Masked Autoencoder for Pre-Training on 3D Point Cloud Object Detection

Guangda Xie, Yang Li (), Hongquan Qu and Zaiming Sun
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Guangda Xie: College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
Yang Li: College of Information, North China University of Technology, Beijing 100144, China
Hongquan Qu: College of Information, North China University of Technology, Beijing 100144, China
Zaiming Sun: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China

Mathematics, 2022, vol. 10, issue 19, 1-14

Abstract: In autonomous driving, the 3D LiDAR (Light Detection and Ranging) point cloud data of the target are missing due to long distance and occlusion. It makes object detection more difficult. This paper proposes Point Cloud Masked Autoencoder (PCMAE), which can provide pre-training for most voxel-based point cloud object detection algorithms. PCMAE improves the feature representation ability of the 3D backbone for long-distance and occluded objects through self-supervised learning. First, a point cloud masking strategy for autonomous driving scenes named PC-Mask is proposed. It is used to simulate the problem of missing point cloud data information due to occlusion and distance in autonomous driving scenarios. Then, a symmetrical encoder–decoder architecture is designed for pre-training. The encoder is used to extract the high-level features of the point cloud after PC-Mask, and the decoder is used to reconstruct the complete point cloud. Finally, the pre-training method proposed in this paper is applied to SECOND (Sparsely Embedded Convolutional Detection) and Part-A2-Net (Part-aware and Aggregate Neural Network) object detection algorithms. The experimental results show that our method can speed up the model convergence speed and improve the detection accuracy, especially the detection effect of long-distance and occluded objects.

Keywords: 3D object detection; self-supervised; pre-training; autoencoder (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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