GaussianZoom: Progressive Zoom-in Generative 3D Gaussian Splatting with Geometric and Semantic Guidance

• URL: http://arxiv.org/abs/2605.18252v1
• Date: Mon, 18 May 2026 11:52:36 GMT
• Title: GaussianZoom: Progressive Zoom-in Generative 3D Gaussian Splatting with Geometric and Semantic Guidance
• Abstract summary: We introduce a generative zoom-in 3D reconstruction system with an iterative progressive framework to enable high-fidelity extreme zoom-in rendering from low-resolution inputs.<n>Experiments on Mip-NeRF360 and Tanks&Temples demonstrate that GaussianZoom achieves superior perceptual quality, multi-view consistency, and robustness under extreme magnification.
• Score: 55.14874423865652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, and robustness under extreme magnification, establishing a strong baseline for generative zoom-in 3D scene reconstruction.

Related papers

• High-Fidelity Surface Splatting-Based 3D Reconstruction from Multi-View Images [0.0]
  Implicit Moving Least Squares (IMLS) enables direct conversion of point clouds into signed distance and texture fields.<n>We introduce a compact kernel with local support and greater flexibility, allowing better control over frequency content.<n> Experiments show state-of-the-art performance in both surface reconstruction and rendering, yielding more accurate geometry and sharper visuals.
  arXiv  Detail & Related papers  (2026-05-08T05:23:28Z)
• ProDiG: Progressive Diffusion-Guided Gaussian Splatting for Aerial to Ground Reconstruction [24.54261347154623]
  ProDiG is a diffusion-guided framework that transforms aerial 3D representations toward ground-level fidelity.<n>It produces visually realistic ground-level renderings and coherent 3D geometry, significantly outperforming existing approaches.
  arXiv  Detail & Related papers  (2026-04-02T13:09:05Z)
• One-Shot Refiner: Boosting Feed-forward Novel View Synthesis via One-Step Diffusion [57.824020826432815]
  We present a novel framework for high-fidelity novel view synthesis (NVS) from sparse images.<n>We design a Dual-Domain Detail Perception Module, which enables handling high-resolution images without being limited by the ViT backbone.<n>We develop a feature-guided diffusion network, which can preserve high-frequency details during the restoration process.
  arXiv  Detail & Related papers  (2026-01-20T17:11:55Z)
• C3G: Learning Compact 3D Representations with 2K Gaussians [55.04010158339562]
  Recent approaches use per-pixel 3D Gaussian Splatting for reconstruction, followed by a 2D-to-3D feature lifting stage for scene understanding.<n>We propose C3G, a novel feed-forward framework that estimates compact 3D Gaussians only at essential spatial locations.
  arXiv  Detail & Related papers  (2025-12-03T17:59:05Z)
• MetroGS: Efficient and Stable Reconstruction of Geometrically Accurate High-Fidelity Large-Scale Scenes [20.601722393809244]
  We introduce MetroGS, a novel framework for efficient and robust reconstruction in complex urban environments.<n>Our method is built upon a distributed 2D Gaussian Splatting representation as the core foundation.<n>Experiments on large-scale urban datasets demonstrate that MetroGS achieves superior geometric accuracy, rendering quality.
  arXiv  Detail & Related papers  (2025-11-24T14:34:19Z)
• Gesplat: Robust Pose-Free 3D Reconstruction via Geometry-Guided Gaussian Splatting [21.952325954391508]
  We introduce Gesplat, a 3DGS-based framework that enables robust novel view synthesis and geometrically consistent reconstruction from unposed sparse images.<n>Our approach achieves more robust performance on both forward-facing and large-scale complex datasets compared to other pose-free methods.
  arXiv  Detail & Related papers  (2025-10-11T08:13:46Z)
• MuGS: Multi-Baseline Generalizable Gaussian Splatting Reconstruction [32.14335364083271]
  We present Multi-Baseline Gaussian Splatting (MuGS), a feed-forward approach for novel view synthesis.<n>MuGS effectively handles diverse baseline settings, including sparse input views with both small and large baselines.<n>We demonstrate promising zero-shot performance on the LLFF and Mip-NeRF 360 datasets.
  arXiv  Detail & Related papers  (2025-08-06T10:34:24Z)
• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices.<n>Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• MVGamba: Unify 3D Content Generation as State Space Sequence Modeling [150.80564081817786]
  We introduce MVGamba, a general and lightweight Gaussian reconstruction model featuring a multi-view Gaussian reconstructor.<n>With off-the-detail multi-view diffusion models integrated, MVGamba unifies 3D generation tasks from a single image, sparse images, or text prompts.<n>Experiments demonstrate that MVGamba outperforms state-of-the-art baselines in all 3D content generation scenarios with approximately only $0.1times$ of the model size.
  arXiv  Detail & Related papers  (2024-06-10T15:26:48Z)
• FreeSplat: Generalizable 3D Gaussian Splatting Towards Free-View Synthesis of Indoor Scenes [50.534213038479926]
  FreeSplat is capable of reconstructing geometrically consistent 3D scenes from long sequence input towards free-view synthesis. We propose a simple but effective free-view training strategy that ensures robust view synthesis across broader view range regardless of the number of views.
  arXiv  Detail & Related papers  (2024-05-28T08:40:14Z)
• GeoGS3D: Single-view 3D Reconstruction via Geometric-aware Diffusion Model and Gaussian Splatting [81.03553265684184]
  We introduce GeoGS3D, a framework for reconstructing detailed 3D objects from single-view images. We propose a novel metric, Gaussian Divergence Significance (GDS), to prune unnecessary operations during optimization. Experiments demonstrate that GeoGS3D generates images with high consistency across views and reconstructs high-quality 3D objects.
  arXiv  Detail & Related papers  (2024-03-15T12:24:36Z)
• Gaussian Splatting SLAM [16.3858380078553]
  We present the first application of 3D Gaussian Splatting in monocular SLAM. Our method runs live at 3fps, unifying the required representation for accurate tracking, mapping, and high-quality rendering. Several innovations are required to continuously reconstruct 3D scenes with high fidelity from a live camera.
  arXiv  Detail & Related papers  (2023-12-11T18:19:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.