FlowObject: Flow Steering for Bridging Generative Priors and Reconstruction Fidelity

2026-06-17

AI Summary

Reconstructing complete 3D objects from sparse, casually captured images remains challenging, as generative models often suffer from synthetic biases while optimization-based methods struggle to inpaint unobserved regions. This work proposes a training-free dual-space guidance mechanism that reformulates the reconstruction task as a guided inverse problem within a flow-matching framework. By steering ODE trajectories, the method efficiently leverages generative priors to complete unseen regions while strictly preserving consistency with observed data. It further refines geometry and appearance using 3D Gaussian Splatting. To our knowledge, this is the first approach within the flow-matching paradigm to simultaneously achieve high geometric completeness and strong observational fidelity, significantly outperforming both generative and optimization-based baselines on both synthetic and real-world datasets.

Abstract

Recovering complete 3D representations of objects from few casual image captures remains a significant challenge. Recent 3D generative models, particularly those based on Flow-Matching (FM), can synthesize high-quality textured assets; however, they often suffer from ''synthetic bias'' where learned priors override observational evidence, alongside a lack of alignment with the observed instance. Conversely, optimization-based methods like 3D Gaussian Splatting (3DGS) provide high fidelity on visible surfaces but fail to reason about unobserved geometry. In this paper, we present FlowObject, a framework that reformulates sparse-view 3D reconstruction as a training-free, guided inverse problem. Our approach applies a dual-space guidance strategy to steer the Ordinary Differential Equation (ODE) trajectory of a flow-matching model, enabling the completion of unseen regions through learned generative priors while enforcing strict consistency with real-world observations. By integrating a 3DGS refinement stage, FlowObject further bridges the gap between ''synthetic-looking'' generative outputs and photorealistic reconstructions. Comprehensive benchmarks on synthetic and real-world datasets demonstrate that current state-of-the-art methods often struggle to achieve geometric completeness and observational consistency simultaneously, especially under severe occlusions. In contrast, our method significantly outperforms state-of-the-art generative models and optimization-based frameworks in both geometric completeness and view-dependent appearance fidelity.

Problem

• 3D reconstruction
• generative priors
• observational consistency
• geometric completeness
• sparse-view

Innovation

• Flow-Matching
• 3D Reconstruction
• Guided Inversion
• 3D Gaussian Splatting
• Generative Priors