RDINet: A Deep Learning Model Integrating RGB-D and Ingredient Features for Food Nutrition Estimation

With growing public health awareness, accurate food nutrition estimation plays an increasingly important role in dietary management and disease prevention. The main bottleneck lies in how to effectively integrate multi-source heterogeneous information. We propose RDINet, a multimodal network that fuses RGB appearance, depth geometry, and ingredient semantics for food nutrition estimation. It comprises two core modules: The RGB-D fusion module integrates the textural appearance of RGB images and the 3D shape information conveyed by depth images through a channel–spatial attention mechanism, achieving a joint understanding of food appearance and geometric morphology without explicit 3D reconstruction; the ingredient fusion module embeds ingredient information into visual features via attention mechanisms, enabling the model to fully leverage components that are visually difficult to discern or prone to confusion, thereby activating corresponding nutritional reasoning pathways and achieving cross-modal inference from explicit observations to latent attributes. Experimental results on the Nutrition5k dataset show that RDINet achieves percentage mean absolute errors (PMAE) of 14.9%, 11.2%, 19.7%, 18.9%, and 19.5% for estimating calories, mass, fat, carbohydrates, and protein, respectively, with a mean PMAE of 16.8% across all metrics, outperforming existing mainstream methods. The results demonstrate that the appearance–geometry–semantics fusion framework is effective.