3D Object Detection Based on Iterative Self-Training

Kangru Wang; Jingang Tan; Liang Du; Lili Chen; Jiamao Li; Xiaolin Zhang

doi:10.3788/AOS202040.0915005

Acta Optica Sinica, Volume. 40, Issue 9, 0915005(2020)

3D Object Detection Based on Iterative Self-Training

Kangru Wang^1,2、*, Jingang Tan^1,2, Liang Du³, Lili Chen¹, Jiamao Li¹, and Xiaolin Zhang¹

¹Bionic Vision System Laboratory, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China

²University of Chinese Academy of Sciences, Beijing, 100049, China

³Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Ministry of Education, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China

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Figures & Tables(13)

Fig. 1. Flow chart of 3D object detection system

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Fig. 2. Architectural diagram of IST-Net

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Fig. 3. Flow chart of iterative self-training

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Fig. 4. Architectural diagram of SAFF-3DOD Net

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Fig. 5. Diagram of SAFFM

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Fig. 6. Qualitative comparison of baseline and our method on estimated disparity map. (a) RGB left image; (b) PSMNET method; (c) our disparity estimation method

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Fig. 7. Qualitative comparison of baseline and our method on estimated point cloud. (a) RGB left image; (b) PSMNET method; (c) our disparity estimation method

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Fig. 8. Qualitative comparison of 3D object detection results. (a) Pseudo- LiDAR; (b) our method

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Table 1. Detailed configuration of SAFFM

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Table 1. Detailed configuration of SAFFM

Parameter	SAFFM in region proposal network		SAFFM in detection network
Parameter	Layer setting	Output dimension	Layer setting	Output dimension
F_RGB/F_BEV		3×3×1		7×7×32
L₀			1--1×1	7×7×1
I_RGB/I_BEV		9×1×1		49×1×1
L₁	36--1×1	36×1×1	98--1×1	98×1×1
L₂	36--1×1	36×1×1	98--1×1	98×1×1
L₃	18--1×1	18×1×1	49--1×1	49×1×1
L₄	9--1×1	9×1×1	49--1×1	49×1×1
Sigmoid		9×1×1		49×1×1
Spatial-attention map		3×3×1		7×7×1
Weighted F_RGB and weighted F_BEV		3×3×1		7×7×32
F_output		3×3×1		7×7×32

Table 2. Quantitative comparison of disparity estimation on KITTI 3D object detection validation set
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Table 2. Quantitative comparison of disparity estimation on KITTI 3D object detection validation set
Method Disparity error rate /%
Object region Background region Global image
PSMNET(base) 8.96 4.35 5.49
Ours(IST) 8.69 4.18 5.27
Ours(SOL) 8.72 4.20 5.30
Ours(IST+SOL) 8.60 4.17 5.25

Table 3. Quantitative comparison of disparity estimation on KITTI stereo matching validation set
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Table 3. Quantitative comparison of disparity estimation on KITTI stereo matching validation set
Method Disparity error rate /%
Object region Background region Global image
Stereonet 11.14 5.23 6.99
PSMNET 7.23 3.33 4.44
Ours(IST+SOL) 6.83 3.20 4.27

Table 4. Quantitative comparison of 3D object detection on KITTI 3D object detection validation set (units of A_BEV and A_3D are both %)

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Table 4. Quantitative comparison of 3D object detection on KITTI 3D object detection validation set (units of A_BEV and A_3D are both %)

Method	IoU is 0.5			IoU is 0.7
Method	Easy	Moderate	Hard	Easy	Moderate	Hard
Pseudo-LiDAR(base)	92.1/91.6	78.3/75.3	66.7/63.8	75.6/61.5	55.6/43.3	48.3/36.8
Ours(IST)	92.1/91.0	80.4/77.4	70.8/67.8	77.5/61.3	59.5/43.3	50.6/36.9
Ours(SOL)	92.3/91.5	80.6/75.9	69.1/66.2	78.8/63.1	58.2/43.7	50.1/37.4
Ours(IST+SOL)	92.1/91.4	81.0/78.0	69.2/66.3	78.4/63.5	59.6/45.0	50.8/38.6
Ours(SAFF)	92.0/91.5	78.3/75.4	68.5/65.5	77.7/63.0	57.1/43.3	48.6/37.0
Ours	94.5/92.5	81.6/78.6	73.6/70.7	80.9/65.8	60.7/46.1	52.3/39.4

Table 5. 3D object detection results on KITTI test benchmark(units of A_BEV and A_3D are both %)

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Table 5. 3D object detection results on KITTI test benchmark(units of A_BEV and A_3D are both %)

Method	Input	Easy	Moderate	Hard
MonoPSR^[7]	Monocular	18.33/10.76	12.58/7.25	9.91/5.85
Mono3D_PLiDAR^[8]	Monocular	21.27/10.76	13.92/7.50	11.25/6.10
TopNet-HighRes^[2]	Lidar	67.84/12.67	53.05/9.28	46.99/7.95
M3D-RPN^[9]	Monocular	21.02/14.76	13.67/9.71	10.23/7.42
AM3D^[10]	Monocular	25.03/16.50	17.32/10.47	14.91/9.52
RT3D^[3]	Lidar	56.44/23.74	44.00/19.14	42.34/18.86
RT3DStereo^[13]	Stereo	58.81/29.90	46.82/23.28	38.38/18.96
Stereo R-CNN^[27]	Stereo	61.92/47.58	41.31/30.23	33.42/23.72
Pseudo-LiDAR^[12]	Stereo	67.30/54.53	45.00/34.05	38.40/28.25
Ours	Stereo	71.47/58.70	49.61/37.92	42.71/31.99

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Kangru Wang, Jingang Tan, Liang Du, Lili Chen, Jiamao Li, Xiaolin Zhang. 3D Object Detection Based on Iterative Self-Training[J]. Acta Optica Sinica, 2020, 40(9): 0915005

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Paper Information

Category: Machine Vision

Received: Nov. 25, 2019

Accepted: Feb. 10, 2020

Published Online: May. 6, 2020

The Author Email: Kangru Wang (wangkangru@mail.sim.ac.cn)

DOI:10.3788/AOS202040.0915005

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Detailed configuration of SAFFM

Table 1. Detailed configuration of SAFFM

Table 2. Quantitative comparison of disparity estimation on KITTI 3D object detection validation set

Table 2. Quantitative comparison of disparity estimation on KITTI 3D object detection validation set

Table 3. Quantitative comparison of disparity estimation on KITTI stereo matching validation set

Table 3. Quantitative comparison of disparity estimation on KITTI stereo matching validation set

Table 4. Quantitative comparison of 3D object detection on KITTI 3D object detection validation set (units of ABEV and A3D are both %)

Table 4. Quantitative comparison of 3D object detection on KITTI 3D object detection validation set (units of ABEV and A3D are both %)

Table 5. 3D object detection results on KITTI test benchmark(units of ABEV and A3D are both %)

Table 5. 3D object detection results on KITTI test benchmark(units of ABEV and A3D are both %)

Table 4. Quantitative comparison of 3D object detection on KITTI 3D object detection validation set (units of A_BEV and A_3D are both %)

Table 4. Quantitative comparison of 3D object detection on KITTI 3D object detection validation set (units of A_BEV and A_3D are both %)

Table 5. 3D object detection results on KITTI test benchmark(units of A_BEV and A_3D are both %)

Table 5. 3D object detection results on KITTI test benchmark(units of A_BEV and A_3D are both %)