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MonoAMP: Adaptive Multi-Order Perceptual Aggregation for Monocular 3D Vehicle Detection.

Xiaoxi Hu¹, Tao Chen¹, Wentao Zhang¹

¹School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China.

Sensors (Basel, Switzerland)

|February 13, 2025

Summary

This summary is machine-generated.

MonoAMP improves monocular 3D object detection using adaptive multi-order perception and uncertainty-guided depth estimation. This algorithm enhances performance in complex autonomous driving scenes.

Keywords:

autonomous driving cross-dimensional attention monocular 3D object detection multi-order aggregation multi-scale fusion

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Area of Science:

Computer Vision
Autonomous Driving Systems

Background:

Monocular 3D object detection is crucial for autonomous driving due to efficiency.
Existing methods struggle with cross-dimensional attention and multi-order context in complex scenes.

Purpose of the Study:

To introduce MonoAMP, an adaptive multi-order perceptual aggregation algorithm.
To enhance feature attention and contextual information modeling for improved 3D object detection.

Main Methods:

Implemented triplet attention for enhanced cross-dimensional feature interaction.
Developed an adaptive multi-order perceptual aggregation module for dynamic context capture.
Proposed an uncertainty-guided adaptive depth ensemble for robust depth prediction.

Main Results:

MonoAMP achieved significant performance gains on the KITTI dataset (16.80% AP3D, 24.47% APBEV).
Ablation studies showed a 3.78% improvement in object detection accuracy over baseline methods.
Demonstrated superior detection capabilities, particularly in complex driving scenarios.

Conclusions:

MonoAMP effectively addresses limitations in current monocular 3D object detection methods.
The proposed adaptive aggregation and uncertainty-guided depth fusion enhance perception and accuracy.
MonoAMP offers a promising solution for robust 3D object detection in autonomous driving.