首页|Yantai University Reports Findings in Intelligent Systems (Adaptive Learning Point Cloud and Image Diversity Feature Fusion Network for 3d Object Detection)
Yantai University Reports Findings in Intelligent Systems (Adaptive Learning Point Cloud and Image Diversity Feature Fusion Network for 3d Object Detection)
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NETL
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Springer Nature
A new study on Machine Learning - Intelligent Systems is now available. According to news originating from Yantai, People’s Republic of China, by NewsRx correspondents, research stated, “3D object detection is a critical task in the fields of virtual reality and autonomous driving. Given that each sensor has its own strengths and limitations, multi-sensor-based 3D object detection has gained popularity.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Yantai University, “However, most existing methods extract high-level image semantic features and fuse them with point cloud features, focusing solely on consistent information from both sensors while ignoring their complementary information. In this paper,we present a novel two-stage multi-sensor deep neural network, called the adaptive learning point cloud and image diversity feature fusion network (APIDFF-Net), for 3D object detection. Our approach employs the fine-grained image information to complement the point cloud information by combining low-level image features with high-level point cloud features. Specifically, we design a shallow image feature extraction module to learn fine-grained information from images, instead of relying on deep layer features with coarsegrained information. Furthermore, we design a diversity feature fusion (DFF) module that transforms low-level image features into point-wise image features and explores their complementary features through an attention mechanism, ensuring an effective combination of fine-grained image features and point cloud features.”
YantaiPeople’s Republic of ChinaAsiaIntelligent SystemsMachine LearningYantai University
NETL
NSTL
Springer Nature
