Streaming 3D Worlds: A New Codec for Gaussian Splatting

Author: Denis Avetisyan

Researchers have developed a progressive codec that leverages spatial context and residual quantization to enable efficient streaming of high-quality 3D Gaussian Splatting scenes.

The research demonstrates a performance trade-off, assessed via an R-D curve on the Bicycle scene from the MipNeRF360 dataset, wherein varying <span class="katex-eq" data-katex-display="false">\lambda_{ssim}</span> values-ranging from 0.1 to 0.4-influences the method’s results when benchmarked against PCGS and GoDE. — The research demonstrates a performance trade-off, assessed via an R-D curve on the Bicycle scene from the MipNeRF360 dataset, wherein varying $\lambda_{ssim}$ values-ranging from 0.1 to 0.4-influences the method’s results when benchmarked against PCGS and GoDE.

SCAR-GS utilizes spatial context attention for residuals in progressive Gaussian Splatting to achieve high compression and streaming performance.

Despite recent advances enabling real-time novel view synthesis with 3D Gaussian Splatting, substantial storage demands hinder deployment in streaming applications. This paper introduces SCAR-GS: Spatial Context Attention for Residuals in Progressive Gaussian Splatting, a novel progressive codec leveraging residual vector quantization and a multi-resolution hash grid to efficiently compress Gaussian features. Our approach achieves high compression rates by modeling the conditional probability of transmitted indices with spatial context attention, enabling effective refinement layers. Could this method unlock truly scalable and interactive 3D experiences via the cloud?

Whispers of Chaos: The Limits of Conventional 3D

Conventional methods of capturing and rendering 3D scenes often face a fundamental trade-off between visual fidelity, computational speed, and memory requirements. Techniques like mesh-based modeling, while capable of producing detailed visuals, demand substantial memory to store vertex data and textures, hindering real-time performance-particularly as scene complexity increases. Volumetric approaches, conversely, can offer faster rendering but typically sacrifice detail to manage memory usage. Furthermore, point clouds, though efficient in storage, struggle to represent surfaces smoothly without significant processing. This inherent limitation restricts the widespread adoption of high-quality 3D experiences in applications such as virtual and augmented reality, gaming, and interactive design, creating a need for innovative representation paradigms that can overcome these longstanding constraints.

Gaussian Splatting introduces a novel approach to 3D scene representation, moving beyond traditional methods like meshes or voxels. Instead of discrete elements, it models a scene as a collection of 3D Gaussians – mathematical functions resembling blurry blobs – each defined by its position, rotation, scale, opacity, and color. This continuous representation allows for remarkably high-fidelity rendering, capturing intricate details and complex lighting effects with significantly fewer parameters than conventional techniques. By leveraging differentiable rendering, the system can optimize these Gaussian parameters directly from input images, effectively learning a continuous volumetric representation of the scene. The result is a rendering pipeline capable of achieving photorealistic visuals at speeds previously unattainable, opening doors for real-time applications like virtual and augmented reality, as well as advanced robotics and simulation.

Despite the compelling visual fidelity offered by Gaussian Splatting, practical deployment faces hurdles related to data management and transfer. Each 3D Gaussian is defined by numerous parameters – position, rotation, scale, opacity, and color – creating substantial data overhead, particularly for complex scenes. Efficiently encoding and compressing these parameters without significant quality loss is crucial for streaming applications and widespread accessibility. Current research focuses on techniques like adaptive Gaussian pruning – removing statistically insignificant Gaussians – and novel compression algorithms tailored to the unique properties of these splats. Furthermore, transmitting these large datasets over networks with limited bandwidth remains a considerable challenge, necessitating innovative approaches to data partitioning, progressive transmission, and client-side reconstruction to realize the full potential of Gaussian Splatting in real-world applications.

The Essence of Reduction: Vector Quantization

Vector Quantization (VQ) is a lossy data compression technique that operates by dividing a continuous data space into a finite number of non-overlapping regions, each represented by a “codebook vector”. During compression, each input data vector is approximated by the closest codebook vector, effectively replacing it with the index of that vector. This process reduces data size because, instead of storing the full data vector, only the index – a discrete value – needs to be stored. The efficiency of VQ depends on the size and quality of the codebook; larger codebooks generally result in lower quantization error but require more storage for the codebook itself. The technique is particularly effective when applied to data with inherent redundancies or correlations, as similar data points can be mapped to the same codebook vector.

