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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 1: AAAI-22 Technical Tracks 1

SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition

February 1, 2023

Authors

Zhaoxin Fan

Key Laboratory of Data Engineering and Knowledge Engineering of MOE, School of Information, Renmin University of China, 100872, Beijing, China


Zhenbo Song

School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China


Hongyan Liu

Department of Management Science and Engineering, Tsinghua University, 100084, Beijing, China


Zhiwu Lu

Gaoling School of Artificial Intelligence, Renmin University of China, 100872, Beijing, China


Jun He

Key Laboratory of Data Engineering and Knowledge Engineering of MOE, School of Information, Renmin University of China, 100872, Beijing, China


Xiaoyong Du

Key Laboratory of Data Engineering and Knowledge Engineering of MOE, School of Information, Renmin University of China, 100872, Beijing, China


Proceedings:

No. 1: AAAI-22 Technical Tracks 1

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Computer Vision I

Downloads:

Download PDF

Abstract:

Simultaneous Localization and Mapping (SLAM) and Autonomous Driving are becoming increasingly more important in recent years. Point cloud-based large scale place recognition is the spine of them. While many models have been proposed and have achieved acceptable performance by learning short-range local features, they always skip long-range contextual properties. Moreover, the model size also becomes a serious shackle for their wide applications. To overcome these challenges, we propose a super light-weight network model termed SVT-Net. On top of the highly efficient 3D Sparse Convolution (SP-Conv), an Atom-based Sparse Voxel Transformer (ASVT) and a Cluster-based Sparse Voxel Transformer (CSVT) are proposed respectively to learn both short-range local features and long-range contextual features. Consisting of ASVT and CSVT, SVT-Net can achieve state-of-the-art performance in terms of both recognition accuracy and running speed with a super-light model size (0.9M parameters). Meanwhile, for the purpose of further boosting efficiency, we introduce two simplified versions, which also achieve state-of-the-art performance and further reduce the model size to 0.8M and 0.4M respectively.

DOI:

10.1609/aaai.v36i1.19934


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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