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BlockGaussian: Efficient Large-Scale Scene Novel View Synthesis via Adaptive Block-Based Gaussian Splatting (2504.09048v2)

Published 12 Apr 2025 in cs.CV

Abstract: The recent advancements in 3D Gaussian Splatting (3DGS) have demonstrated remarkable potential in novel view synthesis tasks. The divide-and-conquer paradigm has enabled large-scale scene reconstruction, but significant challenges remain in scene partitioning, optimization, and merging processes. This paper introduces BlockGaussian, a novel framework incorporating a content-aware scene partition strategy and visibility-aware block optimization to achieve efficient and high-quality large-scale scene reconstruction. Specifically, our approach considers the content-complexity variation across different regions and balances computational load during scene partitioning, enabling efficient scene reconstruction. To tackle the supervision mismatch issue during independent block optimization, we introduce auxiliary points during individual block optimization to align the ground-truth supervision, which enhances the reconstruction quality. Furthermore, we propose a pseudo-view geometry constraint that effectively mitigates rendering degradation caused by airspace floaters during block merging. Extensive experiments on large-scale scenes demonstrate that our approach achieves state-of-the-art performance in both reconstruction efficiency and rendering quality, with a 5x speedup in optimization and an average PSNR improvement of 1.21 dB on multiple benchmarks. Notably, BlockGaussian significantly reduces computational requirements, enabling large-scale scene reconstruction on a single 24GB VRAM device. The project page is available at https://github.com/SunshineWYC/BlockGaussian

Summary

  • The paper introduces BlockGaussian, a novel adaptive block-based method for efficient large-scale scene novel view synthesis that improves computational efficiency and rendering quality.
  • BlockGaussian uses content-aware scene partitioning, visibility-aware block optimization with auxiliary Gaussian points, and a pseudo-view geometry constraint to enhance reconstruction accuracy and reduce artifacts.
  • Evaluations show BlockGaussian achieves a 5× speedup in optimization and a 1.21 dB PSNR improvement on large scenes, requiring only a single 24GB VRAM GPU.

Overview of "BlockGaussian: Efficient Large-Scale Scene Novel View Synthesis via Adaptive Block-Based Gaussian Splatting"

The paper introduces a novel approach named BlockGaussian, aimed at enhancing efficiency in large-scale scene novel view synthesis. Recognizing the limitations of existing scene reconstruction methods, the authors propose a framework that leverages adaptive block-based Gaussian splatting to improve computational efficiency and rendering quality. This work addresses key challenges such as content-aware scene partitioning, visibility-aware block optimization, and artifacts management during block merging.

Methodology Highlights

BlockGaussian employs a divide-and-conquer approach for scene reconstruction, which is broadly divided into the following components:

  1. Content-Aware Scene Partitioning:
    • The proposed method integrates a scene partition strategy reliant on sparse point cloud data from a preliminary Structure from Motion (SfM) process.
    • By considering the content complexity across different scene areas, this approach ensures balanced computational load across blocks, facilitating efficient parallel optimization.
  2. Visibility-Aware Block Optimization:
    • To address supervision mismatch occurring during block-wise optimization, auxiliary Gaussian points are introduced to represent occluded regions of the scene relative to a given view.
    • This technique refines the reconstructed scene quality by ensuring cohesive alignment with the ground truth images.
  3. Pseudo-View Geometry Constraint:
    • A pseudo-view geometry constraint is proposed to minimize rendering degradation caused by artifacts, such as floaters in airspace.
    • This constraint supervises the scene from artificial viewpoints generated through perturbations of training camera poses, effectively enhancing post-block-merge rendering quality.

Numerical and Qualitative Results

The methodology yields notable improvements reflected in both reconstruction efficiency and rendering quality. Demonstrative experiments on large-scale scenes indicate:

  • A significant speedup in optimization by a factor of 5× compared to conventional methods.
  • An average PSNR improvement of 1.21 dB across standard benchmarks.
  • Feasible computational requirements, making it suitable for execution on a single 24GB VRAM GPU, thus democratizing large-scale scene rendering capabilities.

Implications and Future Prospects

Practical Implications:

BlockGaussian's enhancements in scene partitioning and view synthesis hold potential for real-time applications in areas such as autonomous driving, virtual reality, and remote sensing. The method's reduced computational demand and superior image fidelity could enhance user experiences in these domains, offering quick, high-quality environmental reconstructions that are critical for tasks such as navigation and mapping.

Theoretical Implications:

From a theoretical standpoint, this work contributes to advancing photometric rendering through adaptive methods tailored for complex large-scale scenes. The insights into visibility handling and scene partitioning could inspire further innovations in computer graphics and vision, particularly in methods tackling high-dimensional scene representations.

Speculation on AI Developments:

Future research could explore the integration of BlockGaussian with generative models or reinforcement learning techniques to further refine scene synthesis and adaptability. Additionally, synergizing this approach with real-time neural rendering pipelines might lead to breakthroughs in immersive, interactive environments, expanding applications in entertainment, training simulations, and beyond.

In summary, BlockGaussian presents an effective framework for efficient scene reconstruction, offering advancements in both accuracy and processing efficiency. Its potential impact cuts across several high-demand application areas, establishing a foundation for future enhancements in the growing field of large-scale scene synthesis.

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