URL ]
48.98[URL ]
37.03[URL ]
Target Domain
81.63[URL ]
81.63[URL ]
81.63[URL ]
81.41[URL ]
71.75[URL ]
3D Multiobject tracking results on the car, pedestrian, cyclist, and truck. Tracking result of the AD3DMOT based on the InScope dataset on the car class (IoU threshold = 0.5/0.7)
Detector
sAMOTA↑
MOTA↑
IDSW↓
FRAG↓
PointRCNN
74.81/60.34
63.25/44.45
12/6
595/1834
Pointpillar
82.23/64.98
68.85/46.82
56/44
391/2166
PVRCNN++
81.63/68.71
67.56/50.72
83/39
386/1560
Centerpoint
78.76/61.25
61.02/40.98
27/15
367/1720
Tracking result of the AD3DMOT based on the InScope-Pri dataset on the car class (IoU threshold = 0.5/0.7)
Detector
sAMOTA↑
MOTA↑
IDSW↓
FRAG↓
PointRCNN
61.14/44.91
55.04/35.34
42/31
1319/2406
Pointpillar
74.02/51.81
66.89/37.84
154/63
1820/3138
PVRCNN++
73.47/57.82
54.98/37.94
378/99
914/1524
Centerpoint
76.01/49.32
61.89/31.07
103/49
717/2151
Tracking result of the AD3DMOT based on the InScope dataset on the pedestrian class (IoU threshold = 0.25/0.5)
Detector
sAMOTA↑
MOTA↑
IDSW↓
FRAG↓
PointRCNN
59.89/56.59
39.73/37.06
1/1
6/22
Pointpillar
32.09/27.42
27.79/25.36
0/0
4/24
PVRCNN++
31.39/28.54
27.71/25.75
3/3
10/20
Centerpoint
67.38/62.03
63.48/59.30
5/4
8/35
Tracking result of the AD3DMOT based on the InScope-Pri dataset on the pedestrian class (IoU threshold = 0.25/0.5)
Detector
sAMOTA↑
MOTA↑
IDSW↓
FRAG↓
PointRCNN
78.76/72.65
67.61/60.94
1/1
189/241
Pointpillar
78.14/72.78
68.68/61.43
7/6
130/321
PVRCNN++
73.76/67.67
58.18/51.61
25/1
2121/205
Centerpoint
75.37/64.27
65.03/53.43
10/7
298/500
Tracking result of the AD3DMOT based on the InScope dataset on the cyclist class (IoU threshold = 0.25/0.5)
Detector
sAMOTA↑
MOTA
IDSW↓
FRAG↓
PointRCNN
60.97/50.27
41.56/33.77
10/13
99/272
Pointpillar
49.96/33.75
33.82/22.33
3/13
64/379
PVRCNN++
63.00/52.65
43.22/34.12
126/82
177/349
Centerpoint
68.78/57.50
45.42/37.58
6/16
70/267
Tracking result of the AD3DMOT based on the InScope-Pri dataset on the cyclist class (IoU threshold = 0.25/0.5)
Detector
sAMOTA↑
MOTA↑
IDSW↓
FRAG↓
PointRCNN
38.31/25.57
27.68/18.74
31/27
302/595
Pointpillar
27.90/9.46
19.41/5.58
22/12
272/275
PVRCNN++
23.27/17.06
12.37/10.44
48/32
151/140
Centerpoint
55.81/34.88
38.70/19.55
46/19
198/613
Tracking result of the AD3DMOT based on the InScope dataset on the truck class (IoU threshold = 0.5/0.7)
Detector
sAMOTA↑
MOTA↑
IDSW↓
FRAG↓
PointRCNN
82.53/78.67
73.34/68.20
3/2
124/181
Pointpillar
82.18/76.79
75.26/70.33
9/8
80/182
PVRCNN++
81.50/77.20
69.15/64.53
9/8
76/141
Centerpoint
81.44/76.11
71.89/65.85
7/7
70/207
Tracking result of the AD3DMOT based on the InScope-Pri dataset on the truck class (IoU threshold = 0.5/0.7)
Detector
sAMOTA↑
MOTA↑
IDSW↓
FRAG↓
PointRCNN
78.76/72.65
67.61/60.94
1/1
189/241
Pointpillar
78.14/72.78
68.68/61.43
7/6
130/321
PVRCNN++
73.76/67.67
58.18/51.61
25/1
2121/205
Centerpoint
75.37/64.27
65.03/53.43
10/7
298/500
The code and configuration of 3DMOT on the InScope dataset will be released.
If you find InScope useful in your research or applications, please consider giving us a star 🌟.
