从少量2D照片精准估算珊瑚体积与表面积，无需3D建模。
No Mesh, No Problem: Estimating Coral Volume and Surface from Sparse Multi-View Images
- 用多视角图像生成点云，结合置信度加权融合
- 双分支网络同时输出体积、表面积及置信度
- 适用于未见过的珊瑚形态，适合生态监测应用
有效的珊瑚礁监测需要通过精确的体积和表面积估计来量化珊瑚生长，但珊瑚复杂的形态结构带来了挑战。本文提出一种轻量、可扩展的学习框架，仅需2D多视角RGB图像即可预测珊瑚类物体的3D体积与表面面积。方法利用预训练模块（VGGT）从各视角提取密集点图，合并为统一点云，并融入每视角置信度分数；该点云输入两个并行的DGCNN解码头，联合输出珊瑚体积、表面积及其置信度估计。为提升预测稳定性并提供不确定性估计，引入基于真实域与对数域高斯负对数似然的复合损失函数。实验表明，该方法具备良好精度与泛化能力，可直接从稀疏图像集高效估算珊瑚几何特征，为珊瑚生长分析与珊瑚礁监测提供新路径。
原文摘要 · Abstract (English)
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.
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