VIASEG: Visual Information Assisted Lightweight Point Cloud Segmentation

Zhibin Zhong, Chi Zhang, Yuehu Liu, Ying Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Rapid and precise point cloud segmentation is one of the prerequisites for real-time and robust autonomous perception and environmental understanding, which requires a balance between speed and accuracy in architecture design. However, recent lightweight architectures, though fast enough, rely on domain adaptation from time-consuming-constructed synthetic dataset and sophisticated post-processing procedure to improve their performance, neglecting the rich visual information acquired by cameras aside from LiDAR sensors. In this paper, such color information is embedded at data-level to boost the performance of real-time point cloud segmentation. Furthermore, a multiscale lightweight fully convolutional network, VIASeg, is proposed based on the newly designed Super Squeeze Residual module and Semantic Connection from higher convolutional layers to lower layers, which improves the performance by feature denoising with high level semantic information. The superiority of the proposed method is validated and demonstrated in the comparative and ablative experimental analysis, while maintaining the real-time characteristic.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages1500-1504
Number of pages5
ISBN (Electronic)9781538662496
DOIs
StatePublished - Sep 2019
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: Sep 22 2019Sep 25 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
CountryTaiwan, Province of China
CityTaipei
Period9/22/199/25/19

Keywords

  • Cross-modality Fusion
  • Fully Convolutional Residual Network
  • Point Cloud Segmentation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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