Improving Gaussian Splatting Performance without Reliance on Structure-from-Motion Initialization

Core Concepts

Combining improved random initialization and structure guidance from pre-trained NeRF models can match or exceed the quality of Gaussian Splatting models initialized with COLMAP point clouds, without requiring the computationally expensive SFM solution.

Abstract

The paper investigates various initialization strategies for Gaussian Splatting, a high-quality and efficient scene reconstruction and novel view synthesis method, in order to overcome its reliance on computationally expensive Structure-from-Motion (SFM) algorithms for initialization.
The authors first analyze the performance of random initialization and find that using a large, constant bounding box can outperform the originally proposed random initialization strategy. They then explore leveraging pre-trained Neural Radiance Field (NeRF) models to provide a more complete and accurate initial point cloud for Gaussian Splatting, as well as using the depth predictions from NeRF to guide the training of the Gaussian Splatting model.
The experiments on the Mip-NeRF 360 and OMMO datasets show that the combination of improved random initialization and NeRF-based depth supervision can match or exceed the quality of Gaussian Splatting models initialized with COLMAP point clouds, without requiring the SFM solution. This could have significant implications for applications of Gaussian Splatting that do not require an SFM solution, such as autonomous vehicles with fused inertial/satellite navigation.
The authors also find that the required training time for the NeRF teacher model can be very low, on the order of 30 seconds, much faster than large SFM reconstructions and even the Gaussian Splatting training itself.

Stats

Gaussian Splatting models initialized with COLMAP point clouds can achieve higher PSNR than those initialized with a 3x bounding box of the camera frustum.
Initializing Gaussian Splatting with point clouds from a NeRF model trained for just 5,000 iterations (about 30 seconds) can match or exceed the quality of COLMAP initialization on the Mip-NeRF 360 and OMMO datasets.
Applying depth supervision from the pre-trained NeRF model further improves the performance of the Gaussian Splatting models.

Quotes

"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."
"We hypothesize that the limitations of Gaussian Splatting stem from its discrete and localized pruning and growing operations, which struggle to capture the coarse structure of scenes effectively."

Key Insights Distilled From

Does Gaussian Splatting need SFM Initialization?

by Yalda Forout... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12547.pdf

Deeper Inquiries

How could the proposed techniques be extended to handle dynamic scenes or scenes with significant view-dependent effects

To handle dynamic scenes or scenes with significant view-dependent effects, the proposed techniques could be extended in several ways. One approach could involve incorporating temporal information into the initialization process. By leveraging temporal coherence between frames, the initialization could adapt to dynamic changes in the scene, such as moving objects or changing lighting conditions. Additionally, techniques from video-based rendering could be integrated to predict scene dynamics and optimize the initialization for dynamic scenes. Furthermore, incorporating semantic segmentation or object detection algorithms could help identify and handle dynamic elements in the scene more effectively during the initialization phase.

What other types of scene representations or initialization strategies could be explored to further improve the performance of Gaussian Splatting without relying on SFM

Exploring alternative scene representations or initialization strategies could further enhance the performance of Gaussian Splatting without relying on SFM. One potential approach is to investigate the use of implicit neural representations, such as implicit functions or implicit neural fields, to model scene geometry. These representations offer flexibility in capturing complex shapes and structures without the need for explicit point clouds. Additionally, exploring self-supervised learning techniques or unsupervised pre-training methods could help in learning meaningful scene representations without the need for costly SFM data. Moreover, integrating domain-specific knowledge or priors into the initialization process could improve the accuracy and efficiency of Gaussian Splatting for various types of scenes.

Could the insights from this work be applied to improve the efficiency and robustness of other neural rendering techniques beyond Gaussian Splatting

The insights from this work could be applied to enhance the efficiency and robustness of other neural rendering techniques beyond Gaussian Splatting. For instance, the concept of leveraging pre-trained models or alternative initialization strategies could be beneficial for methods like Neural Radiance Fields (NeRF) or Occupancy Networks. By incorporating depth supervision or structure distillation from pre-trained models, these techniques could achieve better generalization and performance on diverse scenes. Furthermore, the idea of combining different neural rendering approaches, such as blending Gaussian Splatting with NeRF-based methods, could lead to hybrid models that leverage the strengths of each technique for improved rendering quality and speed. Overall, the insights gained from this work have the potential to advance the field of neural rendering and make it more efficient and adaptable to various scene complexities.