GoDe: Gaussians on Demand for Progressive Level of Detail and Scalable Compression

• URL: http://arxiv.org/abs/2501.13558v1
• Date: Thu, 23 Jan 2025 11:05:45 GMT
• Title: GoDe: Gaussians on Demand for Progressive Level of Detail and Scalable Compression
• Authors: Francesco Di Sario, Riccardo Renzulli, Marco Grangetto, Akihiro Sugimoto, Enzo Tartaglione,
• Abstract summary: We propose a novel, model-agnostic technique that organizes Gaussians into several hierarchical layers. This method, combined with recent approach of compression of 3DGS, allows a single model to instantly scale across several compression ratios. We validate our approach on typical datasets and benchmarks, showcasing low distortion and substantial gains in terms of scalability and adaptability.
• Score: 13.616981296093932
• License:
• Abstract: 3D Gaussian Splatting enhances real-time performance in novel view synthesis by representing scenes with mixtures of Gaussians and utilizing differentiable rasterization. However, it typically requires large storage capacity and high VRAM, demanding the design of effective pruning and compression techniques. Existing methods, while effective in some scenarios, struggle with scalability and fail to adapt models based on critical factors such as computing capabilities or bandwidth, requiring to re-train the model under different configurations. In this work, we propose a novel, model-agnostic technique that organizes Gaussians into several hierarchical layers, enabling progressive Level of Detail (LoD) strategy. This method, combined with recent approach of compression of 3DGS, allows a single model to instantly scale across several compression ratios, with minimal to none impact to quality compared to a single non-scalable model and without requiring re-training. We validate our approach on typical datasets and benchmarks, showcasing low distortion and substantial gains in terms of scalability and adaptability.

Related papers

• Forget the Data and Fine-Tuning! Just Fold the Network to Compress [13.611551223875194]
  We introduce model folding, a novel data-free model compression technique that merges structurally similar neurons across layers. We show that model folding achieves comparable performance to data-driven compression techniques and outperforms recently proposed data-free methods. This approach is particularly effective for compressing large-scale models, making it suitable for deployment in resource-constrained environments.
  arXiv  Detail & Related papers  (2025-02-14T15:10:43Z)
• Choose Your Model Size: Any Compression by a Single Gradient Descent [9.074689052563878]
  We present Any Compression via Iterative Pruning (ACIP) ACIP is an algorithmic approach to determine a compression-performance trade-off from a single gradient descent run. We show that ACIP seamlessly complements common quantization-based compression techniques.
  arXiv  Detail & Related papers  (2025-02-03T18:40:58Z)
• ELMGS: Enhancing memory and computation scaLability through coMpression for 3D Gaussian Splatting [16.373800112150573]
  3D models have recently been popularized by the potentiality of end-to-end training offered by Neural Radiance Fields and 3D Gaussian Splatting models. We propose an approach enabling both memory and computation scalability of such models. Our results on popular benchmarks showcase the effectiveness of the proposed approach and open the road to the broad deployability of such a solution even on resource-constrained devices.
  arXiv  Detail & Related papers  (2024-10-30T17:01:28Z)
• Fast Feedforward 3D Gaussian Splatting Compression [55.149325473447384]
  3D Gaussian Splatting (FCGS) is an optimization-free model that can compress 3DGS representations rapidly in a single feed-forward pass. FCGS achieves a compression ratio of over 20X while maintaining fidelity, surpassing most per-scene SOTA optimization-based methods.
  arXiv  Detail & Related papers  (2024-10-10T15:13:08Z)
• Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models [56.00251589760559]
  Large language models (LLMs) can act as gradient priors in a zero-shot setting. We introduce LM-GC, a novel method that integrates LLMs with arithmetic coding. Experiments indicate that LM-GC surpasses existing state-of-the-art lossless compression methods.
  arXiv  Detail & Related papers  (2024-09-26T13:38:33Z)
• Generalized Nested Latent Variable Models for Lossy Coding applied to Wind Turbine Scenarios [14.48369551534582]
  A learning-based approach seeks to minimize the compromise between compression rate and reconstructed image quality. A successful technique consists in introducing a deep hyperprior that operates within a 2-level nested latent variable model. This paper extends this concept by designing a generalized L-level nested generative model with a Markov chain structure.
  arXiv  Detail & Related papers  (2024-06-10T11:00:26Z)
• ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model [77.71796503321632]
  We introduce a context model in the anchor level for 3DGS representation, yielding an impressive size reduction of over 100 times compared to vanilla 3DGS. Our work pioneers the context model in the anchor level for 3DGS representation, yielding an impressive size reduction of over 100 times compared to vanilla 3DGS and 15 times compared to the most recent state-of-the-art work Scaffold-GS.
  arXiv  Detail & Related papers  (2024-05-31T09:23:39Z)
• SAGS: Structure-Aware 3D Gaussian Splatting [53.6730827668389]
  We propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene. SAGS reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets.
  arXiv  Detail & Related papers  (2024-04-29T23:26:30Z)
• CompGS: Efficient 3D Scene Representation via Compressed Gaussian Splatting [68.94594215660473]
  We propose an efficient 3D scene representation, named Compressed Gaussian Splatting (CompGS) We exploit a small set of anchor primitives for prediction, allowing the majority of primitives to be encapsulated into highly compact residual forms. Experimental results show that the proposed CompGS significantly outperforms existing methods, achieving superior compactness in 3D scene representation without compromising model accuracy and rendering quality.
  arXiv  Detail & Related papers  (2024-04-15T04:50:39Z)
• Activations and Gradients Compression for Model-Parallel Training [85.99744701008802]
  We study how simultaneous compression of activations and gradients in model-parallel distributed training setup affects convergence. We find that gradients require milder compression rates than activations. Experiments also show that models trained with TopK perform well only when compression is also applied during inference.
  arXiv  Detail & Related papers  (2024-01-15T15:54:54Z)
• Self-Supervised GAN Compression [32.21713098893454]
  We show that a standard model compression technique, weight pruning, cannot be applied to GANs using existing methods. We then develop a self-supervised compression technique which uses the trained discriminator to supervise the training of a compressed generator. We show that this framework has a compelling performance to high degrees of sparsity, can be easily applied to new tasks and models, and enables meaningful comparisons between different pruning granularities.
  arXiv  Detail & Related papers  (2020-07-03T04:18:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.