CityGaussian: Efficient Large-Scale 3D Scene Reconstruction and Real-Time Rendering with Adaptive Gaussian Primitives

Core Concepts

CityGaussian employs a novel divide-and-conquer training approach and Level-of-Detail (LoD) strategy to enable efficient large-scale 3D Gaussian Splatting training and real-time rendering across vastly different scales.

Abstract

The paper introduces CityGaussian (CityGS), a method for large-scale 3D scene reconstruction and real-time rendering.
Key highlights:

CityGS uses a divide-and-conquer training approach to efficiently train large-scale 3D Gaussian Splatting (3DGS) models. It generates a coarse global Gaussian prior to guide the finetuning of individual blocks, enabling seamless fusion of the blocks.
CityGS employs an adaptive training data selection strategy to ensure sufficient training for each block, based on the projected content contribution and spatial containment.
CityGS proposes a block-wise LoD strategy to dynamically select the appropriate level of detail for different regions of the scene, significantly reducing the computation burden while maintaining rendering quality.
Extensive experiments on large-scale scenes demonstrate that CityGS achieves state-of-the-art rendering quality and enables consistent real-time performance across vastly different scales.

Stats

The paper reports the following key metrics:

On the MatrixCity dataset, CityGS achieves PSNR of 27.46, SSIM of 0.865, and LPIPS of 0.204.
On the Residence, Rubble, and Building datasets, CityGS obtains SSIM ranging from 0.778 to 0.813, PSNR ranging from 21.55 to 25.77, and LPIPS ranging from 0.211 to 0.246.
With the proposed LoD strategy, CityGS maintains real-time rendering performance (53.7 FPS on average) under drastically varying camera heights, while the minimum FPS stays above 25 FPS.

Quotes

"CityGaussian (CityGS) employs a novel divide-and-conquer training approach and Level-of-Detail (LoD) strategy for efficient large-scale 3D Gaussian Splatting training and rendering."
"Our method, termed as CityGS, performs favorably against current state-of-the-art methods in public benchmarks."

Key Insights Distilled From

CityGaussian

by Yang Liu,He ... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.01133.pdf

Deeper Inquiries

How can the CityGS framework be extended to handle dynamic scenes with moving objects

To extend the CityGS framework to handle dynamic scenes with moving objects, several modifications and additions would be necessary. One approach could involve incorporating a dynamic object detection and tracking system to identify and track moving objects within the scene. This system could then update the Gaussian representations of these objects in real-time as they move, ensuring that the scene reconstruction remains accurate and up-to-date. Additionally, the training process could be adapted to include data augmentation techniques that simulate object movement and changes in the scene over time, allowing the model to learn how to handle dynamic elements effectively. By integrating these dynamic scene handling mechanisms, CityGS could be transformed into a robust framework for reconstructing and rendering dynamic scenes with moving objects.

What are the potential limitations of the static scene assumption in CityGS, and how could the method be adapted to handle more general scene configurations

The static scene assumption in CityGS may pose limitations when dealing with scenes that are subject to frequent changes or variations. One potential limitation is the inability to accurately capture and represent dynamic elements such as moving objects, changing lighting conditions, or evolving scene configurations. To address this limitation, CityGS could be adapted to incorporate real-time updates and adjustments based on new data inputs or changes in the scene. By implementing mechanisms for dynamic scene adaptation and continuous learning, the method could better handle general scene configurations that are subject to variability and evolution. Additionally, integrating techniques for scene segmentation and object recognition could help in identifying and isolating dynamic elements within the scene, allowing for more accurate reconstruction and rendering of complex and changing environments.

Given the explicit 3D Gaussian representation in CityGS, how could the method be leveraged for interactive scene editing and manipulation tasks beyond just rendering

The explicit 3D Gaussian representation in CityGS opens up possibilities for interactive scene editing and manipulation tasks beyond rendering. By leveraging the detailed geometric and appearance information encoded in the Gaussian primitives, users could interactively edit and modify the scene in real-time. For example, users could manipulate the position, orientation, and appearance of objects within the scene by directly adjusting the corresponding Gaussian parameters. Additionally, the method could be extended to support functionalities such as object removal, addition, or replacement, enabling users to customize and modify the scene according to their preferences. Furthermore, interactive tools for scene transformation, such as scaling, rotation, and translation, could be implemented based on the Gaussian representations, allowing for intuitive and flexible scene editing capabilities. Overall, the explicit 3D Gaussian representation in CityGS provides a rich foundation for interactive scene manipulation and editing tasks, offering a versatile and powerful tool for creative exploration and customization of 3D scenes.