Spectrally Pruned Gaussian Fields with Neural Compensation (2405.00676v1)

Abstract: Recently, 3D Gaussian Splatting, as a novel 3D representation, has garnered attention for its fast rendering speed and high rendering quality. However, this comes with high memory consumption, e.g., a well-trained Gaussian field may utilize three million Gaussian primitives and over 700 MB of memory. We credit this high memory footprint to the lack of consideration for the relationship between primitives. In this paper, we propose a memory-efficient Gaussian field named SUNDAE with spectral pruning and neural compensation. On one hand, we construct a graph on the set of Gaussian primitives to model their relationship and design a spectral down-sampling module to prune out primitives while preserving desired signals. On the other hand, to compensate for the quality loss of pruning Gaussians, we exploit a lightweight neural network head to mix splatted features, which effectively compensates for quality losses while capturing the relationship between primitives in its weights. We demonstrate the performance of SUNDAE with extensive results. For example, SUNDAE can achieve 26.80 PSNR at 145 FPS using 104 MB memory while the vanilla Gaussian splatting algorithm achieves 25.60 PSNR at 160 FPS using 523 MB memory, on the Mip-NeRF360 dataset. Codes are publicly available at https://runyiyang.github.io/projects/SUNDAE/.

• The paper introduces SUNDAE, a method that employs graph spectral pruning combined with a lightweight neural network to maintain rendering quality while drastically reducing memory usage.
• It achieves a competitive PSNR of 26.80 and renders scenes at 145 FPS on the Mip-NeRF360 dataset, requiring only 104 MB compared to over 523 MB in traditional methods.
• This approach has broad implications for AR/VR, robotics, and mobile graphics, enabling high-quality 3D renderings on resource-constrained platforms.

SUNDAE: A Refreshing Take on Efficient 3D Renderings

What is SUNDAE?

SUNDAE stands for Spectrally prUNeD Gaussian fiElds with Neural Compensation. In a nutshell, it's an innovative approach to representing 3D scenes that helps to greatly reduce memory usage while still maintaining high-quality renderings and decent speeds. This is especially important for applications where memory and rendering speeds are critical, such as in VR/AR or robotics.

Understanding the Core Problem: High Memory Usage

The major drawback in existing 3D Gaussian Splatting (3DGS) methods is their high memory requirement. For instance, to store a well-trained Gaussian field might take up over 700 MB of memory! This hefty requirement limits the practical applications of 3DGS, particularly on mobile or embedded systems. The culprits here are the Gaussian primitives which can number in the millions.

SUNDAE’s Solution: Pruning and Neural Compensation

The SUNDAE approach addresses the memory issue by introducing two key innovations:

1. Graph-based Spectral Pruning:
   • To manage the redundancy among Gaussian primitives, SUNDAE models the relationship between these primitives using graph theory.
   • It prunes the Gaussian field spectrally, meaning it reduces the number of primitives without losing significant scene details. This is achieved by constructing a graph of these primitives and selectively pruning them while maintaining the desired signal quality.
2. Neural Compensation:
   • After pruning, some loss of detail is inevitable. To compensate for this loss, SUNDAE employs a lightweight neural network.
   • This neural head works by mixing the features from the remaining primitives to regenerate the lost details, effectively compensating for the information lost during pruning.

A Glimpse into the Results

Comparing SUNDAE with traditional 3D Gaussian splatting, the new method shows a remarkable improvement in memory efficiency with competitive rendering results. For example, on the Mip-NeRF360 dataset, SUNDAE achieves a Peak Signal-to-Noise Ratio (PSNR) of 26.80 and a fast rendering speed of 145 FPS while only requiring 104 MB of memory. This is a drastic reduction from the 523 MB required by the traditional approach for slightly lower PSNR and FPS.

Why Does It Matter?

The implications of research like SUNDAE are far-reaching. By significantly lowering the memory requirements for high-quality 3D rendering:

• It can make advanced 3D imaging technologies more accessible on platforms with limited resources, such as smartphones and embedded systems.
• It paves the way for advanced AR/VR applications where both rendering quality and speed are crucial for immersive experiences.

Speculating on the Future

Looking ahead, the principles used in SUNDAE could be adapted and expanded for other forms of data beyond 3D scene rendering. Any field dealing with large, redundant datasets may benefit from a similar approach, potentially transforming practices in data compression and real-time rendering in gaming or live simulations.

Moreover, the combination of graph-based techniques with neural networks for data compensation might open new areas of research and application, pushing further the boundaries of what is possible with machine learning and graphics processing.

SUNDAE represents a significant step towards making 3D rendering more memory-efficient and fast without a substantial loss in quality—a sweet deal indeed for developers and users in the 3D content creation field!