Fast Feedforward 3D Gaussian Splatting Compression
Authors: Yihang Chen
Qianyi Wu
Mengyao Li
Weiyao Lin
Mehrtash Harandi
Jianfei Cai
Venue: ICLR 2025 Poster (2025)
Keywords: 3DGS
compression
optimization-free
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A 3DGS compression framework that compresses any existing 3DGS rapidly without fine-tuning.

Abstract: With 3D Gaussian Splatting (3DGS) advancing real-time and high-fidelity rendering for novel view synthesis, storage requirements pose challenges for their widespread adoption. Although various compression techniques have been proposed, previous art suffers from a common limitation: for any existing 3DGS, per-scene optimization is needed to achieve compression, making the compression sluggish and slow. To address this issue, we introduce Fast Compression of 3D Gaussian Splatting (FCGS), an optimization-free model that can compress 3DGS representations rapidly in a single feed-forward pass, which significantly reduces compression time from minutes to seconds. To enhance compression efficiency, we propose a multi-path entropy module that assigns Gaussian attributes to different entropy constraint paths for balance between size and fidelity. We also carefully design both inter- and intra-Gaussian context models to remove redundancies among the unstructured Gaussian blobs. Overall, FCGS achieves a compression ratio of over 20X while maintaining fidelity, surpassing most per-scene SOTA optimization-based methods. Code: github.com/YihangChen-ee/FCGS.

Published as a conference paper at ICLR 2025
FAST FEEDFORWARD 3D GAUSSIAN SPLATTING COM- PRESSION
Yihang Chen Qianyi M'n'Maagyaa Li Weiyao ; Mehrtash Harandi', Jianfei Cai² Shanghai Jiao Tong University, "Monash University, *Shanghai University
(yhchen.cen ee, wylin]esjte edu. en, sdlmy@shu. waa..edu.cco en, [qianyi... wu, mehrtash. harandi, jianfei. cai jlanfei.cal/mmonnsh. @monash.edu edu
ABSTRACT
With 3D Gaussian Splatting (3DGS) advancing real-time and high-fidelity ren- dering for novel view synthesis, storage requirements pose challenges for their widespread adoption. Although various compression techniques have been pro- posed, previous art suffers from common limitation: for any existing 3DGS, per-scene optimization is needed achieve compression, making the compres- sion sluggish and slow. To address this issue, we introduce Fast Compression of 3D Gaussian Splatting (FCGS), an optimization-free model that can compress 3DGS representations rapidly in a single feed-forward pass, which significantly reduces compression time from minutes to seconds. To enhance compression ef- ficiency, we propose a multi-path entropy module that assigns Gaussian attributes to different entropy constraint paths for balance between size and fidelity. We also carefully design both inter- and intra-Gaussian context models to remove re- dundancies among the unstructured Gaussian blobs. Overall, FCGS achieves a compression ratio of over 20x while maintaining fidelity, surpassing most SOTA per-scene optimization-based methods. Code: guthub..om/VihangCheeeeeeee
INTRODUCTION
In recent years, 3D Gaussian Splatting (3DGS) (Kerbl al., 2023) has significantly advanced the field of novel view synthesis. By leveraging fully explicit Gaussians with color and geom- ctry attributes. 3DGS facilitates efficient scene training and rendering through rasterization tech- niques (Zwicker al., 200T). However, the vast number of Gaussians poses considerable storage challenge, hindering its wider application. To address the storage challenges associated with 3DGS, various compression methods have been developed, surveyed in (Bagdasarian et al. 2024). These advancements have significantly reduced the storage requirements, bringing them to an acceptable scale. By reviewing the key developments in this area, identified two fundamental principles that underpin the compression of 3DGS: the value-based and structure-bused principles.
Value-based principle. This principle assesses the importance of parameters through value- based importance scores or similarities. Techniques like pruning and vector quantization are employed reduce the parameter count by retaining only the most representative val- ues. With this principle, earlier studies (Lee et al. 2024 Niedermayr et al.) 2024 Fan et al,
2024) focused on compactly representing unorganized Gaussian primitives based on their parameter values, and discarded less significant parameters for model efficiency.
Stracture-based principle. More recent approaches have shifted towards exploiting the structural relationships between Gaussian primitives for improved compression {Lu et al. 2024 Bagdasarian et al., 2024). This principle emphasizes leveraging organized structures to systematically arrange unsorted Gaussian primitives, which helps eliminate redundan- cies by establishing structured connections. For instance, HAC (Chen et al. 2024b) uses the Instant-NGP (Müller et al. (022) to organize Gaussian anchor features, SOG adopts a self-organizing grid (Morgenstern et al., 2023). and IGS (Wu & Tuytelaars. triplane (Chan et al. 2022) structure to effciently organize Gaussian data.
2024 uses
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context Published as a conference paper at ICLR 2025
Multi-view images & Views
Lighttussii
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minutes
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seconds
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Figure I: Left: Existing compression methods require optimization of the existing 3DGS, leading to the drawback of being time-consuming for training. Our proposed FCGS overcomes this issue by compressing 3DGS representations consumption for compression. Right: Compared to Lightgaussian (Fan et al. 2024). FCGS
single feed-forward pass. significantly reducing time FCGS achieves improved RD performance while requiring much less execution time on the DL3DV-G dataset.
Although effective, these compression techniques have common limitation: for any existing 3DGS. per-scene optimization needed to achieve compression, as shown in Figure [] While the optimization-basee compression pipeline benefits from scene-specific training to achieve superior RD performance, slows down the compression significantly due time-consuming finetuning.