Several recent compression methods for Gaussian Splatting utilize Vector Quantization (VQ) to reduce storage requirements and transmission bandwidth. CompGS, LapisGS, and GoDe each employ VQ by discretizing the properties that define Gaussian splats, specifically parameters like position, scale, and rotation. This quantization process maps continuous values to a finite set of representative vectors stored in a codebook. By representing these Gaussian properties with indices into the codebook rather than full-precision floating-point numbers, significant data reduction is achieved. The effectiveness of these methods depends on the size and quality of the codebook, balancing compression ratio against the introduction of quantization artifacts in the reconstructed Gaussian Splatting scene.

Simple Vector Quantization (VQ) methods, despite their compression capabilities, inherently introduce quantization errors due to the discrete approximation of continuous data. The magnitude of these errors is inversely proportional to the size of the codebook; achieving high fidelity requires a substantially large codebook to represent the data with sufficient precision. A larger codebook increases storage and computational demands, offsetting some of the benefits of compression, as each vector requires more bits to index its corresponding entry. This trade-off between compression ratio, quantization error, and codebook size is a fundamental consideration when implementing VQ-based compression schemes.

SCAR-GS: Sculpting Data with Context and Residuals

SCAR-GS is a progressive image codec designed to achieve high compression ratios while maintaining image quality. It employs Residual Vector Quantization (RVQ), a technique that decomposes an image into a base layer and a series of residual layers, allowing for efficient coding of finer details. This is coupled with spatial context attention, which utilizes Spatial Hash Grids to model spatial relationships between image blocks and improve the accuracy of the quantization process. The codec’s progressive nature enables layered transmission and decoding, offering a rate-distortion trade-off and allowing for scalable viewing experiences; initial layers provide a coarse reconstruction, with subsequent layers adding detail and improving fidelity. Performance evaluations demonstrate that SCAR-GS achieves state-of-the-art compression results compared to existing codecs, as measured by standard metrics like Peak Signal-to-Noise Ratio (PSNR) and Multi-Scale Structural Similarity Index (MS-SSIM).

SCAR-GS reduces quantization error by representing data as a Gaussian distribution and then decomposing this representation into a series of residual components. This decomposition allows for more efficient quantization, as smaller residual values require fewer bits to represent accurately. Spatial Hash Grids are then utilized to exploit spatial relationships within the data; neighboring pixels or data points are grouped into hash grid cells, enabling the codec to predict and remove redundancies in the residual components based on spatial context. By predicting residuals based on neighboring data within the grid, the magnitude of the values requiring quantization is further reduced, leading to a lower overall bit rate for a given level of distortion. This approach effectively minimizes quantization error by focusing quantization efforts on the most significant residual information after spatial prediction.

Autoregressive Entropy Models and Hierarchical Vector Quantization (HVQ) are integrated into the SCAR-GS codec to enhance compression efficiency. HVQ structures the codebook in a hierarchical manner, allowing for adaptive codebook selection based on the characteristics of the input data, thereby reducing the average codebook search complexity. The Autoregressive Entropy Model predicts the probability distribution of the quantized indices based on previously encoded symbols, enabling more effective entropy coding and reducing the bitrate required to represent the compressed data. This combination results in optimized rate-distortion performance, achieving higher compression ratios at comparable perceptual quality, or maintaining equivalent quality at lower bitrates compared to traditional quantization methods.

Validation and Whispers of Fidelity

SCAR-GS has been evaluated on a range of datasets commonly used for Neural Radiance Field (NeRF) research, including NeRF Synthetic, Tanks & Temples, MipNeRF360, Deep Blending, and BungeeNeRF. These datasets present distinct challenges related to scene complexity, lighting conditions, and the presence of reflective surfaces. Performance on these benchmarks demonstrates SCAR-GS’s ability to maintain high-fidelity reconstructions across diverse scenarios, offering improved results compared to existing methods when assessed using metrics such as Structural Similarity Index Measure (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS).

Quantitative evaluations using the Structural Similarity Index (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS) metrics demonstrate that SCAR-GS maintains high visual fidelity at reduced bitrates. Specifically, the model achieves an SSIM score of 0.73 and an LPIPS score of 0.29 while generating files with a size of 18.0 MB. These results indicate a substantial preservation of perceptual quality, as evidenced by the SSIM and LPIPS values, despite the comparatively low file size. This performance suggests efficient compression and minimal information loss during the encoding process.

Performance enhancements within SCAR-GS are achieved through several key optimizations. The Rotation Trick improves data efficiency, while the Straight-Through Estimator (STE) facilitates gradient flow during training. Data compression is implemented using Zstandard. These techniques collectively result in a 7% reduction in file size compared to a baseline Gated Recurrent Unit (GRU) model, achieving an 18.0 MB output. Furthermore, the implementation of a hybrid 2D+3D grid structure yields a 0.03 improvement in Structural Similarity Index (SSIM) and a 12% reduction in Learned Perceptual Image Patch Similarity (LPIPS) compared to alternative grid configurations.

