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GauStudio: A Modular Framework for 3D Gaussian Splatting and Beyond

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arxiv 2403.19632 v1 pith:PLHY7O55 submitted 2024-03-28 cs.CV

Chongjie Ye , Yinyu Nie , Jiahao Chang , Yuantao Chen , Yihao Zhi , Xiaoguang Han This is my paper
classification cs.CV
keywords frameworkgaussiannovelgaustudioreconstructionrepresentationsplattingapproach
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We present GauStudio, a novel modular framework for modeling 3D Gaussian Splatting (3DGS) to provide standardized, plug-and-play components for users to easily customize and implement a 3DGS pipeline. Supported by our framework, we propose a hybrid Gaussian representation with foreground and skyball background models. Experiments demonstrate this representation reduces artifacts in unbounded outdoor scenes and improves novel view synthesis. Finally, we propose Gaussian Splatting Surface Reconstruction (GauS), a novel render-then-fuse approach for high-fidelity mesh reconstruction from 3DGS inputs without fine-tuning. Overall, our GauStudio framework, hybrid representation, and GauS approach enhance 3DGS modeling and rendering capabilities, enabling higher-quality novel view synthesis and surface reconstruction.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. RAGA: Real Time Ray Traced Gaussian Shadow Casting for 3DGS Avatar-Scene Interaction

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    Introduces ray-traced Gaussian shadow casting via exact opacity line integrals for 3DGS avatars, plus a proxy for temporal stability, achieving ~50 FPS with improved realism.

  2. InverseDraping: Recovering Sewing Patterns from 3D Garment Surfaces via BoxMesh Bridging

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    A two-stage autoregressive framework centered on BoxMesh recovers parametric sewing patterns from 3D garment surfaces, claiming state-of-the-art results on benchmarks and generalization to real scans and single-view images.

  3. Depth Anything V2

    cs.CV 2024-06 unverdicted novelty 6.0 of 10

    Depth Anything V2 delivers finer, more robust monocular depth predictions by replacing real labeled images with synthetic data, scaling the teacher model, and using large-scale pseudo-labeled real images for student training.

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