No Mesh, No Problem: Estimating Coral Volume and Surface from Sparse Multi-View Images

2025-09-14

AI Summary

This study addresses the challenge of accurately estimating coral 3D geometric parameters—volume and surface area—from sparse multi-view RGB images. We propose a lightweight, mesh-free learning framework that extracts dense features via a pretrained VGG backbone and constructs a unified point cloud. A view-confidence weighting mechanism and a dual-domain loss—comprising geometric regression and Gaussian negative log-likelihood-based uncertainty modeling—are introduced. Parallel DGCNN decoders jointly predict both geometric parameters and their predictive uncertainties. Our key contribution is an end-to-end, reconstruction-free regression of ecologically critical metrics, significantly enhancing generalization to unseen coral morphologies and robustness under sparse-view conditions. Experiments demonstrate state-of-the-art performance in accuracy, prediction stability, and computational efficiency. The method provides a deployable, image-driven solution for large-scale, low-cost coral reef monitoring.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Estimating coral volume and surface area from sparse multi-view images
• Quantifying coral growth via accurate volumetric and surface area estimates
• Predicting 3D coral geometry from 2D RGB images without mesh reconstruction

Innovation

• Uses multi-view RGB images for 3D estimation
• Employs VGGT and DGCNN for point processing
• Applies Gaussian loss for uncertainty quantification