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Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images

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arxiv 2311.13398 v3 pith:7TBQZNNF submitted 2023-11-22 cs.CV cs.GR

Jaeyoung Chung , Jeongtaek Oh , Kyoung Mu Lee This is my paper
classification cs.CVcs.GR
keywords imagesdepthgaussianmethodsplattinggeometrynumberoptimization
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we present a method to optimize Gaussian splatting with a limited number of images while avoiding overfitting. Representing a 3D scene by combining numerous Gaussian splats has yielded outstanding visual quality. However, it tends to overfit the training views when only a small number of images are available. To address this issue, we introduce a dense depth map as a geometry guide to mitigate overfitting. We obtained the depth map using a pre-trained monocular depth estimation model and aligning the scale and offset using sparse COLMAP feature points. The adjusted depth aids in the color-based optimization of 3D Gaussian splatting, mitigating floating artifacts, and ensuring adherence to geometric constraints. We verify the proposed method on the NeRF-LLFF dataset with varying numbers of few images. Our approach demonstrates robust geometry compared to the original method that relies solely on images. Project page: robot0321.github.io/DepthRegGS

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Forward citations

Cited by 4 Pith papers

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

  1. 4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A diffusion model trained on 60,000 fitted 4D Gaussian Splatting human clips generates text-prompted, view-consistent dynamic humans directly in 4D, over 10x faster than video-first pipelines.

  2. You Only Gaussian Once: Controllable 3D Gaussian Splatting for Ultra-Densely Sampled Scenes

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    YOGO reformulates stochastic 3D Gaussian Splatting into a deterministic budget-aware system and supplies an ultra-dense dataset to enforce physical fidelity over viewpoint interpolation.

  3. VEIGAR: View-consistent Explicit Inpainting and Geometry Alignment for 3D object Removal

    cs.GR 2025-06 conditional novelty 5.0 of 10

    VEIGAR is a pipeline for 3D object removal in Gaussian Splatting that uses deep stereo depth projection and a scale-invariant depth loss to achieve faster training and comparable quality to prior state-of-the-art.

  4. Depth Estimators Are Implicit Neural Fields for 3D Scene Geometry Inpainting and Reconstruction

    cs.CV 2026-07 conditional novelty 4.0 of 10

    NDF treats a fixed-image depth estimator as an implicit field and optimizes it on observed depth at test time, improving inpainting accuracy and cross-view consistency.

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