To address this challenge, we propose a novel approach: an optimization- compression pipeline that enables fast compression of existing 3DGS representations through single feed-forward pass, which we call FCGS (Fast Compression of 3D Gaussian Splatting) from now on. Agnostic to the source of the 3DGS (either is from optimizaiton (Kerbl et al., 2023) or from ferd-forwwrd models (Charatan et al. 2024 Szymanowicz al., 2024 Chen et al. 2024c Tang et al., 2024
our FCGS allows for fast compression, offering a convenient and hassle-fres free solution. In contrast to previous methods that degrade the parameter values alter the 3DGS structure, thereby requiring additional finetuning, FCGS aims to preserve both the values and the structure integrity of 3DGS, enabling an optimization-fres design. The Multi-path Entropy Module (MEM), designed under the value-based principle, deduces masks that determine whether attributes should be directly quantized for compression or processed through an autoencoder. Building on the masks determined by MEM and inspired by the setuectrr-bbsee principle, we propose both inter- and models that effectively capture structural relationships among Gaussian attributes.
It is important to highlight that both pipelines of per-scene optimization-bunn -aased compression (previ- ous methods) and generalizable optimization-free compression (our approach) are significant and serve different purposes. The former benefits from per-scene adaptation to achieve superior RD per- formance but suffers from slow compression speed, making suitable for permanent data storage or server-side encoding. In contrast, our pipeline offers convenient and hassle-free solution, where a single model can directly and rapidly compress various 3DGS without the need for finetuning, making it well-suited for -ime-sensitive applications. To the best of our knowledge, the first to achieve a generalizable optimization-free compression pipeline for 3DGS. Although the absence of per-scene finetuning naturally limits RD performance, pression ratio exceeding 20x while maintaining excellent fidelity by meticulously designing MEM and context models. Our contributions are summarized follows:
work is
have still achieved com-
We propose pioneering fast and generalizable optimization-free compression pipeline for 3D Gaussian Splatting, named FCGS, effectively broadening the application scenarios for 3DGS compression techniques.
To facilitate efficient compression of 3DGS representations, we introduce the MEM mod- ule to balance size and fidelity across different Gaussians. Additionally, we meticulously customize both inter- and intra-Gaussian context models, significantly enhancing compres- sion performance by effectively eliminating redundancies.
Extensive experiments across various datasets demonstrate the effectiveness of FCGS, achieving a compression ratio of 20 while maintaining excellent fidelity, even surpass- ing most of the optimization-base methods.
2 RELATED WORK
The field of 3D scene representation for 3D scenes has seen significant advancements in recent years. Neural Radiance Field (NeRF) (Mildenhall et al. 2021) models scene information using fully
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implicit MLPs, which predict opacity and RGB values at 3D coordinates for ray rendering. How- ever, these MLPs are designed to be quite large to capture all the scene-specific details, leading to slow training and rendering times. To improve both fidelity and rendering efficiency, subsequent works (Müller et al., 2022 Chen et al. 2022 Sun et al., 2022) have introduced learnable explicit representations that assign scene-specifie information to the input coordinates before they are pro- cessed by the MLPs. These methods. however, increase storage requirements due to the use of explicit representations. 3D Gaussian Splatting (Kerbl et al. 2023) takes this further by representing the scene entirely through explicit attributed Gaussians. Using rasterization techniques, these Gaus- sians can be quickly rendered into 2D images for a given viewpoint. Unfortunately, this fully explicit representation significantly increases storage demands. To address the storage issues of NeRF and 3DGS, various research efforts have focused on compression techniques, which generally fall into two main design principles: value-based and structure-
The value-based principle focuses on the importance scores or similarities of parameters. Using these scores or similarities, pruning and vector quantization can be applied via thresholding or clus- tering, allowing for compression by retaining only the most representative parameters. However, this process often alters the original parameter values by a noticeable margin, necessitating ditional finetuning step to recover the lost information. For NeRF, methods like VQRF (Li et all 2023) and Re:NeRF (Deng & Tartaglione 2023) calculate the significance of parameters based on opacity or gradient values and apply pruning or vector quantization, followed by a finetuning phase. BiRF Shin & Park (2023) investigates parameters' values and binarizes them. For 3DGS, simi- lar value-based thresholding and clustering techniques are commonly used, as seen in works such
ad-
as Lee al. (2024); ; Niedermayr et al. (2024); Navaneet et al. (2024): Fan et al. (2024): Girish et al.
2024): Wang et al. (2024)) Ali et al. 2024 Fang & Wang 2024). which consider Gaussian val-
ues. Additionally, scalar quantization and knowledge distillation are implemented in Girish et al.
2024 and (Fan et al. 2024). respectively.
The structure-bused principle represents another major direction that investigates the structural re- dundancies of parameters. CNC (Chen et al. 2024a) introduced a pioneering proof of concept by applying level- and dimension-wise context models to compress hash grids for Instant-NGP Müüller
al. 2022 in the NeRF series. This structure- based design is also evident in works like Tang et al.
