## 2 Related Works
In addition to the above, transformer-based multi-modal fusion and attention-based camera-LiDAR fusion architectures have recently emerged as powerful tools for camera-LiDAR fusion. BAFusion [10] introduces a bidirectional attention fusion module for 3D object detection in autonomous driving. It uses cross-attention and a cross-focused linear attention fusion layer to improve performance, particularly for smaller objects. FusionViT [11] introduces a vision transformer-based 3D object detection model that outperforms existing methods and image-point clouds deep fusion approaches in real-world object detection benchmark datasets. Moreover, CLFT [12], LCPR [13], and CalibFormer [14] propose a network that is a view converter-based network for semantic segmentation, location identification, and automatic LiDAR camera calibration, respectively, enhancing multimodal place recognition and robustness to viewpoint changes in autonomous vehicles by combining LiDAR point clouds with multi-view RGB images. Recent studies have introduced innovative methods to further enhance camera-LiDAR fusion for distance estimation and 3D object detection. [PERSON] et al. proposed SuperFusion, a multilevel fusion network integrating LiDAR and camera data for the creation of long-range HD maps [15]. Similarly, [PERSON] et al. introduced AYDIV, a framework that incorporates a tri-phase alignment method to improve long-distance detection [16]. AYDIV addresses challenges arising from data discrepancies between the sparse data of LiDAR and the high resolution of cameras, improving performance on datasets like Waymo Open Dataset and Argoverse2 [16]. Another notable contribution is that [PERSON] et al. proposed an approach for multi-sensor fusion, where LiDAR and camera data are integrated to enhance both object detection and distance estimation. Their method needs relatively clear object visibility, like fully visible or minimally occluded objects, to ensure accurate distance predictions [17]. In contrast, the methods proposed in our study are designed to operate effectively across varying levels of object visibility, including easy, medium, hard, and unknown cases. By utilizing the transparent sparse depth maps and object detection outputs, the proposed approaches aim to offer robust distance estimation even under partial occlusion or challenging perception conditions, addressing real-world scenarios where objects are not always fully observable. Object detection plays a critical role in multi-sensor fusion systems, especially for tasks involving depth estimation from LiDAR and camera data. The YOLO (You Only Look Once) family of models has been widely adopted due to its high detection speed and competitive accuracy. Recent versions, including YOLOv7 [18], YOLOv8 [19], YOLOv9 [20], and YOLOv10 [21], support both bounding box detection and instance segmentation, making them suitable for tasks requiring detailed spatial information. Earlier YOLO versions, such as YOLOv3 [22], YOLOv4 [23], YOLOv5 [24], and YOLOv6 [25], only support bounding box detection and do not provide native segmentation mask output. This limitation reduces their effectiveness for tasks that require pixel-level accuracy, such as extracting depth values from specific object regions in sparse depth maps through Camera-LiDAR fusion. This study chose YOLOv8 due to its balance of performance, speed, and ease of integration. YOLOv8 is lightweight and optimized for real-time applications, which aligns with the goals of this work, where fast and reliable detection is necessary for accurate depth extraction from sparse LiDAR data. It is particularly useful for methods requiring precise object localization to extract depth values from segmented regions. Building on the advancements in LiDAR-camera fusion, this research proposes three novel methods aimed at improving object distance estimation. These methods take advantage of the transparent sparse depth maps generated from LiDAR data, combined with object detection results from the camera, to refine distance estimates.