This repository implements a PyTorch-based neural network for self-supervised depth completion, trained on the KITTI dataset. The model takes sparse LiDAR depth measurements and RGB camera images as input and predicts full dense depth maps without requiring ground-truth annotations. It uses a self-supervised training approach based on view synthesis, where photometric consistency across frames serves as the supervision signal.