Manuscript received September 1, 2025; revised September 11, 2025; accepted November 17, 2025; published June 12, 2026.

Abstract—Accurate estimation of food volume is essential for personalised nutrition and health initiatives. However, the challenge of obtaining high-quality 3D reconstructions of food items, especially in the presence of occlusions or limited observations, remains considerable. Our prior framework, VolE, provided a robust foundation for mobile-driven 3D food reconstruction but encountered difficulties with entirely unseen or incomplete object parts, thereby limiting model accuracy and volume estimates. We present VolE-Complete, an advanced framework that incorporates a cutting-edge point cloud completion technique for 3D reconstructionand volume estimation of food. By effectively inferring and reconstructing missing geometric details, VolE-Complete yields more comprehensive and precise 3D representations of food.  valuations carried out on the challenging FoodKit and MetaFood3D datasets demonstrate a Mean Absolute Percentage Error (MAPE) of 0.2% and substantially improved reconstruction quality. This development facilitates dependable, mobile-based, and depth-free food volume estimation, thereby enhancing dietary assessments and enabling broader applications. The source code is available at:https://github.com/GCVCG/VolE-Complete.