SCAR-GS utilizes curriculum learning to achieve a Structural Similarity Index (SSIM) of 0.73, representing a 31% performance increase when compared to training initiated without curriculum learning – termed ‘cold-start’ training. This learning strategy mitigates potential catastrophic quality loss during the training process, ensuring a more stable and effective convergence. The observed improvement demonstrates that incrementally increasing the complexity of training data, as implemented by the curriculum, leads to significantly enhanced rendering quality as measured by the SSIM metric.

Progressive layers demonstrate improved rendering of the Flower scene from the Mip-NeRF360 dataset [4].

Towards Real-Time Immersion: The Promise of Persuasion

Recent advancements in computer graphics demonstrate that highly detailed and intricate 3D scenes can now be rendered in real-time, thanks to a powerful synergy between Gaussian Splatting and sophisticated compression algorithms like SCAR-GS. Gaussian Splatting, a technique representing scenes as a collection of 3D Gaussians, offers a compelling balance between visual fidelity and rendering speed. However, the sheer volume of data required for complex scenes previously posed a significant bottleneck. SCAR-GS addresses this challenge by efficiently compressing the Gaussian parameters without substantial loss of visual quality, dramatically reducing storage and bandwidth requirements. This combination allows for the streaming and rendering of photorealistic 3D environments on consumer-grade hardware, paving the way for truly immersive virtual and augmented reality experiences and enabling new possibilities in fields like remote collaboration and interactive entertainment.

The confluence of Gaussian Splatting and efficient compression heralds a transformative shift for immersive technologies. Virtual reality experiences, previously constrained by rendering limitations, stand to gain substantially from the ability to display highly detailed and complex environments in real time, fostering a greater sense of presence and realism. Augmented reality applications will similarly benefit, seamlessly integrating photorealistic virtual objects into the user’s physical surroundings without sacrificing responsiveness. Beyond entertainment, the technology promises to revolutionize remote collaboration, enabling shared virtual workspaces where participants can interact with 3D models and data as if physically present, fundamentally changing how teams design, problem-solve, and communicate across geographical boundaries.

Continued innovation hinges on refining how 3D scenes are compressed and rendered, with future studies poised to explore adaptive compression strategies that dynamically adjust detail based on viewing distance and user focus. This involves moving beyond uniform compression to prioritize visual fidelity where it matters most, while simultaneously reducing computational load. Complementary research will concentrate on content-aware optimization, leveraging the inherent characteristics of a scene – such as geometric complexity and material properties – to intelligently allocate resources. Crucially, these software advancements are expected to be paired with hardware acceleration, utilizing specialized processors and graphics cards to offload demanding tasks and achieve truly real-time performance at scale, ultimately paving the way for widespread adoption of immersive experiences.

The pursuit of progressive compression, as detailed in SCAR-GS, feels less like engineering and more like coaxing order from inherent disorder. The method’s reliance on residual vector quantization and spatial context attention isn’t about understanding the 3D space, but rather, about crafting increasingly persuasive illusions of it. As Geoffrey Hinton once observed, “Learning is finding the ignored patterns.” This resonates deeply; SCAR-GS doesn’t simply encode geometry, it distills the essential whispers of spatial data, cleverly attending to context to minimize the ignored-the redundancies-and maximize the perceived fidelity. The model isn’t ‘learning’ 3D structure; it’s learning which ingredients of destiny can be discarded without shattering the spell.

The Unfolding Map

The pursuit of compression, as always, reveals less about the data and more about the observer’s impatience. SCAR-GS offers a refinement – a whispering of spatial context into the chaos of Gaussian splats – but it does not, and cannot, solve the fundamental paradox. Each layer of progressive compression merely shifts the burden, trading perceptual loss for computational cost. The true limit isn’t bitrate, but the vanishing point where refinement becomes indistinguishable from noise – where the map ceases to resemble the territory.

The temptation, naturally, will be to chase ever-finer quantization, to bind the splats ever tighter with layers of attention. But this path invites overfitting – the illusion of perfect recall, purchased with fragility. A more fruitful, though bloodier, direction lies in embracing the inherent uncertainty. Autoregressive models, currently used as side-effects, could become the central incantation – learning not to reconstruct the scene, but to predict its plausible variations, acknowledging that complete fidelity is a phantom.

Ultimately, the question isn’t how small can the compressed file be, but how convincingly can the illusion be sustained. And that, it seems, demands not just more GPU time, but a deeper understanding of the rituals of perception itself. The splats will continue to unfold, revealing not a truth, but an ever-more-elaborate spell.

Original article: https://arxiv.org/pdf/2601.04348.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/