Lightweight Neural Framework for Robust 3D Volume and Surface Estimation from Multi-View Images

Accurate estimation of object volume and surface area from visual data is an open challenge with broad implications across various domains. We propose a unified framework that predicts volumetric and surface metrics directly from a set of 2D multi-view images. Our approach first generates a point cloud from the captured multi-view images using recent 3D reconstruction techniques, while a parallel 2D encoder aggregates view-aligned features. A fusion module then aligns and merges 3D geometry with 2D visual embeddings, followed by a graph-based decoder that regresses volume, surface area, and their corresponding uncertainties. This proposed architecture maintains robustness against sparse or noisy data. We evaluate the framework across multiple application domains: corals, where precise geometric measurements support growth monitoring; food items, where volume prediction relates to dietary tracking and portion analysis; and human bodies, where volumetric cues are crucial for anthropometric and medical applications. Experimental results demonstrate the reliable performance of our framework across diverse scenarios, highlighting its versatility and adaptability. Furthermore, by coupling 3D reconstruction with neural regression and 2D features, our model provides a scalable and fast solution for quantitative shape analysis from visual data.