(202); Rho et al. (2023); Girish et al. (2023); Li et al. (202)). CodecNeRF (Kang et al.) 2024) investigates a forwaforward approach to generate neural radiance field for scenes and then directly
compresses them. In the case of 3DGS, structure- sinueture-based compression methods have also seen notable progress. The primary challenge is the sparse and unorganized nature of 3DGS, which makes it dif- ficult to identify and utilize structural relationships. Grid-based sorting (Morgenstern et al., 2023) addresses this by projecting 3D Gaussians onto 2D planes for compression while preserving spa- tial relationships Scaffold-GS (Lu et al. (024) uses anchors to cluster nearby Gaussians that share common features, and based on this, works such as Chen et al. (0024b) Liu et. (2024) Wang al.) (2024b) further reduce redundancies for anchors, resulting in improved compression performance.
HAC (Chen et al. 20246) and IGS (Wu & Tuytelaars 2024) explore relations among the organized grids and Gaussians. SUNDAE (Yang et al. 2024) employs spectral Graph to improve pruning.
However, these approaches all require per-scene finetuning for compression on a given 3DGS, which can be time-consuming. In this paper, we explore novel, optimization-fra pipeline for 3DGS compression, innovatively building context models for Gaussian primitives, which are essential for climinating redundancy and improving compression efficiency.
3 FAST COMPRESSION OF 3D GAUSSIAN SPLATTING
Our goal is to rapidly compress a 3DGS representation in a single feed-forward pass without fine- tuning. Inspired by image compression methods (Ballé et al. 2018), we adopt an autoencoder-based structure, where the Gaussian attributes are encoded into a latent space for compression, as shown in Figure - However, we found that treating all Gaussian parameters equally and feeding them all into the same autoencoder could lead to a significant loss of fidelity, some attributes were highly sensitive to deviations. To address this, we introduce a Multi-path Entropy Module (MEM) to effectively balance compression size and fidelity. For the context model design, customize both inter- and Marra-Gassiinn context models that effectively eliminate redundancies among param- eters of Gaussians (Figure P. which are lightweight and efficient. Additionally, use a Gaussian
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Gaussian
Ag
factorized
17
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m=0
x)
mn=
MEM
How to apply context models? MEM
Figure 2: Our approach is inspired by image compression, where the input Gaussian attributes is mapped into the latent space ý for compression after passing through an analysis transform and quantization to eliminate redundancies. To compress y. hyperprior branch introduced, using the coarse representation & to estimate the distribution parameters of ý under a Gaussian distribution assumption, which aids in entropy encoding and decoding. In addition the hyperprior, various context models are applied to ý to improve the estimation of distribution probabilities. After decoding V. a synthesis transform Is projects back to the original space as E. loss function is used to maintain high fidelity between Φ and æ using their rendered images, while minimizing the entropy of ý and &. AE and AD represent Arithmetic Encoding and Arithmetic Decoding, respectively. In paper, implement transform networks simple MLPs. Ia and gs consist of layers each, while ha and hg have 3 layers each.
Mixture Model (GMM) to estimate the probability distribution. In the following sections, we first discuss the background and characteristics of 3DGS, and then delve into the design of our FCGS.
3.1 PRELIMINARIES AND DISCUSSIONS
3D Gaussian Splatting (3DGS) (erbll et al. 2023 employs a large number (e.g.. 1 million) at- tributed Gaussians represent 3D scene. These Gaussians are characterized by color and geome- try parameters, and are splatted along given views using rasterization to generate rendered images. Each Gaussian is defined by covariance Σ E 3 and a location (mean) μ E R33
G(I) = exp ( -t)
(1)
where E IRa is a random 3D location, and Σ is defined by scaling matrix S E R3x3 and a rotation matrix E RR333 such that Σ R R. To render a pixel value C E RR3, the Gaussians are first splatted to 2D, and rendering is performed as follows:
i-1 c= c.a (1-)aa))
j=1
where E E is the opacity, and R3 is the view-dependent color of each Gaussian, which is calculated from 3-degree Spherical Harmonics (SH) 1848, In this paper, we design our FCGS model based on this SH-based rendering structure of 3DGS.
Discussions. The attributes of Gaussians can be categorized into geometry and color attributes. The geometry attributes (opacity a. diagonal elements of scaling S, and quatermion representation of rotation R) determine the dependencies of the rasterization process (e.g., the coverage range of the Gaussians the depth which they should be composed). Once the geometric dependencies established, the color attributes are used to assign colors via 3-degree SH following (Kerbl et al., 2023 Since the geometry attributes directly influence the rasterization dependencies, they more sensitive to deviations than the color attributes. For brevity, we denote geometry attributes as f R$, color attributes as fcol R248, and the concat of both as feam E 1256 In the following sections, unless otherwise specified, x refers to either f or fcol
3.2 VALUE-BASED PRINCIPLE: MULTI-PATH ENTROPY MODULE (MEM)
As shown in Figure we draw inspiration from image compression and apply an autoencoder-bast compression approach with hyperprior structure, where the Gaussian attributes are projected into a quantized latent space ý for compression. After obtaining ÿ through the arithmetic decoding, a synthesis transform, gs employed to project it back to the original space á which is the decoded
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version. However, in 3DGS representations, Gaussians are not the final output; the actual results are the rendered images obtained through rasterization. Unfortunately, deviations in Gaussian at- tributes can be amplified during the rasterization process, leading to even greater deviations in the rendered images. Some Gaussians are highly sensitive to these deviations, as they may occupy cru- cial positions that significantly affect the rasterization process. Simply feeding all attributes into the autoencoder does not yield satisfactory results: The forward pass of MLPs is inherently non- invertible due to non-linear activation functions, which means projecting into latent space via an MLP does not allow for exact recovery of the original x from the latent. As result, attributes & cannot always align precisely with the original attributes for each Gaussian. This mis- alignment would be amplified in rendering. leading to significant fidelity drop in the rendered images, as exhibited in the ablation study (Subsection
Geometry attributes are the most sensitive to such deviations because they directly impact depen- dencies during rasterization. For example, the decoded opacity of a Gaussian is too large, it could block Gaussians behind it, preventing them from contributing they should. Similarly, deviations in scaling and rotation can have significant effects. To address this, we remove Ia and 9s for all geometry attributes f.oh ensuring that y deviations, although they still impact the final result. For foo as depicted in Figure 2 we introduce MEM to deduce a binary mask E R that adaptively determines whether æ (only for foo here) should pass through the MLPs fa and Is to eliminate redundancies, resulting in y = (a()), or simply bypass the MLPs, setting y x to best preserve the original information,
Fortunately, color attributes
less sensitive to
é 9. (Z()((z))) x m; + Q(x;) x (1 m.), my = (Mg(MLPm
> cm
(3)
where represents the quantization operation with quantization step of q: if y; is in the latent space (m, = 1), is 1; otherwise (m, = 0), q becomes a trainable decimal parameter. During training, uniform noise is added, while during testing, quantization is applied. Sig is the Sigmoid function and 6m is hyperparameter that defines the threshold for binarizing the mask. Gradients of
the binarized mask m are backpropagated using STE (Bengio et al. 2013 Lee al. 2024).
