## 3 Methodology
### LiDAR-Camera Processing Pipeline  
The CoRDE pipeline fuses the camera and LiDAR point clouds to generate accurate depth estimates for object detection and segmentation, as shown in Figure 0(a).  
Figure 1: _Cont._  
Initially, a transparent sparse depth map is created from the LiDAR point cloud using the provided calibration file. The YOLOv8 model is applied to the image to detect objects, outputting bounding box coordinates and segmentation masks. These outputs are then mapped onto the transparent sparse depth map. For each detected object, the pipeline extracts the pixel color values within the full bounding box and segmentation region. The minimum depth value within these regions is selected to represent the object's distance. See Figure 1a for an overview of the CoRDE pipeline.  
The CeRDE pipeline also combines camera and LiDAR point clouds to estimate object distances, as shown in Figure 1b. A transparent Sparse Depth Image is first generated using the calibration data and LiDAR point cloud. YOLOv8 provides the bounding box and the segmentation mask coordinates. For each detected object, central regions are calculated in the bounding box. Around the central point, a local area of size (\(n\times n\)) is extracted in the transparent Sparse Depth Image. The minimum value of pixels within this area is selected as the depth distance of the object. A similar process is applied to the segmentation mask. The central point of the segmentation polygon is located, and the object distance is extracted from the Transparent Sparse Depth Image. This approach focuses on the object's central region to reduce the influence of outlier pixels and noise. An illustration of the CeRDE pipeline is provided in Figure 1b.  
The GCRDE pipeline extends the CeRDE approach by applying a grid cell strategy across each object, as shown in Figure 1c. After creating the transparent Sparse Depth Image using the LiDAR point cloud and the calibration file, YOLOv8 is used for object detection. For each detected object, a grid is overlaid on the bounding box or segmentation mask. The center point of each grid cell is identified, and a (\(n\times n\)) pixel region is extracted around it. The minimum depth value from each region is collected. These minimum values are grouped by similar depth values, and the group with the highest count indicates the estimated object distance. This grid-based approach investigates multiple grid cell regions, hence it increases robustness by reducing the impact of noise and missing depth data. A demonstration of the GCRDE pipeline is provided in Figure 1c.  
Figure 1: Comparison of the three proposed pipelines for object distance estimation using camera-LiDAR fusing with Semantic 3D Depth Sensing. (**a**) CoRDE pipeline integrates LiDAR data and camera inputs to generate a transparent sparse depth map and extract the minimum pixel value from the complete bounding box or segmentation region in the sparse depth map. (**b**) CeRDE focuses on the central area of each detected object, extracting the minimum value from an (\(n\times n\)) region around the center point. (**c**) GCRDE applies a grid over the detected region and extracts min depth value from (\(n\times n\)) area centered on each grid cell. The most frequently occurring depth group among these values is used as the final estimate.  
In this work, we propose CoRDE, CeRDE, and GCRD novel pipelines that share a YOLOv8 detection method and transparent Sparse Depth Image (produced by LiDAR point cloud) but differ significantly in their depth extraction strategies, as shown in Table 1. Each pipeline introduces a novel approach for combining image features with sparse LiDAR data to enhance object-level distance estimation.