Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large Scene Reconstruction (2412.04887v1)

Abstract: 3D Gaussian Splatting has demonstrated notable success in large-scale scene reconstruction, but challenges persist due to high training memory consumption and storage overhead. Hybrid representations that integrate implicit and explicit features offer a way to mitigate these limitations. However, when applied in parallelized block-wise training, two critical issues arise since reconstruction accuracy deteriorates due to reduced data diversity when training each block independently, and parallel training restricts the number of divided blocks to the available number of GPUs. To address these issues, we propose Momentum-GS, a novel approach that leverages momentum-based self-distillation to promote consistency and accuracy across the blocks while decoupling the number of blocks from the physical GPU count. Our method maintains a teacher Gaussian decoder updated with momentum, ensuring a stable reference during training. This teacher provides each block with global guidance in a self-distillation manner, promoting spatial consistency in reconstruction. To further ensure consistency across the blocks, we incorporate block weighting, dynamically adjusting each block's weight according to its reconstruction accuracy. Extensive experiments on large-scale scenes show that our method consistently outperforms existing techniques, achieving a 12.8% improvement in LPIPS over CityGaussian with much fewer divided blocks and establishing a new state of the art. Project page: https://jixuan-fan.github.io/Momentum-GS_Page/

• The paper introduces a momentum-based teacher-student framework that decouples scene blocks from GPU limits for scalable training.
• It proposes a dynamic block weighting strategy to improve global scene consistency while reducing memory and storage demands.
• Experiments reveal superior reconstruction quality with enhanced PSNR, SSIM, and a 12.8% LPIPS gain over existing benchmarks.

Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large Scene Reconstruction

The paper "Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large Scene Reconstruction" provides an in-depth exploration into the challenges and advancements in large-scale 3D scene reconstruction using hybrid representations of Gaussian Splatting. This research introduces a novel approach—Momentum Gaussian Self-Distillation (Momentum-GS)—designed to address significant limitations associated with memory consumption and storage during the training of large scenes.

Key Contributions

The researchers present three primary contributions with their proposed method. Firstly, they introduce scene momentum self-distillation, a momentum-based teacher-student framework that decouples the number of scene blocks from the available GPU resources, enabling more scalable parallel training. Secondly, the paper proposes a reconstruction-guided block weighting strategy. This technique dynamically adjusts each block's emphasis based on its reconstruction quality, which further enhances global scene consistency by prioritizing blocks that require more attention. Lastly, the Momentum-GS method showcases superior performance compared to the state-of-the-art methods across various scalability and complexity benchmarks, indicating the profound potential of hybrid Gaussian representations in large-scale scene reconstruction.

Methodological Insights

Momentum-GS leverages both implicit and explicit features, optimizing the intricate balance between memory efficiency and reconstruction fidelity. In doing so, the model maintains a teacher Gaussian decoder, updated with momentum, to provide stable global guidance during self-distillation. This approach ensures that the reconstruction across blocks achieves higher consistency without being hindered by the limitations of physical GPU count, which is a significant bottleneck in conventional parallelized training paradigms. The momentum teacher-student framework allows for the incorporation of extensive data diversity while ensuring inferential efficiency during scene reconstruction tasks.

Experimental Evaluation and Results

The experimental results of Momentum-GS are illustrated through both qualitative and quantitative lenses across comprehensive datasets, namely Building, Rubble, Residence, Sci-Art, and MatrixCity. The analysis highlights substantial performance improvements over prior methods, with a 12.8% gain in LPIPS scores over CityGaussian benchmarks, utilizing fewer divided blocks. The method not only exhibits better visual fidelity in reconstruction but also achieves noticeable improvements in metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and LPIPS (Learned Perceptual Image Patch Similarity).

In terms of computational efficiency, Momentum-GS demonstrates superior performance, particularly in reducing VRAM consumption and storage requirements without compromising on the quality of the scene reconstruction. Such efficiency makes it particularly suitable for rendering large-scale and complex scenes, like urban environments, with applications extending to autonomous driving, surveillance, and 3D modeling.

Theoretical Implications and Future Directions

On a theoretical plane, the research advances the understanding of how hybrid representations can be effectively employed to tackle scalability issues inherent in large-scale 3D scene reconstructions. It sets a precedent for future explorations into developing more adaptive algorithms that can seamlessly integrate scene consistency with computational scalability.

Looking ahead, there is substantial potential for further development in enhancing the dynamic adaptability of scene reconstructions. Future research could explore more sophisticated block weighting schemes or explore optimizing momentum parameters that can adaptively change during training to accommodate varying scene complexities or GPU availability.

In summary, Momentum-GS represents a significant advance in the domain of 3D scene reconstruction, showcasing a balanced trade-off between performance and computational efficiency. Its innovative use of momentum-based self-distillation to manage the challenges of scene complexity provides a strong foundation for future enhancements in large-scale scene rendering applications.