Balancing the mask rate is crucial, as it directly impacts the RD trade-off. Previous works like Lee je al.) (2024) incorporate an additional loss term with a hyperparameter Am. to regulate the mask rate. However, given that the RD loss already includes a trade-off hyperparameter À to balance bits and fidelity, introducing another parameter, Am. would unnecessarily complicate the process of finding the optimal RD tradc-off. To resolve this, we integrate the mask information directly into the bit consumption calculation, allowing the model to adaptively learn the optimal mask rate, thereby eliminating Xm Please refer to Subsection 3.4 for details.
3.3 PERUCTURR-BASEE PRINCIPLE: AUTOREGRESSIVE CONTEXT MODELS
To further enhance the accuracy of probability estimation, context models play a crucial role for ÿ. Specifically, a portion of ÿ is first decoded and then used to assist in predicting the remaining part based contextual relationships. In image compression, where data like a structured grid (i.e., a feature map), designing such context models is relatively straightforward. However, Gaussians in 3DGS are sparse and unorganized (as is ÿ), making it a challenge to design efficient and lightweight context models. To tackle this, we analyze the unique properties of Gaus- sians and develop customized inter- and intra- Gatrs-Gaussian context models, as illustrated in Figure 3
arranged in
Inter- Gaussian Context Models. 3D Gaussians collectively represent the scene, leading to inherent relationships among them. To uncover these relationships, we refer to grid-based structures, which are typically compact, organized, and capable of constructing spatial connections among Gaussians. While this "grid" structure does not naturally exist within 3DGS, we propose an innovative method to create grids from Gaussians themselves. Note that a concurrent work Wang et al. (022bb) pro- poses structuring anchors into multiple levels to model entropy. However, its anchor location quanti- zation process introduces deviations, which impacts fidelity and requires finetuning, Differently, our Mater-Gaussian context model overcomes this limitation by creating grids in a feed-forward manner for context modeling, without modifying Gaussian locations, eliminating the need of finetuning.
We divide all Gaussians' ÿ into No batches (N^ = 2 as an example in Figure B mid) and decode each batch sequentially. During this process. the previously decoded ÿ E Y Moo.- are first used to create grids, which then provide context for the to-be-decoded 인 E Y|=e via interpolation. Here, W [ee] refers to the set of ý belonging to the -h batch out of No. To generate the feature fo for
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Hyperprior
Prob
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factorized
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Figure 3: Context models of FCGS. We build on the hyperprior design of image compression (top)
and introduce our inter- and intra Gaussaan context models (mid & bottom). Together, they form
GMM that provides more accurate estimation of the value distribution probability of ý (right).
voxel at position v on the grids, we investigate ÿ within v o's interpolation range. For a voxel located at v, R (i.e., the red point in Figure 3 mid (b)), its feature f." is computed as follows:
Σ EkiLLEr (0--)
wkyk
f. Eniney (
where Wi
II dime (2.g.=)
(1 (1-1es V...im))
(4)
wkk
where Y [00,--1] denotes the subset of Y 10 where ý fall into v, interpolation range (i.e., the orange box in Figure mid (a,b,c)). mines the contribution of each ÿt:- The closer ÿ is to the greater its contribution. Regardless of the number of ý within v, 's interpolation range, we can always compute a weighted average feature J for v; that integrates information for interpolation in the next step.
the index of ÿk. and wy represents the weight that deter-
By conducting this calculation for all the voxels, we can create the grids, which are organized and structured, enabling them to provide contextual information for any input coordinates via interpola- tion. For a lo-be-decoded ÿ. E Y [nt) located at u its distribution parameters p oj, and лद can be computed from these grids using MLP,;
pc o - MLP.(OIFT) emb (mb))))), where ft
Σ lonp EV"!
wIFF
(5)
where Vul represents the set of voxels forming pe's minimum bounding box (i.e,, the 4 red points in Figure mid (c)), and f! is the feature obtained by grid interpolation. emb and O are sin- cos positional embedding and channel-wise concatenate operation, respectively. The calculated parameters 1 oj, and π. are then used to estimate the distribution of ÿ; in GMM.
