CVMMSep 3, 2024

PRoGS: Progressive Rendering of Gaussian Splats

arXiv:2409.01761v113 citationsh-index: 9
Originality Incremental advance
AI Analysis

This work addresses memory and bandwidth constraints for on-device and streaming applications, making remotely hosted 3DGS content more accessible, but it is incremental as it builds on existing compression methods.

The paper tackles the problem of high storage and memory requirements in 3D Gaussian Splatting (3DGS) by introducing a progressive rendering approach that displays visible content early without loading the entire scene, showing significant improvements in quality across all metrics compared to existing methods.

Over the past year, 3D Gaussian Splatting (3DGS) has received significant attention for its ability to represent 3D scenes in a perceptually accurate manner. However, it can require a substantial amount of storage since each splat's individual data must be stored. While compression techniques offer a potential solution by reducing the memory footprint, they still necessitate retrieving the entire scene before any part of it can be rendered. In this work, we introduce a novel approach for progressively rendering such scenes, aiming to display visible content that closely approximates the final scene as early as possible without loading the entire scene into memory. This approach benefits both on-device rendering applications limited by memory constraints and streaming applications where minimal bandwidth usage is preferred. To achieve this, we approximate the contribution of each Gaussian to the final scene and construct an order of prioritization on their inclusion in the rendering process. Additionally, we demonstrate that our approach can be combined with existing compression methods to progressively render (and stream) 3DGS scenes, optimizing bandwidth usage by focusing on the most important splats within a scene. Overall, our work establishes a foundation for making remotely hosted 3DGS content more quickly accessible to end-users in over-the-top consumption scenarios, with our results showing significant improvements in quality across all metrics compared to existing methods.

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