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

2026-06-22

AI Summary

This work addresses the challenges of high computational cost and low accuracy in estimating three-dimensional volume and surface area from sparse, noisy multi-view images. The authors propose an end-to-end feedforward neural network that directly regresses scale-normalized geometric quantities and their associated uncertainties by fusing view-aligned 2D visual features with reconstructed 3D point clouds, thereby circumventing iterative optimization and enabling efficient inference. The core innovation lies in a lightweight graph decoder architecture that, for the first time, jointly leverages 2D appearance cues and 3D geometric information for geometric estimation. Extensive experiments demonstrate that the method significantly outperforms existing approaches across diverse applications—including coral monitoring, dietary assessment, and anthropometry—maintaining high accuracy and robustness even under sparse input views or resource-constrained conditions.

Abstract

Accurate volume and surface area estimation is critical for diverse applications, from marine ecology to medical diagnostics. However, existing methods often suffer from high computational costs and poor performance with sparse and noisy data. We propose a fully feed-forward framework that regresses scale-normalized volume and surface area and their associated uncertainties directly from multi-view images. By fusing 3D point cloud reconstructions with view-aligned 2D features through a graph-based decoder, our model bypasses iterative optimization, ensuring exceptional scalability and rapid inference. Experimental results demonstrate that our approach outperforms state-of-the-art methods, particularly when operating with a low number of input images. Validated across coral monitoring, dietary analysis, and anthropometry, our proposed framework provides a robust, adaptable solution for quantitative shape analysis. This architecture provides a high-speed, scalable alternative for precise geometric estimation from visual data, maintaining high performance even in resource-constrained or sparse-view scenarios.

Problem

• 3D volume estimation
• surface area estimation
• multi-view images
• sparse data
• computational cost

Innovation

• lightweight neural framework
• multi-view 3D estimation
• graph-based decoder
• scale-normalized regression
• uncertainty-aware prediction