In practice, we use both 3D and 2D grids, designed with multiple resolutions to capture varying levels of detail. The 3D grids effectively represent the spatial relationships between the Gaussians and the voxels, though they are of lower resolution due to the higher dimensionality. To complement this, we employ three 2D grids, each created by collapsing one dimension. These 2D grids provide higher resolutions, allowing for
precise capture of local details.
Intra-Gaussian Context Models. Beyond addressing redundancies across Gaussians, we further utilize our FCGS to eliminate redundancies within each Gaussian. Specifically, split each Ú; into Ne chunks, and MLP, is used to deduce the distribution parameters for each chunk from the previously decoded ones. For path dimension. For path m = 0, is split into N = 3 chunks along RGB axis to leverage redundancies of color components. We do not apply intra-context for f as internal relations are trivial.
in MEM, we split ý into No chunks along channel
(6) where n' and are chunk index and channel amount per chunk, respectively. f of iine: T are the intermediate probability parameters for chunk n", with dimension size of c for each.
o -. = 0 f= o ,n T Ti (.n) where f oi T. = MMLPa(@...oo
Gaussian Mixed Model (GMM). addition to the context models, hyperprior also outputs a set of distribution parameters j.b ai and лe from é.. To this end, we introduce GMM to represent the

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final probability combination of these 3 sets of Gaussian distribution parameters. For any channel j. its probability can be calculated as combination of cumulative Gaussian distribution Q.
0's (BLins + L' o(s) Lii d (nin)ott)) ... E exp(m
exp(Ti,)
p(i).))
Σ
(b.5.0))
(7)
Note that for geometry attributes f 1 E {h,s). GMM adaptively weights the mixing of the three sets of distributions, providing more accurate probability estimation. For & in the hyper- prior branch, we follow Ballé et al. (2018) to use a factorized module to estimate its p(&r.). According to information theory (Cover 1999). bit consumption of z; can be calculated: bit, = Σ D' loga p(ūts)) + Σ3 (-logz pl (E)))). D and D³ are number of channels of ÿ: and 2,
3.4 TRAINING AND CODING PROCESS
Training loss. After obtaining & from ý through the synthesis transform 9s. we can evaluate the fidelity between the ground truth I and the image i rendered from á. To balance the mask rate of fool MEM, we include the mask m directly in the bit calculation process, rather than introducing an additional loss term. Specifically, we forward all Gaussians' f in both MEM paths and use m to compute the weighted sum of the output bit from both paths, ensuring the gradients can be backpropagated for both. The overall loss function is:
Na
L = Lidelity (i,1) + X
Tentropy N8 x 56'
where Lentropy
Σ
bitg + m,bbit, Smbbiisllmm.. + (1- mit,
col
where No represents the amount of Gaussians, 56 is the dimension of fae and they collectively represent the amount of attribute parameters. bitg and bit represents the bit consumption for Gaussian's geometry and color attributes, respectively, with æ; set as f:00 or f.aal respectively. is hyperparameter controlling the trade-off between fidelity and entropy. The fidelity loss, Liaelity includes both MSE and SSIM metrics to evaluate the reconstruction quality of at Minimizing this loss encourages z to closely resemble the original æ while also reducing the bit consumption.
Encoding/decoding process. For encoding, the attributes f and fool are encoded using Arith- metic Encoding (AE) (Witten et al. 1987) in the space of and & given the corresponding proba- bilities, masks m are binary and encoeded using AE based on the occurrence frequency of 1, and Gaussian coordinates μæ8 are 16-bit quantized and encoded losslessly using GPCC (Chen et al, 2023]. For decoding, Gaussian coordinates μu, masks m, and hyperpriors are decoded first. Then, to decode (i U: Y [[e]: its value distribution is calculated using GMM based on: ue sinnes o i,nes The from ÿ E [0. using inter-Gaussian context models (channel sliced), and ue i ge in TT from wcce), ÿt E W(n) using intra-Gaussian context models, and ph,,e> onee whns from &; using hyperprior (channel sliced). On obtaining GMM, it is decoded using AD.
EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
Implementation. Our FCGS model is implemented using the PyTorch framework (Paszke et al., 2019) and trained on single NVIDIA L40s GPU. The dimension of ý set to 256 for color (m = 1). For 2, dimensions are set to 16, 24, and 64 for geometry, color (m = 0), and color (m- 1), respectively. Grid resolutions are {70, 80, 90) for 3D grids and (300, 400, 500) for 2D grids. We set N to 4, using uneven splitting ratios of ( 8. 3.3/ 3/. with uniform random sampling. 6n set to 0.01. guarantee consistency. The training batch size is 1 (iee, one 3DGS scene per training step). We adjust À from le 4 to 16e 4 to achieve variable bitrates. During training, we first train Ja and gs of using only the fidelity loss to ensure satisfactory reconstruction quality. We then jointly train the context models for color with m = 1, and finally, train the entire model in an end-to-end manner. We adopt this training process because the gradient chain for the m path is shorter than that of the m path. Without sufficient pre- pre-training of the m = 1 path, the model is prone to collapsing into a local minimum where all m values are zero
111
inference, we maintain same random seed encoding and decoding to
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DL3DV-GS
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Figure 4: Performance comparison. Each scene is initially trained for 30K iterations to produce the vanilla 3DGS. Methods marked with * and circle are finetuned from this common 3DGS (our FCGS also compresses the same 3DGS); Methods marked with and triangles are trained from scratch due to their modification to structures. We also present the runtime of our method and other approaches at the bottom of the figure (which is also reflected by the size of the marks), where our approach requires significantly less time for compression. For our runtime, it means using multiple/single GPUs. Thanks our optimization-fre pipeline, we divide the 3DGS into chunks, with each chunk containing million Gaussians, allowing us to easily encode these chunks in parallel using multiple GPUs, further speeding up the process.
