Comparison

Depth completion involves enhancing sparse and irregular depth data to generate denser and more uniform depth maps, benefiting downstream perception tasks. This process is particularly helpful in mitigating the uneven distribution of points in LiDAR scans, which can lead to incomplete representations of objects, especially those at a distance. By employing high-resolution images as a guide, depth completion upsamples sparse data points related to distant objects, aligning them with their closer counterparts. This fusion of LiDAR and image data through advanced algorithms results in improved depth maps, enhancing the accuracy of various applications such as autonomous driving and robotics This comparative provides the results of depth completion models on the KITTI depth completion benchmark.