BEVStereo: Enhancing Depth Estimation in Multi-View 3D Object Detection with Temporal Stereo

Institute of Computing Technology · University of Chinese Academy of Sciences · +3 more institutions

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Abstract

Restricted by the ability of depth perception, all Multi-view 3D object detection methods fall into the bottleneck of depth accuracy. By constructing temporal stereo, depth estimation is quite reliable in indoor scenarios. However, there are two difficulties in directly integrating temporal stereo into outdoor multi-view 3D object detectors: 1) The construction of temporal stereos for all views results in high computing costs. 2) Unable to adapt to challenging outdoor scenarios. In this study, we propose an effective method for creating temporal stereo by dynamically determining the center and range of the temporal stereo. The most confident center is found using the EM algorithm. Numerous experiments on…

Citation impact

183
total citations
FWCI
10.79
Percentile
100%
References
50
Citations per year

Authors

6

Topics & keywords

Keywords
  • Computer science
  • Artificial intelligence
  • Computer vision
  • Bottleneck
  • Stereopsis
  • Stereo camera
  • Object (grammar)
  • Depth perception
UN Sustainable Development Goals
  • Sustainable cities and communities
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