FluidGaussian: Propagating Simulation-Based Uncertainty Toward Functionally-Intelligent 3D Reconstruction

2026-03-22

AI Summary

This work addresses the limitation of existing 3D reconstruction methods that prioritize visual fidelity while neglecting functional plausibility in physical interactions, often leading to distorted fluid simulation outcomes. To bridge this gap, we propose FluidGaussian—a plug-and-play 3D reconstruction framework that, for the first time, couples fluid–structure interaction with Gaussian splatting representations. Our approach leverages uncertainty estimates derived from fluid simulations to guide an active learning strategy, jointly optimizing both visual and physical fidelity. Evaluated on NeRF Synthetic, Mip-NeRF 360, and DrivAerNet++, FluidGaussian achieves up to an 8.6% improvement in PSNR and a 62.3% reduction in velocity divergence, significantly enhancing both geometric accuracy and physical plausibility of the reconstructed scenes.

Technology Category

• Computer Vision: 3D Computer Vision
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

Real objects that inhabit the physical world follow physical laws and thus behave plausibly during interaction with other physical objects. However, current methods that perform 3D reconstructions of real-world scenes from multi-view 2D images optimize primarily for visual fidelity, i.e., they train with photometric losses and reason about uncertainty in the image or representation space. This appearance-centric view overlooks body contacts and couplings, conflates function-critical regions (e.g., aerodynamic or hydrodynamic surfaces) with ornamentation, and reconstructs structures suboptimally, even when physical regularizers are added. All these can lead to unphysical and implausible interactions. To address this, we consider the question: How can 3D reconstruction become aware of real-world interactions and underlying object functionality, beyond visual cues? To answer this question, we propose FluidGaussian, a plug-and-play method that tightly couples geometry reconstruction with ubiquitous fluid-structure interactions to assess surface quality at high granularity. We define a simulation-based uncertainty metric induced by fluid simulations and integrate it with active learning to prioritize views that improve both visual and physical fidelity. In an empirical evaluation on NeRF Synthetic (Blender), Mip-NeRF 360, and DrivAerNet++, our FluidGaussian method yields up to +8.6% visual PSNR (Peak Signal-to-Noise Ratio) and -62.3% velocity divergence during fluid simulations. Our code is available at https://github.com/delta-lab-ai/FluidGaussian.

Problem

• 3D reconstruction
• physical plausibility
• fluid-structure interaction
• functional awareness
• simulation-based uncertainty

Innovation

• fluid-structure interaction
• simulation-based uncertainty
• functionally-intelligent reconstruction
• active learning
• 3D Gaussian splatting