2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper ID3D-2.11
Paper Title SEMI-SUPERVISED 3D OBJECT DETECTION VIA ADAPTIVE PSEUDO-LABELING
Authors Hongyi Xu, Fengqi Liu, Qianyu Zhou, Shanghai Jiao Tong University, China; Jinkun Hao, East China University of Science and Technology, China; Zhijie Cao, Zhengyang Feng, Lizhuang Ma, Shanghai Jiao Tong University, China
Session3D-2: Point Cloud Processing 2
LocationArea J
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Three-Dimensional Image and Video Processing: Point cloud processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 3D object detection is an important task in computer vision. Most existing methods require a large number of high-quality 3D annotations, which are expensive to collect. Especially for outdoor scenes, the problem becomes more severe due to the sparseness of the point cloud and the complexity of urban scenes. Semi-supervised learning is a promising technique to mitigate the data annotation issue. Inspired by this, we propose a novel semi-supervised framework based on pseudo-labeling for outdoor 3D object detection tasks. We design the Adaptive Class Confidence Selection module (ACCS) to generate high-quality pseudo-labels. Besides, we propose Holistic Point Cloud Augmentation (HPCA) for unlabeled data to improve robustness. Experiments on the KITTI benchmark demonstrate the effectiveness of our method. Code and supplementary material are available at https://github.com/tayson0825/SS3DOD.