267511444
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Figure 5: Qualitative comparison. We achieve substantial size reduction while preserving high fidelity. PSNR (dB) SIZE (MB) are indicated the bottom-right corner. We only present two baseline methods for qualitative comparison here due to space limitation. Please refer to Appendix Section N for more comprehensive qualitative comparisons.
Training Dataset. FCGS requires training on large-scale dataset with abundant 3DGS. To achieve that, we refer to DL3DV dataset (Ling al. 2024), which contains approximately 7K multi-view scenes. We train these scenes to generate their 3DGS at a resolution of 960P, which takes about 60 GPU days on NVIDIA L40s. After filtering out low-quality ones, obtain 6770 3DGS, and randomly split 100 for testing and the remaining for training. This dataset is referred to as DL3DV- GS. For more details regarding DL3DV-GS, please refer to Appendix Section A
Metrics. We assess compression performance in terms of fidelity relative to size. Herein, we present PSNR metric evaluate fidelity due to page constraints. For additional metrics (SSIM (Wang al.) 2004) and LPIPS ZZhang et al. 2018)), please refer to Appendix Section F
4.2 EXPERIMENT EVALUATION
For 3DGS from optimization, FCGS enables rapid compression. Since prior works customize optimization-fret compression for 3DGS, direct comparisons are unavailable. This leaves us to benchmark against optimization- Nonetheless, we achieve excellent RD performance despite this lack of optimization. Furthermore, FCGS can also compress 3DGS generated by feed-forward models (Chen et al. 2024c 2024), demonstrating its versatility.
methods, comparison that is inherently unfair to FCGS.
Tang al.
For 3DGS from optimization, we employ DL3DV-GS, MipNeRF360 Barron et al. 2022), and Tank& Temples (Knapitsch et al. 2017) for evaluation. For the baseline methods, we compare those built upon the vanilla 3DGS structure, including both finetune from existing 3DGS (Navaneet al.
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Figure 6: Compression of 3DGS from feed-forward models. Left: MVSplat is a generalizable reconstruction model, whose fidelity is measured between the rendered images and the ground truth. Right: LGM is a generative model, whose fidelity is measured between images rendered from 3DGS before and after compression, as ground truth cannot be measured due to its generative nature.
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Figure 7: Ablation studies on the DL3DV-GS dataset. Left: Setting m all results in significant increase in bit consumption. Conversely, setting to all is leads to drastic drop in fidelity, even without applying quantization or entropy constraints to y. Right: Excluding the proposed context models leads to substantial increase in bit consumption, due to the absence of mutual dependencies.
Niedermayr al. 2024, Fan al., 2024) or train from scratch (Mrgenstern et al., 2023 Girish et al.) 2024 Lee et al.) 2024p. The results are in Figure E Although our FCGS lacks per- scene adaptation, which naturally puts us at a disadvantage for unfair comparison, still surpasses most optimization-basel methods, thanks to the effectiveness of MEM and context models. The qualitative comparisons are shown in Figure which exhibit high fidelity after compression. Please refer to Appendix Section D and G to find discussions on the SoTA compression methods and the detailed analysis on the storage size of each component, respectively.
2024
For 3DGS from feed-forward models, also demonstrate compression capability. To evaluate our performance, we refer to MVSplat (Chen et al, 2024c) and LGM (Tang et al., 2024). MVSplat a generalizable model that reconstructs interpolated novel views given two bounded views. LGM is a generative model that creates the 3D scenes from the 4 multi-view images. We follow LGM to generate the remaining 3 views from the initial 1 view using (Wang & Shi, 2023), which are then collectively input into LGM for 3DGS creation. This process results in ground
cannot be mea-
sured for LGM, thus we evaluate the compression fidelity by measuring the similarities of images rendered from 3DGS before or after compression. We utilize 10 scenes from ACID (Liu et al. 2021b and scenes from Gobjaverse (Qiu et al., 2023) Deitke et al., (022) for these two models for evalu- ation. The results are shown in Figure o Although trained on 3DGS from optimization (Kerbl al.] 2023), FCGS still generalizes in a zero-shot manner to sord-foowwrd-basee 3DGS, achieving com- pression ratios of 15x and 5x. Notably, when compressing 3DGS from ferd-forward models, we set mask m to all Os for color attributes. Please refer to Appendix Section B for limitation analysis.
4.3 ABLATION STUDY
We perform ablation studies to evaluate the effectiveness of our proposed MEM and context mod- els. First, we investigate the MEM module, which adaptively selects high-tolerance color attributes

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Deep Blending
Figure 8: Building on pruning approaches, FCGS can further boost compression perfor- mance. The size of the compressed 3DGS ing FCGS (at the highest rate) is only 40% that of MSC with the same fidelity. When com- bined with pruning techniques, FCGS achieves compression ratio of 100x over the vanilla 3DGS (see Muri** Experiments conducted on the Deep Blending dataset. The x-axis uses Log scale.
