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Reducing the Memory Footprint of 3D Gaussian Splatting

105
Citations
May 11, 2024
Published Date

Research Abstract & Technology Focus

3D Gaussian splatting provides excellent visual quality for novel view synthesis, with fast training and realtime rendering; unfortunately, the memory requirements of this method for storing and transmission are unreasonably high. We first analyze the reasons for this, identifying three main areas where storage can be reduced: the number of 3D Gaussian primitives used to represent a scene, the number of coefficients for the spherical harmonics used to represent directional radiance, and the precision required to store Gaussian primitive attributes. We present a solution to each of these issues. First, we propose an efficient, resolution-aware primitive pruning approach, reducing the primitive count by half. Second, we introduce an adaptive adjustment method to choose the number of coefficients used to represent directional radiance for each Gaussian primitive, and finally a codebook-based quantization method, together with a half-float representation for further memory reduction. Taken together, these three components result in a x27 reduction in overall size on disk on the standard datasets we tested, along with a x1.7 speedup in rendering speed. We demonstrate our method on standard datasets and show how our solution results in significantly reduced download times when using the method on a mobile device (see Fig. 1).
reducing memory footprint gaussian splatting
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What is the core focus of the research titled 'Reducing the Memory Footprint of 3D Gaussian Splatting'?

This literature focuses on: 3D Gaussian splatting provides excellent visual quality for novel view synthesis, with fast training and realtime rendering; unfortunately, the memory requirements of this method for storing and transmission are unreasonably high. We first analyze...

Are there open-source GitHub repositories related to Reducing the Memory Footprint of 3D Gaussian Splatting?

Yes, open-source projects like nidhinjs/prompt-master (A Claude skill that writes the accurate prompts for any AI tool. Zero tokens or credits wasted. Full context and memory retention) are actively building upon these concepts.

Which startups are commercializing the technology behind Reducing the Memory Footprint of 3D Gaussian Splatting?

Products like ContextPool are bringing this to market. Their focus is: Persistent memory for AI coding agents.

What other academic literature is closely related to 'Reducing the Memory Footprint of 3D Gaussian Splatting'?

Yes, highly correlated activity was mapped. An entry titled '4D Gaussian Splatting for Real-Time Dynamic Scene Rendering' discusses this: No description provided.

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