Camera-LiDAR fusion models significantly enhance perception performance in autonomous driving.

We thank the reviewers for taking the time to carefully read the paper and their constructive comments. We think this might be feasible. T o Reviewer#1 Thank you for your detailed comments. Please also see the revision plan to Reviewer#2. We admit that the claimed "redundant parameters" problem of TMM is a bit artificial and NAG, TMM and G-TM (optimal tuning), and provide the guarantee of TMM (Eq.(11) in [7]) in Section 3.1; (ii) we will About the flawed guarantee, thanks for pointing out the intermediate inequality.

We thank the reviewers for taking the time to carefully read the paper and their constructive comments. We think this might be feasible. T o Reviewer#1 Thank you for your detailed comments. Please also see the revision plan to Reviewer#2. We admit that the claimed "redundant parameters" problem of TMM is a bit artificial and NAG, TMM and G-TM (optimal tuning), and provide the guarantee of TMM (Eq.(11) in [7]) in Section 3.1; (ii) we will About the flawed guarantee, thanks for pointing out the intermediate inequality.

Camera-LiDAR fusion models significantly enhance perception performance in autonomous driving. Moreover, in practice, camera-LiDAR fusion models utilize pre-trained backbones for efficient training. However, we argue that directly loading single-modal pre-trained camera and LiDAR backbones into camera-LiDAR fusion models introduces similar feature redundancy across modalities due to the nature of the fusion mechanism. Unfortunately, existing pruning methods are developed explicitly for single-modal models, and thus, they struggle to effectively identify these specific redundant parameters in camera-LiDAR fusion models. In this paper, to address the issue above on camera-LiDAR fusion models, we propose a novelty pruning framework Alternative Modality Masking Pruning (AlterMOMA), which employs alternative masking on each modality and identifies the redundant parameters.