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CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for Large-Scale Scenes (2411.00771v2)

Published 1 Nov 2024 in cs.CV

Abstract: Recently, 3D Gaussian Splatting (3DGS) has revolutionized radiance field reconstruction, manifesting efficient and high-fidelity novel view synthesis. However, accurately representing surfaces, especially in large and complex scenarios, remains a significant challenge due to the unstructured nature of 3DGS. In this paper, we present CityGaussianV2, a novel approach for large-scale scene reconstruction that addresses critical challenges related to geometric accuracy and efficiency. Building on the favorable generalization capabilities of 2D Gaussian Splatting (2DGS), we address its convergence and scalability issues. Specifically, we implement a decomposed-gradient-based densification and depth regression technique to eliminate blurry artifacts and accelerate convergence. To scale up, we introduce an elongation filter that mitigates Gaussian count explosion caused by 2DGS degeneration. Furthermore, we optimize the CityGaussian pipeline for parallel training, achieving up to 10$\times$ compression, at least 25% savings in training time, and a 50% decrease in memory usage. We also established standard geometry benchmarks under large-scale scenes. Experimental results demonstrate that our method strikes a promising balance between visual quality, geometric accuracy, as well as storage and training costs. The project page is available at https://dekuliutesla.github.io/CityGaussianV2/.

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Summary

  • The paper introduces a novel decomposed-gradient-based densification technique that accelerates convergence and enhances geometric fidelity.
  • It implements an elongation filter to control Gaussian explosion during parallel tuning and avoid excessive computational overhead.
  • The parallel training pipeline reduces training time by at least 25% and memory usage by 50%, achieving state-of-the-art reconstruction accuracy.

Evaluating CityGaussianV2: Advancements in Large-Scale Scene Reconstruction

The paper "CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for Large-Scale Scenes" presents a novel approach to addressing the challenges inherent in large-scale scene reconstruction, particularly focusing on improving geometric accuracy and efficiency. This research provides compelling insights into overcoming the limitations of existing methods like 3D Gaussian Splatting (3DGS) and 2D Gaussian Splatting (2DGS), which have been prominent in the field due to their convergence rates and rendering efficiencies.

Core Contributions and Methodology

CityGaussianV2 introduces a refined pipeline for large-scale scene reconstruction, leveraging the strengths of 2DGS while addressing its scalability and convergence issues. The methodology centers around several key innovations:

  1. Decomposed-Gradient-Based Densification (DGD): This technique accelerates convergence and enhances geometric fidelity by prioritizing gradients from SSIM loss, effectively reducing blurry surfels that can degrade both rendering and geometric outputs. The paper demonstrates that assigning higher importance to SSIM gradients enables faster convergence and higher-quality reconstructions compared to relying on gradients obtained from L1 RGB loss.
  2. Elongation Filter: This approach mitigates the Gaussian count explosion observed during the parallel tuning phase, a common issue in 2DGS when handling elongated Gaussians. By filtering these Gaussians, the method avoids exponential growth in computational requirements, thus maintaining manageable resource usage even when scaling up.
  3. Parallel Training Pipeline: By optimizing the training pipeline through parallelization and modifying spherical harmonics, CityGaussianV2 achieves substantial reductions in training time (by at least 25%) and memory consumption (by 50%), while simultaneously benefiting from improved geometric quality. Notably, this pipeline omits the time-consuming steps of pruning and distillation, prevalent in previous methods like CityGaussian, by integrating spherical harmonics of degree two from scratch.
  4. Evaluation Protocol: Addressing previous benchmarks' shortcomings, CityGaussianV2 proposes a standardized evaluation protocol for unbounded scenes, incorporating visibility-based crop volume estimation to ensure stable and objective metric assessment for geometry accuracy.

Numerical Results and Implications

The experimental results reveal that CityGaussianV2 not only enhances visual quality metrics such as PSNR, SSIM, and LPIPS but also achieves state-of-the-art performance in geometric accuracy across various large-scale datasets. For instance, in the challenging scenes from GauU-Scene and MatrixCity datasets, the method outperforms both geometry-specialized techniques and large-scale reconstruction methods, demonstrating superior balance between storage efficiency and geometric fidelity.

These advancements hold significant implications for practical applications, such as urban planning, virtual reality, and autonomous navigation, where accurate and efficient scene reconstructions enable better decision-making and user experiences. Furthermore, the framework's scalability and the ability to perform well on low-end devices mark a noteworthy step towards democratizing access to high-fidelity 3D reconstructions.

Future Directions and Potential Developments

CityGaussianV2 sets a new benchmark in large-scale scene reconstruction, yet it also opens avenues for further exploration. Future research could delve into refining rasterizers to enhance rendering speed, potentially integrating level-of-detail (LoD) techniques to optimize computational resources further. Additionally, improving mesh extraction techniques to balance the quality and completeness of thin structures would enhance CityGaussianV2's applicability across a broader range of use cases.

In conclusion, CityGaussianV2 represents a substantial advancement in the field of large-scale scene reconstruction, offering a robust framework that prioritizes efficiency without compromising geometric accuracy. This work not only addresses existing challenges with innovative solutions but also lays the groundwork for future developments in 3D scene reconstruction technologies.

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