Fine-Grained Multilevel Fusion for Anti-Occlusion Monocular 3D Object Detection

Area of Science:

• Computer Vision
• Deep Learning
• 3D Object Detection

Background:

• Monocular 3D object detection methods often struggle with accurate depth estimation and handling occluded objects.
• Existing approaches rely on geometric constraints, which are insufficient for extracting rich fusion information from depth estimation.

Purpose of the Study:

• To develop a deep fine-grained multi-level fusion architecture for monocular 3D object detection.
• To introduce an anti-occlusion optimization process to improve detection in occluded scenes.

Main Methods:

• Integration of monocular 3D features with a pseudo-LiDAR filter generation network between multi-level layers.
• Utilizing inherent multi-scale features to promote depth and semantic information flow.
• Development of a novel loss function specifically designed to alleviate occlusion issues.

Main Results:

• The proposed architecture successfully extracts features with more reliable depth information.
• The anti-occlusion optimization significantly improves detection performance in complex, occluded scenes.
• Experimental results demonstrate competitive performance compared to existing methods.

Conclusions:

• The novel deep fusion architecture offers a robust solution for monocular 3D object detection.
• The anti-occlusion strategy effectively addresses a key challenge in real-world object detection scenarios.
• This framework shows significant promise for applications requiring accurate 3D perception from single images.