Effective reef monitoring requires the quantification of coral growth via accurate volumetric and surface area estimates, which is a challenging task due to the complex morphology of corals. We propose a novel, lightweight, and scalable learning framework that addresses this challenge by predicting the 3D volume and surface area of coral-like objects from 2D multi-view RGB images. Our approach utilizes a pre-trained module (VGGT) to extract dense point maps from each view; these maps are merged into a unified point cloud and enriched with per-view confidence scores. The resulting cloud is fed to two parallel DGCNN decoder heads, which jointly output the volume and the surface area of the coral, as well as their corresponding confidence estimate. To enhance prediction stability and provide uncertainty estimates, we introduce a composite loss function based on Gaussian negative log-likelihood in both real and log domains. Our method achieves competitive accuracy and generalizes well to unseen morphologies. This framework paves the way for efficient and scalable coral geometry estimation directly from a sparse set of images, with potential applications in coral growth analysis and reef monitoring.

翻译：有效的珊瑚礁监测需要通过精确的体积与表面积估算来量化珊瑚生长，而珊瑚复杂的形态结构使得这一任务极具挑战性。本文提出一种新颖、轻量且可扩展的学习框架，通过从二维多视角RGB图像预测类珊瑚物体的三维体积与表面积来应对这一挑战。我们的方法利用预训练模块（VGGT）从每个视角提取稠密点图；这些点图被融合为统一的点云，并通过每视角置信度得分进行增强。生成的点云被输入两个并行的DGCNN解码器头部，其联合输出珊瑚的体积与表面积，以及相应的置信度估计。为提升预测稳定性并提供不确定性估计，我们在实数域与对数域中引入了基于高斯负对数似然的复合损失函数。本方法达到了具有竞争力的精度，并能良好泛化至未见过的形态。该框架为直接从稀疏图像集高效、可扩展地估算珊瑚几何特征开辟了道路，在珊瑚生长分析与礁体监测中具有潜在应用价值。