  Recent advancements in transformer-based monocular 3D object detection
techniques have exhibited exceptional performance in inferring 3D attributes
from single 2D images. However, most existing methods rely on
resource-intensive transformer architectures, which often lead to significant
drops in computational efficiency and performance when handling long sequence
data. To address these challenges and advance monocular 3D object detection
technology, we propose an innovative network architecture, MonoMM, a
Multi-scale \textbf{M}amba-Enhanced network for real-time Monocular 3D object
detection. This well-designed architecture primarily includes the following two
core modules: Focused Multi-Scale Fusion (FMF) Module, which focuses on
effectively preserving and fusing image information from different scales with
lower computational resource consumption. By precisely regulating the
information flow, the FMF module enhances the model adaptability and robustness
to scale variations while maintaining image details. Depth-Aware Feature
Enhancement Mamba (DMB) Module: It utilizes the fused features from image
characteristics as input and employs a novel adaptive strategy to globally
integrate depth information and visual information. This depth fusion strategy
not only improves the accuracy of depth estimation but also enhances the model
performance under different viewing angles and environmental conditions.
Moreover, the modular design of MonoMM provides high flexibility and
scalability, facilitating adjustments and optimizations according to specific
application needs. Extensive experiments conducted on the KITTI dataset show
that our method outperforms previous monocular methods and achieves real-time
detection.
