InScope: A New Real-world 3D Infrastructure-side Collaborative Perception Dataset for Open Traffic Scenarios

This is the official implementation of the InScope dataset. The paper has been accepted by Information Fusion. "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.

Overview

• Data Download
• Data Loading
• Quick Start
• Benchmark
• TODO
• Citation
• Acknowledgment

Data Download

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

Data Loading

To facilitate researchers' use and understanding, we adapted the InScope dataset to the OpenPCDet framework and provided the corresponding dataset configuration file ./InScope.config

Quick Start

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/.

Benchmark

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	[pri URL]+[sec URL]
Pointpillar	68.65	31.81	49.92	93.48	60.96	1.81	[pri URL]+[sec URL]
PV-RCNN++	68.01	53.47	56.95	92.65	67.77	1.21	[pri URL]+[sec URL]
CenterPoint	58.13	50.03	56.01	85.65	62.45	6.40	[pri URL]+[sec URL]

Detection result based on the Middle Fusion Mechanism (based on BEV fusion framework)

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	[BEV fusion URL]
CenterPoint	52.74	38.95	51.19	81.73	56.15	15.85	[BEV fusion URL]

We provide another middle fusion framework, based on 2D feature fusion mechanism. The checkpoint can be found in [PV-RCNN++ 2D fusion URL] and [CenterPoint 2D fusion 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

TODO

The code and configuration of 3DMOT on the InScope dataset will be released.

Citation

If you find InScope useful in your research or applications, please consider giving us a star 🌟.

The BibTeX format is as follows:

@article{inscope_2026,
title = {InScope: A new real-world 3D infrastructure-side collaborative perception dataset for open traffic scenarios},
journal = {Information Fusion},
volume = {128},
pages = {103951},
year = {2026},
issn = {1566-2535},
doi = {https://doi.org/10.1016/j.inffus.2025.103951},
author = {Xiaofei Zhang and Yining Li and Jinping Wang and Xiangyi Qin and Ying Shen and Zhengping Fan and Xiaojun Tan},
}

Acknowledgment

• DAIR-V2X
• AB3DMOT
• ST3D