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Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images (2311.13398v3)

Published 22 Nov 2023 in cs.CV and cs.GR

Abstract: 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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Citations (56)
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Summary

  • The paper introduces a depth-guided optimization that integrates dense monocular depth maps with sparse SfM cues to reduce overfitting in few-shot image scenarios.
  • It employs a rasterization-based depth rendering process and a smoothness constraint to optimize both color and depth consistency.
  • Experimental results on the NeRF-LLFF dataset demonstrate enhanced PSNR, SSIM, and LPIPS scores, reducing artifacts and improving 3D reconstruction quality.

High-Resolution 3D Gaussian Splatting in Few-Shot Image Scenarios: Depth-Regularized Optimization

The paper "Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images," authored by Chung, Oh, and Lee, presents a method for optimizing 3D Gaussian splatting in scenarios where only a limited number of images are available. This research is particularly pertinent to applications necessitating quick and photorealistic 3D reconstructions, such as virtual reality and mobile graphics, where the acquisition of numerous images is impractical. The approach builds on the high-performance 3D Gaussian Splatting (3DGS) technique, addressing its notable propensity to overfit when the input data comprises a sparse number of views.

Methodology Overview

To mitigate the overfitting challenge inherent in 3DGS with limited images, the authors propose integrating a geometry-guided depth regularization methodology. They utilize a monocular depth estimation model to provide a dense depth map, which is aligned using sparse feature points derived from Structure-from-Motion (SfM) algorithms, specifically COLMAP. This adjusted depth map is critical to guiding the color-based optimization in 3D Gaussian splatting, thereby reducing artifacts and enhancing the 3D scene's geometric fidelity.

The paper details a depth-informed optimization strategy:

  1. Depth Guidance Integration: A monocular depth estimation model outputs a dense depth map, which undergoes scale adaption to align with sparse SfM-derived depths for accuracy and coherence across images.
  2. Rasterization-based Depth Rendering: The Gaussian splatting approach leverages the rasterization pipeline to render depth maps of Gaussian splats, optimizing both color and depth consistency simultaneously.
  3. Smoothness Constraint: To address inconsistency and noise, an unsupervised smoothness constraint, inspired by edge detection techniques, is implemented to ensure geometric stability.
  4. Optimization Techniques Specific to Few-Image Contexts: Customizations are made to optimization techniques, such as removing processes like opacity resetting and introducing an early-stop mechanism reliant on depth loss, optimizing for settings with limited images.

Experimental Insights

Empirical evaluations on the NeRF-LLFF dataset, a benchmark for novel view synthesis from forward-facing scenes, reveal significant benefits of the proposed method over baseline 3DGS, particularly with a reduced number of images. The results demonstrate that incorporating depth guides reduces floating artifacts and improves overall geometric alignment and visual quality. Quantitative metrics such as PSNR, SSIM, and LPIPS underscore these improvements, and qualitative examinations reveal enriched scene details and reduced artifacts in sparse view synthesis scenarios.

Implications and Future Directions

The method's capacity to significantly stabilize 3D reconstructions in few-shot settings ushers in enhancements in fields reliant on minimal input data, spelling substantial implications for real-time and resource-constrained applications. The integration of depth priors as a regularizing component points to a broader application potential in computer vision tasks where comprehensive datasets are infeasible.

Speculatively, future developments could explore refining depth estimation models to further enhance alignment with the SfM process, thus improving the primary limitation identified by the authors: the dependency on monocular depth model accuracy. Additionally, investigating more advanced fusion techniques for depth cues from different modalities could lead to further robustness and flexibility in reconstructions.

In conclusion, this work offers a robust framework intersecting between depth-informed regularization and Gaussian splatting, propelling few-shot 3D image reconstruction forward by enhancing model resilience and output fidelity, ultimately advancing the practical utility of 3DGS in constrained imaging scenarios.

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