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to eliminate redundancies via an autoencoder structure, while ensuring all geometry attributes are directly quantized. Without MEM, we observe the following: If both color and geometry tributes are processed through the autoencoder, the model collapses because rasterization cannot be performed correctly due deviations in the geometry information. 2) Fixing the geometry attributes to be directly quantized, we allow all color attributes to be processed by the autoencoder (i.e., all m = =). As shown in Fig 7 left, even without quantization or entropy constraints on y, the ren- dered images suffer from a significant drop in fidelity due to deviations brought by MLPs. 3) On the other hand, if all color attributes are directly quantized (i.e., m 0), their redundancies are not effectively eliminated, leading increased storage costs.
Next, we explore the impact of our context models. As demonstrated in Figure 0 right, removing intra- and Inter-Gaussian context models progressively decreases RD performance. Compared the full FCGS model, the bit consumption 1.5x for the base model under similar fidelity conditions.
4.4 BOOST EXISTING COMPRESSION A APPROACHES
sometimes struggles with densification issues, leading to suboptimal fidelity. Pruning techniques effectively eliminate trivial Gaussians, enhancing fidelity while reducing size, particularly evident with the Deep Blending dataset (Hedman et al., 2018 Im- portantly, FCGS is compatible with these pruning techniques. We refer to Trimming (Ali et al. 2024) and Mini-Splatting (Fang & Wang 2024). both of which apply pruning to Gaussians. As shown in Figure FCGS significantly boosts their compression performance. Notably, MSC is straightforward optimization- free compression tool utilized in Fang Wang 2024). We compare the compression performance of FCGS and MSC by directly applying them to the same pruned 3DGS representations. The superior compression performance of FCGS underscores its signifi- cance. Please refer to Appendix Section M to find the results on the other three datasets.
The vanilla 3DGS Kerbl
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4.5 CODING AND RENDERING EFFICIANCY ANALYSIS
Our coding time includes GPCC CCen et al. 2023) coding for coordinates μ8l and arithmetic coding for attributes f800 and fool and masks m. average, FCGS takes about 1 second encode 100K Gaussians when running on single GPU. Please refer to Appendix Section H to find the detailed analysis on coding time. The rendering time of the decoded 3DGS is consistent with that before compression since FCGS does not alter the number structure of the Gaussians. For instance, the average FPS is 102 and 91 before and after compression (A = le - 4) on the MipNeRF366 dataset.
5 CONCLUSION
In this paper, we introduce a pioneering generalizable optimization-free compression pipeline for 3DGS representations and propose our FCGS model. FCGS enables fast compression of existing 3DGS without any finetuning, offering significant time savings. More importantly, we achieve im- pressive RD performance, exceeding 20x compression, through the meticulous design of the MEM module and context models. Our approach can also boost compression performance of pruning- based methods. Overall, this new compression pipeline has the potential significantly enhance the widespread application of 3DGS compression techniques due to its numerous ad advantages.
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ACKNOWLEDGEMENT
The paper is supported part by The National Natural Science Foundation China (No.62325 02325109, U21 U21B2013). MH is supported by funding from The Australian Research Council Discovery Program DP230101176.
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Reviews:
[{"review_id": "7ZmyYuwZjC", "reviewer": "Reviewer_xSne", "summary": "The paper proposes a new compression model for 3D Gaussian Splatting (3DGS) representations. It aims to reduce compression time in an optimization-free pipeline, which is different from existing work. It introduces a Multi-path Entropy Module (MEM) to adaptive control the compression size and quality. It also designs inter- and intra-Gaussian context models to enhance compression efficiency. Experiments on multiple datasets show a large compression rate and high image quality.", "questions": "1. Can authors provide visualization of inter- and intra-Gaussian context models on real input data. It would help reader better understand how this method work. \n\n2. Will the code or data open-source for reproduction ? the training set is interesting and would benefit many other tasks.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. It proposes a novel optimization-free pipeline for 3DGS-based compression.\n2. MEM module to adaptively control size and fidelity.\n3. It also designs inter- and intra-Gaussian context models to enhance compression efficiency. \n4. The proposed method achieves a high compression rate and surpasses the existing optimized-based methods in various experiments.", "weaknesses": "1. The training data is very large, and it takes 60 GPU days to get. Per-scene optimization does not need such an amount of training data. Will smaller data hurt the effectiveness of the feedforward model?\n2. More discussion of failure cases. it could be useful to discuss / show scenarios where the method performs poorly.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Hd1MTTy8Be", "reviewer": "Reviewer_VFpS", "summary": "This paper presents a novel, optimization-free compression pipeline for 3D Gaussian Splatting (3DGS) representations, called FCGS (Fast Compression of 3D Gaussian Splatting). FCGS uses a single feed-forward model to compress 3DGS representations, drastically reducing compression time compared to pruning and optimization-based approaches. The paper further details the Multi-path Entropy Module (MEM) and context models that FCGS employs to balance size and fidelity during compression. Finally, the authors demonstrate the efficacy of FCGS through extensive experiments, achieving a compression ratio exceeding 20x while preserving high fidelity.", "questions": "- What are the analysis transform and synthesis transform exactly? Architecture? Space? \n- Why can \"the potential nonlinearity of the MLPs still cause considerable deviations in decoded gaussians\", line 222?\n- How are the the set of Gaussian split and what is the criterion to split them?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper provides a complete overview of related works on 3DGS with compression and structure it according to the methodology, see Section 1 and 2.\n- The overall goal of the paper is clear and the authors motivate compression potential for 3DGS scenes well.\n- The quantitative evaluation is very good, as the authors provide a comparison to several relevant methods, like light gaussians and Niedermayr etal.,  on three different datasets (MipNeRF360, T&T and DL3DV-GS) and present results in expressive graphs that highlight the trade-off of size and quality, see Figure 4.