reviews: 
Comments: This paper tackles the challenge of effectively combining information from LiDAR and camera data for 3D object detection, which is crucial for applications like autonomous driving. Traditional fusion methods often overlook depth, despite its importance in balancing the strengths of both LiDAR and RGB image data at different ranges. To address this, the authors introduce a Depth-Aware Hybrid Feature Fusion (DH-Fusion) method, which leverages depth encoding to dynamically adjust the contributions of LiDAR and camera features.

The fusion is done on two levels: globally, where the depth-aware model prioritizes different features depending on the object's distance, and locally, through a Depth-Aware Local Feature Fusion (DLF) module. The DLF module specifically enhances details in local regions by balancing original voxel data and multi-view image features, guided by depth encoding. This approach not only boosts detection accuracy but also improves robustness across varying conditions, as demonstrated in experiments on challenging datasets.
Strengths: 1. Examining image and point cloud information variability across different depth layers offers valuable insights and may inspire future research. To this end, the paper introduces a novel approach to 3D object detection by incorporating depth information into the fusion of LiDAR and camera features—a crucial but often overlooked factor in previous fusion methods. This depth-aware approach enables more adaptive and accurate fusion, particularly enhancing the detection of objects at varying distances.  

2. The study of robustness to data corruption is particularly compelling. The proposed DH-Fusion method is rigorously evaluated against various types of corruption, demonstrating improved resilience compared to previous models. This robustness makes the model more suitable for real-world applications, where data quality often fluctuates due to environmental factors like weather conditions and sensor noise.
Weaknesses: 1. The novelty in the fusion strategy is marginal.   The proposed Depth-Aware Hybrid Feature Fusion (DH-Fusion) strategy, while unique in its dual-level (global and local) application, still follows the intermediate fusion trend that has been widely adopted. Intermediate fusion methods, which integrate LiDAR and image data at the feature level, have become mainstream. The specific structure of DH-Fusion, including using transformer-based encoders for feature interaction, is similar to other recent methods, such as BEVFusion and TransFusion, which also use transformers or other deep networks to perform feature alignment and fusion. The learned nature of these works indicates that the optimal fusion strategy is learned from data. That means that they can find a fusion mechanism that treats of variability of point clouds and image information across different depth layers if that information is critical. Thus it is unclear how much the explicit introduction of depth information can boost learning particularly when there is sufficient data.  

2. The performance improvement is modest. As shown in Table 4, the gains range from only 1 to 2 percent, indicating that depth guidance may not be a decisive factor in enhancing this fusion approach. Interestingly, the improvements in corrupted data (Table 3) are more pronounced. However, the paper does not thoroughly explore why the introduced fusion strategy is particularly effective in this context.

3. Limited exploration of depth information usage. While the paper applies depth encoding to adjust feature weights, its approach is straightforward, relying on sine and cosine functions for positional representation. Other potential methods for encoding depth, such as adaptive or learning-based encoding techniques, are not investigated. Additionally, to better understand the fusion mechanism, it would be helpful to visualize the fusion weights through case studies, demonstrating how image and LiDAR features are combined. Such visualizations could provide stronger support for the motivation behind the proposed approach.
Recommendation: 5
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3