Depth data is a crucial source of information for applications like autonomous vehicles and 3D cinema. However, depth maps often contain holes or low-confidence regions, which must be addressed to ensure optimal performance in tasks like object avoidance and path planning. This paper proposes a convolutional neural network (CNN) to replace the feature extraction stage of a hybrid depth completion model. The hybrid model integrates convolutional stages with an infinity Laplacian-based interpolator to propagate sparse depth data into missing regions. The proposed CNN reduces processing time by a factor of three while maintaining comparable accuracy in depth map quality. This research is motivated by the need to improve depth completion systems for autonomous vehicles in the context of heavy machinery operating in complex industrial environments. Aligned with the principles of Industry 5.0, the solution focuses on enhancing efficiency and reliability while considering the integration of autonomous systems with human-centric industrial ecosystems. Preliminary results demonstrate the CNN's effectiveness in depth map completion, emphasizing its potential for improving computational efficiency in real-world applications.