  Camera and LiDAR serve as informative sensors for accurate and robust
autonomous driving systems. However, these sensors often exhibit heterogeneous
natures, resulting in distributional modality gaps that present significant
challenges for fusion. To address this, a robust fusion technique is crucial,
particularly for enhancing 3D object detection. In this paper, we introduce a
dynamic adjustment technology aimed at aligning modal distributions and
learning effective modality representations to enhance the fusion process.
Specifically, we propose a triphase domain aligning module. This module adjusts
the feature distributions from both the camera and LiDAR, bringing them closer
to the ground truth domain and minimizing differences. Additionally, we explore
improved representation acquisition methods for dynamic fusion, which includes
modal interaction and specialty enhancement. Finally, an adaptive learning
technique that merges the semantics and geometry information for dynamical
instance optimization. Extensive experiments in the nuScenes dataset present
competitive performance with state-of-the-art approaches. Our code will be
released in the future.
