Efficient Memory-Optimized 3D Gaussian Fields with Spectral Pruning and Neural Compensation

Concepts de base

A memory-efficient method for 3D Gaussian splatting that leverages spectral pruning of Gaussian primitives and a neural compensation module to maintain high rendering quality and speed with low storage requirements.

Résumé

The paper introduces SUNDAE, a memory-efficient approach for 3D Gaussian splatting. The key contributions are:

Spectral Graph Pruning: The authors construct a graph to model the relationship between Gaussian primitives and use a band-limited graph filter to prune redundant primitives while preserving important details.

Neural Compensation: To mitigate the quality loss from pruning, the authors introduce a lightweight neural network that mixes splatted features from the remaining primitives, effectively capturing the relationship between primitives.

The authors demonstrate that SUNDAE can achieve state-of-the-art rendering quality and speed while significantly reducing the memory footprint compared to vanilla 3D Gaussian splatting. For example, SUNDAE can achieve 26.80 PSNR at 145 FPS using 104 MB memory, while the original 3D Gaussian splatting achieves 25.60 PSNR at 160 FPS using 523 MB memory.
The paper also explores a continuous pruning strategy that integrates pruning into the training process, which can further reduce peak memory usage during training.

Stats

The paper provides the following key metrics:

SUNDAE (10% pruning): 26.80 PSNR, 145 FPS, 104 MB memory
3D Gaussian Splatting (7K): 25.60 PSNR, 160 FPS, 523 MB memory
3D Gaussian Splatting (30K): 27.21 PSNR, 134 FPS, 734 MB memory

Citations

"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."
"Compared with recent neural rendering methods [5, 40], 3DGS requires a much larger memory cost for the same scene, which limits the application of 3GDS on mobile platforms and edge computing."

Idées clés tirées de

Spectrally Pruned Gaussian Fields with Neural Compensation

by Runyi Yang,Z... à arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00676.pdf

Questions plus approfondies

How could the proposed spectral pruning and neural compensation techniques be extended to other primitive-based neural rendering methods beyond 3D Gaussian splatting

The proposed spectral pruning and neural compensation techniques in SUNDAE can be extended to other primitive-based neural rendering methods by adapting the graph signal processing framework and neural compensation module to suit the specific characteristics of each method. For instance, in methods that utilize point-based neural radiance fields, the graph construction can be tailored to capture the relationships between the points in the scene. This could involve defining a graph based on the spatial proximity of points and applying graph filters to prune out redundant or less important points while preserving essential details.
Similarly, the neural compensation module can be adjusted to work with different types of primitives or features used in other rendering methods. For example, in methods that employ voxel-based representations, the neural network head can be designed to integrate information from voxel features to compensate for quality losses post-pruning. By customizing the graph construction and neural compensation components to align with the specific primitives and features used in different rendering methods, the spectral pruning and neural compensation techniques can be effectively applied across a variety of primitive-based neural rendering approaches.

What are the potential limitations or failure cases of the SUNDAE approach, and how could they be addressed in future work

One potential limitation of the SUNDAE approach could be the sensitivity of the spectral pruning technique to the choice of parameters, such as the band-limited ratio 𝛾. If 𝛾 is not appropriately selected, it may lead to suboptimal pruning results, affecting the rendering quality. To address this limitation, future work could focus on developing adaptive or data-driven methods to determine the optimal 𝛾 value based on the characteristics of the scene or dataset. This could involve incorporating machine learning algorithms to automatically adjust 𝛾 during training based on the performance metrics or loss functions.
Another potential failure case could arise from the neural compensation module's inability to effectively capture the complex relationships between primitives in certain scenes. To mitigate this, future research could explore more advanced neural network architectures or attention mechanisms that can better model the intricate dependencies between primitives. Additionally, incorporating feedback mechanisms or reinforcement learning techniques to fine-tune the neural compensation module during training could enhance its ability to compensate for quality losses post-pruning.

How could the continuous pruning strategy be further improved to provide more consistent and predictable memory usage during training

To improve the continuous pruning strategy for more consistent and predictable memory usage during training, several enhancements can be considered:

Adaptive Pruning Intervals: Instead of fixed pruning intervals, an adaptive approach could be implemented where the pruning frequency adjusts dynamically based on memory usage or convergence metrics. This adaptive strategy could help maintain a balance between memory efficiency and rendering quality throughout the training process.

Progressive Pruning: Implementing a progressive pruning strategy where the pruning rate gradually increases or decreases based on the training progress could help achieve more stable memory usage. By incrementally adjusting the pruning rate, the model can adapt to the scene complexity and memory constraints more effectively.

Hybrid Pruning Techniques: Combining continuous pruning with batch pruning at specific milestones during training could provide a more robust memory management strategy. By periodically evaluating the memory footprint and performance metrics, the model can switch between continuous and batch pruning to optimize memory usage while maintaining rendering quality.

By incorporating these enhancements, the continuous pruning strategy can be further refined to ensure more consistent and predictable memory usage during training, leading to improved efficiency and performance in primitive-based neural rendering methods.