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This paper introduces YOGO, a system-level framework for 3D Gaussian Splatting (3DGS) that addresses the unpredictable resource consumption and data pollution issues hindering real-world deployment. YOGO reformulates the stochastic Gaussian growth process into a deterministic, budget-aware equilibrium using a novel budget controller and an availability-registration protocol for robust multi-sensor fusion. The authors also introduce Immersion v1.0, an ultra-dense indoor dataset designed to push the boundaries of reconstruction fidelity.
Current 3D Gaussian Splatting methods are too unpredictable for real-world use, but YOGO makes them deterministic and production-ready.
3D Gaussian Splatting (3DGS) has revolutionized neural rendering, yet existing methods remain predominantly research prototypes ill-suited for production-level deployment. We identify a critical"Industry-Academia Gap"hindering real-world application: unpredictable resource consumption from heuristic Gaussian growth, the"sparsity shield"of current benchmarks that rewards hallucination over physical fidelity, and severe multi-sensor data pollution. To bridge this gap, we propose YOGO (You Only Gaussian Once), a system-level framework that reformulates the stochastic growth process into a deterministic, budget-aware equilibrium. YOGO integrates a novel budget controller for hardware-constrained resource allocation and an availability-registration protocol for robust multi-sensor fusion. To push the boundaries of reconstruction fidelity, we introduce Immersion v1.0, the first ultra-dense indoor dataset specifically designed to break the"sparsity shield."By providing saturated viewpoint coverage, Immersion v1.0 forces algorithms to focus on extreme physical fidelity rather than viewpoint interpolation, and enables the community to focus on the upper limits of high-fidelity reconstruction. Extensive experiments demonstrate that YOGO achieves state-of-the-art visual quality while maintaining a strictly deterministic profile, establishing a new standard for production-grade 3DGS. To facilitate reproducibility, part scenes of Immersion v1.0 dataset and source code of YOGO has been publicly released. The project link is https://jjrcn.github.io/YOGO/.