\n- In addition to comparisons to compression works, the author further extent t0 feed forward reconstruction models and find a more efficient representation of their approach.\n- Overall the experiments indicate that the proposed methods is an effective approach for compression performs favourable to most other approaches in terms of (PSNR/SIZE), however less effective than, Niedermayr et al.. See Figure 4.", "weaknesses": "- The methodology is very complex and the respective section in the paper is hard to understand even for someone familiar with 3DGS. The reason is that there is information missing at the appropriate position in the text. For example, figure 2 in line 262 is is unclear how the Gaussians are divided into batches (randomly, per grid cell stays unclear). Hence it appears to hard to reproduce. Please see the Questions sections for more open points.\n- The method's focus lies very much on increasing the compression speed and it does not improve over existing methods in terms of PSNR/Size, see Figure 4 (Simon*). One could argue that there is limited interests in compression speed, since a 3D GS reconstruction takes several minutes to hours and a follow up compression of a few minutes (e.g. Niedermayr et al.) does not impact the overall reconstruction time significantly.\n- For qualitative evaluation, I'd have expected more visual comparisons to baseline methods. There is only comparisons to 3DGS in paper and supplementary material. Please provide more qualitative evidence.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HPIX43AazP", "reviewer": "Reviewer_SSjz", "summary": "This paper proposes a generalizable 3D Gaussian compression framework incorporated with an effective entropy minimization module inspired by 2D image compression. The main contribution of this work is compressing 3D Gaussians within a minute, using a feed-forward pipeline. To achieve this, the authors first divide Gaussian attributes into a) geometry attributes, and b) color attributes. Then, these attributes are encoded and decoded using hyper-priors from inter-/intra-Gaussian context models, along with context design from the image compression approach. Consequently, this paper achieves a compact size and comparable rendering quality compared to the existing compression method, while requiring only several seconds.", "questions": "- How much time is needed for encoding and decoding parameters?\n- It could be helpful to add compression performance for each attribute by demonstrating the storage size of each attribute before and after compression. (or storage size of color attributes and geometry attributes)\n- How many scenes are required to acquire a feasible compression performance?\n- An optimization-based compression work, ContextGS [1], has already applied an image compression-based pipeline for 3DGS using hyper-prior similar to the design of inter-Gaussian context models. However, the authors do not need to consider it, since ContextGS has not been published yet, accepted to NeurIPS 2024. Therefore, it would be helpful to mention this paper as a concurrent work.\n\n\n(+minor question)\n- Do you have any plans to release DL3DV-GS, which is a training dataset for this work? This could be helpful for various generalizable 3DGS approaches, including compression.\n\n\n---\n### Reference\n[1] Wang et al., ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model, NeurIPS 2024", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- This paper suggests a novel framework that efficiently compresses the 3D Gaussian in a feed-forward manner, whereas existing optimization-based compression methods require additional adaptation steps.\n\n- Experimental results demonstrate that the framework achieves high compression performance with comparable rendering quality with existing compression methods.\n\n- The authors validate that the proposed hyper-prior design, including inter-/intra-Gaussian context models, further improves the compression performance.", "weaknesses": "- Despite the effectiveness, the design of context models comes from heuristic priors that the Gaussians share inter-/intra-information. It could be helpful to show those relations of Gaussians to understand the justification of the proposed hyper-prior designs.\n- There is no ablation on the strategy for dividing chunks in the inter-/intra-Gaussian context model. It could be helpful to show the results for several splitting designs.\n- Due to arithmetic coding-based compression, it might yield notable computational costs to encode and decode the parameters.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "S3jy7TZQaf", "reviewer": "Reviewer_SkMb", "summary": "This paper introduces an optimization-free model, called FCGS, for 3D Gaussian Splatting (3DGS) compression, which allows compressing an optimized 3DGS representation in a feed-forward process. The model incorporates both intra- and inter-Gaussian context information. The authors project most Gaussian attributes into a latent space and utilize arithmetic encoding and decoding for data compression. Additionally, they employ an end-to-end progressive training scheme for the proposed pipeline.", "questions": "It seems that the process of generating m by MLP_m is not illustrated in Figure 2. By reading the method section, it is somewhat difficult for readers to understand the role and position of m in the pipeline (although there is a formula provided). It is suggested to draw a more detailed pathway in Figure 2.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "As far as I know, this is the first generalizable optimization-free 3DGS compression pipeline. Without the need of  original  training images, this generalizable compression method enables the compression of 3DGS, expanding the application scope of 3DGS compression algorithms.  \n\nThis article provides a novel approach to context modeling for exploring the correlations between Gaussians.", "weaknesses": "I find the experimental content of this paper somewhat disorganized and difficult to follow. The authors present the main comparative experimental results using figures, but there are no numerical comparisons in the main paper. Although detailed data is provided in the Appendix, the absence of tables in the main paper causes some inconvenience for readers. \n\nAdditionally, in Tables C-D, the authors report the running time, but I believe this comparison may not be entirely reasonable. The running time for methods that involve training from scratch includes the time for reconstruction. FCGS also requires a pre-trained 3DGS, and the time spent pre-training the 3DGS should be reflected in the table. \n\nIn the comparative experiments, the authors only compare methods with worse rendering quality than FCGS. However, there are existing 3DGS compression methods that offer better rendering quality and higher compression rates than FCGS. While these methods are not generalizable, readers should still be informed of the current gap between FCGS and the state-of-the-art.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)