GaussianZoom: Progressive Zoom-in Generative 3D Gaussian Splatting with Geometric and Semantic Guidance
Jiale Shi, Jiarui Hu, Zesong Yang, Kaixuan Luan, Hujun Bao, Zhaopeng Cui

TL;DR
GaussianZoom is a novel 3D reconstruction system enabling high-fidelity zoom-in rendering from low-resolution inputs through progressive, multi-scale, and geometry-consistent modeling techniques.
Contribution
It introduces a progressive zoom-in framework with a multi-scale semantic hierarchy and a super-resolution module for detailed 3D scene reconstruction.
Findings
Achieves superior perceptual quality and multi-view consistency.
Demonstrates robustness under extreme magnification.
Establishes a new baseline for generative zoom-in 3D reconstruction.
Abstract
We introduce GaussianZoom, a generative zoom-in 3D reconstruction system with an iterative progressive framework that combines geometry-consistent scene modeling and multi-scale semantic reasoning to enable high-fidelity extreme zoom-in rendering from low-resolution inputs. To achieve this, we develop a novel multi-view consistent super-resolution module with depth-based feature warping and VLM-driven detail synthesis, ensuring accurate multi-view correspondence while enriching fine-scale appearance beyond the observed resolution. To support zooming across large magnification ranges, we further introduce a new expandable continuous Level-of-Detail hierarchy that dynamically modulates Gaussian visibility for smooth, alias-free cross-scale rendering. Experiments on Mip-NeRF360 and Tanks\&Temples demonstrate that GaussianZoom achieves superior perceptual quality, multi-view consistency,…
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