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Evaluating Alternatives to SFM Point Cloud Initialization for Gaussian Splatting (2404.12547v3)

Published 18 Apr 2024 in cs.CV

Abstract: 3D Gaussian Splatting has recently been embraced as a versatile and effective method for scene reconstruction and novel view synthesis, owing to its high-quality results and compatibility with hardware rasterization. Despite its advantages, Gaussian Splatting's reliance on high-quality point cloud initialization by Structure-from-Motion (SFM) algorithms is a significant limitation to be overcome. To this end, we investigate various initialization strategies for Gaussian Splatting and delve into how volumetric reconstructions from Neural Radiance Fields (NeRF) can be utilized to bypass the dependency on SFM data. Our findings demonstrate that random initialization can perform much better if carefully designed and that by employing a combination of improved initialization strategies and structure distillation from low-cost NeRF models, it is possible to achieve equivalent results, or at times even superior, to those obtained from SFM initialization. Source code is available at https://theialab.github.io/nerf-3dgs .

Citations (1)

Summary

  • The paper demonstrates that NeRF-derived initialization achieves comparable or superior reconstruction quality to SFM methods.
  • The study reveals that NeRF models can be trained in about 30 seconds, significantly reducing computational overhead.
  • The inclusion of depth supervision refines geometric accuracy, enhancing performance in complex 3D scene rendering.

Enhancing Gaussian Splatting with NeRF-based Initialization and Depth Supervision

Introduction and Background

Gaussian Splatting has become an esteemed rendering approach, beneficial for tasks like scene reconstruction and novel view synthesis. Typically, it relies on effective initialization from existing models such as Structure-from-Motion (SFM) for accurate scene capture. However, given SFM's computational intensity and speed limitations, there is considerable merit in exploring alternative initialization strategies to improve Gaussian Splatting's efficiency and applicability.

Research into the application of Neural Radiance Fields (NeRF) presents a promising direction. NeRF's efficacy in 3D scene representation, despite its computational demands, makes the method an attractive pre-initialization candidate for Gaussian Splatting. This paper investigates the possibility of employing low-cost, efficiently trained NeRF models to generate initial point clouds, thus potentially bypassing the need for traditional SFM data.

Methodological Insights

The paper is structured around evaluating methods that condense the reliance on SFM by introducing alternatives using NeRF:

  1. NeRF-based Point Cloud Initialization: The researchers demonstrated that initializing Gaussian Splatting using point clouds derived from NeRF models can lead to comparable or superior reconstruction results relative to those using SFM initialization. The low training requirements (approximately 30 seconds) for the NeRF model underscore potential efficiency gains.
  2. Improved Random Initialization: The paper also explored random initialization enhancements. By deploying a uniformly distributed initial set of points encompassing the entire scene, the model could achieve stable outcomes without prior knowledge of the scene geometry.
  3. Depth Supervision via NeRF: An additional layer of depth supervision was derived from short-duration trained NeRF models. The proposed method utilizes a loss function blending Gaussian Splatting and depth estimation discrepancies. This method not only secures the positional accuracy of points but also encourages finer geometric detail in reconstructions.

Experimental Analysis

The experiments conducted provide a quantitative assessment of each proposed method's effectiveness, utilizing large-scale scenes from the Mip-NeRF 360 and OMMO datasets. The results were favorable:

  • Quality Assessment: With both NeRF-based initialization and depth supervision, Gaussian Splatting often matched or surpassed the quality metrics of SFM-based methods. This was especially notable in complex scenes where traditional methods struggled due to sparse or incomplete data.
  • Efficiency Gains: The streamlined training for NeRF models yielded initialization data within seconds, significantly faster than typical SFM processes. This efficiency makes the proposed method appealing for real-time applications.

Future Directions and Implications

This paper's findings open several avenues for future research and application. For instance, leveraging NeRF’s ability to handle dynamic scenes could revolutionize real-time applications in autonomous vehicle navigation and augmented reality, where rapid scene understanding is crucial. The combination of initialization strategies and depth supervision could further refine the integration of radiance fields with Gaussian splatting, promising even swifter and more precise reconstructions.

In conclusion, the paper presents a compelling case for the viability of NeRF as both an initializer and a depth supervisor for Gaussian Splatting. This approach not only challenges the traditional reliance on SFM but also extends the practical utility of Gaussian Splatting across various real-world scenarios.