LapisGS: Layered Progressive 3D Gaussian Splatting for Adaptive Streaming (2408.14823v2)

Abstract: The rise of Extended Reality (XR) requires efficient streaming of 3D online worlds, challenging current 3DGS representations to adapt to bandwidth-constrained environments. This paper proposes LapisGS, a layered 3DGS that supports adaptive streaming and progressive rendering. Our method constructs a layered structure for cumulative representation, incorporates dynamic opacity optimization to maintain visual fidelity, and utilizes occupancy maps to efficiently manage Gaussian splats. This proposed model offers a progressive representation supporting a continuous rendering quality adapted for bandwidth-aware streaming. Extensive experiments validate the effectiveness of our approach in balancing visual fidelity with the compactness of the model, with up to 50.71% improvement in SSIM, 286.53% improvement in LPIPS with 23% of the original model size, and shows its potential for bandwidth-adapted 3D streaming and rendering applications.

• The paper introduces a layered progressive approach that encodes multiple levels of detail for adaptive 3D streaming.
• It employs dynamic opacity optimization and occupancy mapping to ensure visual consistency while reducing redundancy.
• Extensive tests show up to 50.71% SSIM improvement, 286.53% LPIPS gain, and 318.41% model size reduction compared to baselines.

Layered Progressive 3D Gaussian Splatting for Adaptive Streaming

This paper introduces LapisGS, a novel approach to adaptive streaming of 3D content using a layered progre3D Gaussian Splatting (3DGS) framework. This approach is particularly designed to address the scalability and visual fidelity issues inherent in streaming complex 3D scenes over bandwidth-constrained environments. The proposed method seeks to balance resource utilization, visual quality, and user experience through a layered representation that allows for dynamic adaptation to varying levels of detail (LOD).

Methodology

LapisGS constructs a multiscale representation for 3DGS content via a progressive training pipeline. The layered structure includes a base layer for fundamental scene depiction and subsequent enhancement layers that progressively add higher resolution details. This framework draws inspiration from scalable coding techniques and progressive LOD representations, optimizing each enhancement layer while freezing the parameters of the underlying layers, thereby concentrating on the newly introduced details.

To maintain visual coherence and reduce redundancy, the method employs dynamic opacity optimization, refining the influence of each layer’s Gaussian splats dynamically during the training process. Additionally, an occupancy map is utilized to exclude insignificant splats, enhancing computational and storage efficiency.

The following are the significant contributions of this work:

1. Layered Progressive Approach: The method encodes multiple levels of detail into a single-layered model, supporting seamless adaptive streaming and rendering.
2. Dynamic Opacity Optimization: This ensures consistency across varying resolution levels by adjusting layer contributions selectively, thereby maintaining visual fidelity and reducing data size.
3. Flexible and Adaptive Rendering: The model allows seamless transitions and view-adaptive rendering strategies without the necessity for separate models for each LOD.
4. Extensive Experimental Validation: Evaluations demonstrate the method’s efficacy in balancing high-quality rendering with compact model size across diverse 3D content.

Experimental Results

The paper presents quantitative results across several datasets: Synthetic Blender, Mip-NeRF360, Tanks and Temples, and Deep Blending. LapisGS consistently outperforms baseline methods such as single-scale, multiscale, and downsample models, achieving high-quality visual fidelity and substantially reduced model sizes. Notably, LapisGS achieves up to a 50.71% improvement in SSIM, 286.53% improvement in LPIPS, and 318.41% reduction in model size compared to baseline models.

Implications and Future Directions

The results underscore the potential of LapisGS for applications requiring real-time rendering and adaptive 3D content streaming. By leveraging a layered structure that progressively refines details, LapisGS can adapt to varying bandwidth conditions and device capabilities, making it particularly useful for extended reality (XR) applications.

Future research could explore several avenues:

• Dynamic Scene Adaptation: Extending the LapisGS framework to handle dynamic scenes and real-time updates.
• Network Condition Adaptability: Evaluating and optimizing the performance of LapisGS under fluctuating network conditions, which would involve designing bitrate adaptation algorithms.
• Enhanced Compression Techniques: Integrating advanced compression techniques to further improve the model's data efficiency without compromising visual fidelity.

In conclusion, LapisGS presents an efficient and scalable solution for adaptive 3DGS streaming, leveraging a progressive, layered approach to balance the trade-offs between visual quality and resource utilization. This framework holds significant promise for a wide range of applications in real-time 3D streaming and rendering.