3D Object Detection Based on Extremely Sparse Laser Point Cloud and RGB Images

Table 1. Target detection accuracy of proposed algorithm on KITTI dataset
View table
Table 1. Target detection accuracy of proposed algorithm on KITTI dataset
Algorithm Car（IOU is 0.7） Person（IOU is 0.5） Bicycle（IOU is 0.5）
Easy Moderate Difficult Easy Moderate Difficult Easy Moderate Difficult
Proposed algorithm 87.98 77.14 73.33 45.97 38.94 35.81 68.12 55.25 53.55

Table 2. Target detection accuracy under the condition of sparse point cloud BEV as theinput of key point feature pyramid network

View table

Table 2. Target detection accuracy under the condition of sparse point cloud BEV as theinput of key point feature pyramid network

Input	Car（IOU is 0.7）			Person（IOU is 0.5）			Bicycle（IOU is 0.5）
Input	Easy	Moderate	Difficult	Easy	Moderate	Difficult	Easy	Moderate	Difficult
Sparse point cloud	4.50	3.15	2.88	0.96	0.94	0.9	0.82	0.78	0.70
Proposed algorithm	87.98	77.14	73.33	45.97	38.94	35.81	68.12	55.25	53.55

Table 3. Target detection accuracy under the condition of coded point cloud in previous view form

View table

Table 3. Target detection accuracy under the condition of coded point cloud in previous view form

Input

Car（IOU is 0.7）

Person（IOU is 0.5）

Bicycle（IOU is 0.5）

Easy

Moderate

Difficult

Easy

Moderate

Difficult

Easy

Moderate

Difficult

Point cloud

organized as（x，y，z）

60.25

52.80

45.60

35.86

31.38

29.72

41.82

39.56

36.09

Proposed algorithm

87.98

77.14

73.33

45.97

38.94

35.81

68.12

55.25

53.55

Table 4. Target detection accuracy under the condition of only taking the picture as the input of depth complement network

View table

Table 4. Target detection accuracy under the condition of only taking the picture as the input of depth complement network

Input	Car（IOU is 0.7）			Person（IOU is 0.5）			Bicycl e（IOU is 0.5）
Input	Easy	Moderate	Difficult	Easy	Moderate	Difficult	Easy	Moderate	Difficult
Image	34.52	21.04	19.03	22.24	13.56	12.26	5.63	3.43	3.10
Proposed algorithm	87.98	77.14	73.33	45.97	38.94	35.81	68.12	55.25	53.55

Table 5. Target detection accuracy under different point cloud down sampling rates

View table

Table 5. Target detection accuracy under different point cloud down sampling rates

Down sampling rate	Car（IOU is 0.7）			Person（IOU is 0.5）			Bicycle（IOU is 0.5）
Down sampling rate	Easy	Moderate	Difficult	Easy	Moderate	Difficult	Easy	Moderate	Difficult
1%	44.30	32.03	28.32	31.56	22.36	20.79	16.27	12.85	11.50
6%	73.54	60.17	55.80	38.91	33.40	29.95	48.2	40.76	39.80
8%	81.85	69.84	67.80	41.20	35.21	31.90	53.52	47.92	43.77
10%	87.98	77.14	73.33	45.97	38.94	35.81	68.12	55.25	53.55
12%	88.20	78.26	73.65	46.01	39.70	36.11	68.80	55.73	54.02

Table 6. Comparison of 3D object detection algorithms on KITTI dataset

View table

Table 6. Comparison of 3D object detection algorithms on KITTI dataset

Algorithm	Modality	Car（IOU is 0.7）			Person（IOU is 0.5）			Bicycle（IOU is 0.5）
Algorithm	Modality	Easy	Moderate	Difficult	Easy	Moderate	Difficult	Easy	Moderate	Difficult
VoxelNet	64-line LiDAR	77.47	65.11	57.73	39.48	33.69	31.51	61.22	48.36	44.37
SECOND	64-line LiDAR	83.13	73.66	66.20	51.07	42.56	37.29	70.51	53.85	46.90
PointRCNN	64-line LiDAR	85.94	75.76	68.32	49.43	41.78	38.63	73.93	59.60	53.59
SS3D^［30］	Camera	10.78	7.68	6.51	2.31	1.78	1.48	2.80	1.45	1.35
D4LCN^［31］	Camera	16.65	11.72	9.51	4.55	3.42	2.83	2.45	1.67	1.36
AVOD	64-line LiDAR+camera	76.39	66.47	60.23	36.10	27.86	25.76	57.19	42.08	38.29
Frustum PointNets	64-line LiDAR+camera	82.19	69.79	60.59	50.53	42.15	38.08	72.27	56.12	49.01
Proposed algorithm	Sparse point cloud+camera	87.98	77.14	73.33	45.97	38.94	35.81	68.12	55.25	53.55

Table 7. Running time comparison of 3D object detection algorithms on KITTI dataset
View table
Table 7. Running time comparison of 3D object detection algorithms on KITTI dataset
Parameter VoxelNet SECOND PointRCNN SS3D D4LCN Proposed algorithm
Running time /s 0.23 0.05 0.1 0.05 0.2 0.08

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Chao Qin, Yafei Wang, Yuchao Zhang, Chengliang Yin. 3D Object Detection Based on Extremely Sparse Laser Point Cloud and RGB Images[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1828004

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Paper Information

Category: Remote Sensing and Sensors

Received: Aug. 3, 2021

Accepted: Aug. 23, 2021

Published Online: Aug. 29, 2022

The Author Email: Yin Chengliang (clyin@sjtu.edu.cn)

DOI:10.3788/LOP202259.1828004

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Target detection accuracy of proposed algorithm on KITTI dataset

Table 1. Target detection accuracy of proposed algorithm on KITTI dataset

Table 2. Target detection accuracy under the condition of sparse point cloud BEV as theinput of key point feature pyramid network

Table 2. Target detection accuracy under the condition of sparse point cloud BEV as theinput of key point feature pyramid network

Table 3. Target detection accuracy under the condition of coded point cloud in previous view form

Table 3. Target detection accuracy under the condition of coded point cloud in previous view form

Table 4. Target detection accuracy under the condition of only taking the picture as the input of depth complement network

Table 4. Target detection accuracy under the condition of only taking the picture as the input of depth complement network

Table 5. Target detection accuracy under different point cloud down sampling rates

Table 5. Target detection accuracy under different point cloud down sampling rates

Table 6. Comparison of 3D object detection algorithms on KITTI dataset

Table 6. Comparison of 3D object detection algorithms on KITTI dataset

Table 7. Running time comparison of 3D object detection algorithms on KITTI dataset

Table 7. Running time comparison of 3D object detection algorithms on KITTI dataset