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Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation

Li, Li; Shum, Hubert P.H.; Breckon, Toby P.

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Authors

Luis Li li.li4@durham.ac.uk
PGR Student Doctor of Philosophy



Abstract

Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semisupervised semantic segmentation methods with application domains such as autonomous driving. Existing work very often employs relatively large segmentation backbone networks to improve segmentation accuracy, at the expense of computational costs. In addition, many use uniform sampling to reduce ground truth data requirements for learning needed, often resulting in sub-optimal performance. To address these issues, we propose a new pipeline that employs a smaller architecture, requiring fewer ground-truth annotations to achieve superior segmentation accuracy compared to contemporary approaches. This is facilitated via a novel Sparse Depthwise Separable Convolution module that significantly reduces the network parameter count while retaining overall task performance. To effectively sub-sample our training data, we propose a new Spatio-Temporal Redundant Frame Downsampling (ST-RFD) method that leverages knowledge of sensor motion within the environment to extract a more diverse subset of training data frame samples. To leverage the use of limited annotated data samples, we further propose a soft pseudo-label method informed by Li- DAR reflectivity. Our method outperforms contemporary semi-supervised work in terms of mIoU, using less labeled data, on the SemanticKITTI (59.5@5%) and ScribbleKITTI (58.1@5%) benchmark datasets, based on a 2.3× reduction in model parameters and 641× fewer multiply-add operations whilst also demonstrating significant performance improvement on limited training data (i.e., Less is More).

Citation

Li, L., Shum, H. P., & Breckon, T. P. (2023). Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR52729.2023.00903

Conference Name 2023 IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)
Conference Location Vancouver, BC
Start Date Jun 17, 2023
End Date Jun 24, 2023
Acceptance Date Feb 27, 2023
Online Publication Date Aug 22, 2023
Publication Date 2023
Deposit Date Mar 24, 2023
Publicly Available Date Sep 7, 2023
Publisher Institute of Electrical and Electronics Engineers
Book Title 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN 9798350301304
DOI https://doi.org/10.1109/CVPR52729.2023.00903
Public URL https://durham-repository.worktribe.com/output/1135025
Publisher URL https://ieeexplore.ieee.org/xpl/conhome/1000147/all-proceedings

Files

Accepted Conference Proceeding (6.4 Mb)
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.







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