Constructing 3D Object Detectors Based on Deformable Convolutional Guided Depths

Constructing 3D Object Detectors Based on Deformable Convolutional Guided Depths

This paper introduces a depth-guided 3D object detection method that enhances the feature extraction capability of the backbone network through weak supervision. It combines large kernel convolution, global response normalization, and layer normalization techniques to significantly improve feature r...

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Bibliographic Details
Main Authors: Xinwang Zheng, Guangsong Yang, Lu Yang, Chengyu Lu
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
3D object detection
deep learning
depth guidance
autonomous automobiles
model refinement
Online Access:https://ieeexplore.ieee.org/document/10740295/
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https://ieeexplore.ieee.org/document/10740295/

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