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

[1] Ku J, Mozifian M, Lee J et al. Joint 3D proposal generation and object detection from view aggregation. [C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 1-5, 2018, Madrid. New York: IEEE, 1-8(2018).

[2] Wirges S, Fischer T, Stiller C et al. Object detection and classification in occupancy grid maps using deep convolutional networks. [C]//2018 21st International Conference on Intelligent Transportation Systems (ITSC), November 4-7, 2018, Maui, HI. New York: IEEE, 3530-3535(2018).

[3] Zeng Y M, Hu Y, Liu S C et al. RT3D: real-time 3-D vehicle detection in LiDAR point cloud for autonomous driving[J]. IEEE Robotics and Automation Letters, 3, 3434-3440(2018).

[4] Yang Z T, Sun Y N, Liu S et al. -07-22)[2019-10-08], org/abs/1907, 10471(2019). https://arxiv.

[5] Shi S S, Wang X G, Li H S. PointRCNN: 3D object proposal generation and detection from point cloud. [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA. New York: IEEE, 770-779(2019).

[6] Zhang Y, Ren G Q, Cheng Z Y et al. Application research of there-dimensional LiDAR in unmanned vehicle environment perception[J]. Laser & Optoelectronics Progress, 56, 130001(2019).

[7] Ku J, Pon A D, Waslander S L. Monocular 3D object detection leveraging accurate proposals and shape reconstruction. [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA. New York: IEEE, 11867-11876(2019).

[8] Weng X S. -03-23)[2019-10-08][EB/OL]. Kitani K. Monocular 3D object detection with pseudo-LiDAR point cloud., org/abs/1903, 09847(2019). https://arxiv.

[9] Brazil G. -07-13)[2019-10-08][EB/OL]. Liu X M. M3D-RPN: monocular 3D region proposal network for object detection., org/abs/1907, 06038(2019). https://arxiv.

[10] Ma X Z, Wang Z H, Li H J et al. -03-27)[2019-10-08], org/abs/1903, 11444(2019). https://arxiv.

[11] Chen X, Kundu K, Zhu Y et al. 3D object proposals for accurate object class detection. [C]// Proceedings of the 28 ^th International Conference on Neural Information Processing Systems, December 7-12, 2015, Montreal, Quebec, Canada. New York: Curran Associates, 424-432(2015).

[12] Wang Y, Chao W L, Garg D et al. Pseudo-LiDAR from visual depth estimation: bridging the gap in 3D object detection for autonomous driving. [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA. New York: IEEE, 8445-8453(2019).

[13] Konigshof H, Salscheider N O, Stiller C. Realtime 3D object detection for automated driving using stereo vision and semantic information. [C]//2019 IEEE Intelligent Transportation Systems Conference (ITSC), October 27-30, 2019, Auckland, New Zealand. New York: IEEE, 19211370(2019).

[14] Chang J R, Chen Y S. Pyramid stereo matching network. [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT. New York: IEEE, 5410-5418(2018).

[15] Cheng X J, Wang P, Yang R G[M]. Depth estimation via affinity learned with convolutional spatial propagation network, 108-125(2018).

[16] Park J, Woo S, Lee J et al. -07-18)[2019-10-03], org/abs/1807, 06514(2018). https://arxiv.

[17] Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? The KITTI vision benchmark suite. [C]//2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA. New York: IEEE, 3354-3361(2012).

[18] Menze M, Heipke C, Remote Sensing, Spatial Information Sciences. II-, 3/W5, 427-434(2015).

[19] Meister S, Hur J. -11-21)[2019-10-03][EB/OL]. Roth S. UnFlow: unsupervised learning of optical flow with a bidirectional census loss., org/abs/1711, 07837(2017). https://arxiv.

[20] Geiger A, Lenz P, Stiller C et al. Vision meets robotics: The KITTI dataset[J]. The International Journal of Robotics Research, 32, 1231-1237(2013).

[21] Guney F, Geiger A. Displets: Resolving stereo ambiguities using object knowledge. [C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 4165-4175(2015).

[22] Abu Alhaija H, Mustikovela S K, Mescheder L et al. Augmented reality meets computer vision: efficient data generation for urban driving scenes[J]. International Journal of Computer Vision, 126, 961-972(2018).

[23] Qi C R, Liu W, Wu C X et al. Frustum PointNets for 3D object detection from RGB-D data. [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 918-927(2018).

[24] Mayer N, Ilg E, Hausser P et al. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 4040-4048(2016).

[25] Simonelli A, Bulò S R R, Porzi L et al. -05-29)[2019-10-03], org/abs/1905, 12365(2019). https://arxiv.

[26] Khamis S, Fanello S, Rhemann C et al[M]. StereoNet: guided hierarchical refinement for real-time edge-aware depth prediction, 596-613(2018).

[27] Li P L, Chen X Z, Shen S J. Stereo R-CNN based 3D object detection for autonomous driving. [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA. New York: IEEE, 7644-7652(2019).