## 2 Related Works  
Multi-sensor fusion has been a popular research area, especially in the field of autonomous vehicles, where accurate environmental perception is critical. Combining LiDAR and camera data has been shown to improve object detection, tracking, and distance estimation compared to using a single sensor. Several existing approaches aim to fuse LiDAR and camera data at different levels, including early fusion, late fusion, and deep fusion techniques. In early fusion, sensor data are combined at an early stage of processing, allowing the perception system to utilize raw data from multiple sensors. One common approach is to project LiDAR points onto the camera image plane, aligning the data in 2D space using calibration parameters. [PERSON] et al. employed this technique for depth estimation in urban environments [1]. [PERSON] et al. proposed an early fusion technique where RGB images and depth information from LiDAR are fused to enhance object detection [2]. The fused data are then passed through a neural network for classification. However, early fusion methods may struggle to handle differences in sensor resolution and noise from each modality, limiting their effectiveness in certain scenarios. Late fusion involves fusing the sensor data after independent feature extraction from each modality. [PERSON] et al. introduced a 3D object detection system where features from LiDAR point clouds and camera images are extracted separately using convolutional neural networks (CNNs) before being combined for final object detection [3]. Late fusion methods offer flexibility in handling sensor-specific noise and characteristics, but may miss potential correlations between raw sensor data. Deep learning methods for sensor fusion have become increasingly prominent due to their ability to learn very complicated feature representations. [PERSON] et al. introduced Multi-View 3D (MV3D), a framework that processes LiDAR point clouds and RGB images to generate 3D object proposals and performs detection in a joint 2D-3D space [4]. Similarly, the PointPainting method pains LiDAR point clouds with semantic information from image segmentation, enabling the network to leverage both 2D and 3D data for improved detection [5]. However, deep fusion techniques frequently require large amounts of annotated data and considerable processing resources, making them challenging for real-time applications. In distance estimation, multiple strategies have been explored to accurately determine object distances using LiDAR and camera fusion. [PERSON] et al. demonstrated a camera-radar sensor fusion architecture for precise object detection and the estimation of distance in autonomous driving contexts [6]. Their approach utilizes radar view and RGB images, employing a middle-fusion technique to combine these data sources effectively [6]. Similarly, a study by [PERSON] et al. proposed an accurate data fusion technique between a stereolabs ZED2 camera and a LiDAR sensor to determine object distance, hence improving the vehicle's perception capabilities [7]. Sparse depth maps derived from LiDAR data are increasingly used in fusion tasks to address the low spatial resolution of LiDAR sensors. Sparse-to-dense depth estimation methods have been explored, where sparse LiDAR points are used to estimate dense depth maps corresponding to the camera's field of view. This technique allows for better handling of regions where LiDAR data may be sparse, such as at far distances or occluded areas. Work by [PERSON] et al. demonstrated an efficient depth completion approach that combines sparse depth measurements from LiDAR with RGB images using a deep neural network, improving depth map quality [8]. Additionally, the Sparse LiDAR and Stereo Fusion (SLS-Fusion) method integrates data from LiDAR and stereo cameras through a neural network for depth estimation, resulting in enhanced dense depth maps and improving 3D object detection performance [9].