Temporal Smoothness-Aware Rate-Distortion Optimized 4D Gaussian Splatting
Authors: Hyeongmin Lee
Kyungjune Baek
Venue: NeurIPS 2025 poster ()
Keywords: compression
video
4D
gaussian splatting
Primary area: applications
TL;DR: We introduce the first rate-distortion optimized compression framework for 4D Gaussian Splatting, leveraging temporal smoothness via wavelet transforms to achieve up to 91× compression with minimal loss in visual fidelity.

Abstract: Dynamic 4D Gaussian Splatting (4DGS) effectively extends the high-speed rendering capabilities of 3D Gaussian Splatting (3DGS) to represent volumetric videos. However, the large number of Gaussians, substantial temporal redundancies, and especially the absence of an entropy-aware compression framework result in large storage requirements. Consequently, this poses significant challenges for practical deployment, efficient edge-device processing, and data transmission.
In this paper, we introduce a novel end-to-end RD-optimized compression framework tailored for 4DGS, aiming to enable flexible, high-fidelity rendering across varied computational platforms.
Leveraging Fully Explicit Dynamic Gaussian Splatting (Ex4DGS), one of the state-of-the-art 4DGS methods, as our baseline, we start from the existing 3DGS compression methods for compatibility while effectively addressing additional challenges introduced by the temporal axis. In particular, instead of storing motion trajectories independently per point, we employ a wavelet transform to reflect the real-world smoothness prior, significantly enhancing storage efficiency.
This approach yields significantly improved compression ratios and provides a user-controlled balance between compression efficiency and rendering quality. Extensive experiments demonstrate the effectiveness of our method, achieving up to 91$\times$ compression compared to the original Ex4DGS model while maintaining high visual fidelity. These results highlight the applicability of our framework for real-time dynamic scene rendering in diverse scenarios, from resource-constrained edge devices to high-performance environments. The source code is available at https://github.com/HyeongminLEE/RD4DGS.

Temporal Smoothness-Aware Rate-Distortion Optimized 4D Gaussian Splatting
Hyeongmin Lee Twelve Labs lucas@twelvelabs.. io
Kyungjune Baek*
Sejong University kyungjune www.eeeeeee.a.....eee.... ac
Abstract
Dynamic 4D Gaussian Splatting (4DGS) effectively extends the high-speed render- ing capabilities of 3D Gaussian Splatting (3DGS) to represent volumetric videos. However, the large number of Gaussians, substantial temporal redundancies, and especially the absence of an entropy-aware compression framework result large storage requirements. Consequently, this poses significant challenges for practical deployment, efficient edge-device processing, and data transmission. In this paper, we introduce novel end-to-end RD-optimized compression framework tailored for 4DGS, aiming to enable flexible, high-fidelity rendering across varied computa- tional platforms. Leveraging Fully Explicit Dynamic Gaussian Splatting (Ex4DGS), one of the statc-of-the-as 4DGS methods, as our baseline, we start from the ex- isting 3DGS compression methods for compatibility while effectively addressing additional challenges introduced by the temporal axis. In particular, instead of stor- ing motion trajectories independently per point, we employ a wavelet transform reflect the real-world smoothness prior, significantly enhancing storage efficiency. This approach yields significantly improved compression ratios and provides a user-controlled balance between compression efficiency and rendering quality. Extensive experiments demonstrate
effectiveness of our method, achieving up to 91 x compression compared to the original Ex4DGS model while maintaining high visual fidelity. These results highlight the applicability of our framework for real-time dynamic scene rendering in diverse scenarios, from essource-constraing edge devices to high-performance environments. The source code is available at /tttps:/gittuut com htwmm//yeoogmin////RDDDOO0
Introduction
The advent of 3D Gaussian Splatting (3DGS) [1] marked significant step in real-time radiance field rendering, offering superior speed and simpler training compared to Neural Radiance Field (NeRF)-basee approaches [2] 3 4 5 6 77. Furthermore, the Gaussian Splatting framework is not restricted to static scenes but has been effectively extended represent dynamic, or 4D, scenes such as videos by capturing temporal variations [8] 9
[3]
Dynamic 4D Gaussian Splatting (4DGS) methods typically require a substantial number of Gaus- sians and extensive Spherical Harmonics (SH) parameters to represent volumetric video accurately. Additionally, dynamic points in these methods are often stored independently per timestamp, inher- ently causing significant temporal redundancies. Consequently, such characteristies lead to large models and excessive storage demands, severely restricting applicability in resource-constraaine scenarios, including edge computing and efficient data transmission. Although several existing techniques [14 15
have explored model size reduction, there is currently no entropy-aware, bit-level compression method for 4DGS that enables flexible rate-distortion optimization.
"Correspondig Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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In this paper, we propose a novel rate- ante-distortion (RD) optimized compression method specifically designed for 4D Gaussian Splatting (4DGS), enabling real-tims applications across diverse computing environments, ranging from edge devices to general computing platforms. We adopt Fully Explicit Dynamic Gaussian Splatting (Ex4DGS) [8] as our baseline model and introduce a novel approach that leverages inherent video characteristics beyond traditional RD-optimization. Specifically, fol- lowing EX4DGS, we decompose the scene into static and dynamic components. For components common to both 3DGS and 4DGS, we apply masking and vector quantization after model training, following the approach of Wang et al. [1]]. Although Ex4DGS reduces redundancy by storing dynamic points only at selected keyframes instead of every frame, the positions at these keyframes stored independently, inherently limiting the exploitation of temporal redundancy and thus reducing compression efficiency. Instead of utilizing the compression method for 3DGS directly, we leverage the prior that each point moves along a smooth trajectory. To this end, we apply a novel wavelet transform each point's trajectory and discard high-frequency detail components, significantly reducing storage without severely compromising fidelity. Additionally, we conduct a fine-grained analysis, evaluating the rate- dit-oiitootion impact of quantizing individual dynamic opacity elements. Based on this analysis, we selectively apply quantization only to those elements that offer a favorable trade-off between compression rate and rendering quality.
Our method achieves model sizes as low as only 1.1% on average of the original Ex4DGS model while maintaining reasonable visual fidelity. Furthermore, our framework allows users to flexibly control the trade-off between distortion and data rate based on specific scenario requirements.
Our contributions can be summarized as follows:
We propose novel end-to-end rate-distortion optimized compression framework specifically designed for 4D Gaussian Splatting representations, using Ex4DGS as the baseline.
To the best of our knowledge, this work is the first to introduce bit-level rate-distortion optimizable compression framework for dynamic 4D Gaussian representations.
We apply the pointwise wavelet transform to further compress the position trajectories of dynamic Gaussian points, effectively leveraging temporal redundancy inherent in 4D representations.
Our method achieves significant compression ratios (up to 91 x) for dynamic scenes while main- taining reasonable visual fidelity and offering flexible control over the rate-distortion trade-off suitable for diverse computing platforms.
2 Related Work
2.1 Neural Rendering
Neural Radiance Fields (NeRF) introduced novel view synthesis approach by learning density and color via a multi-layer perceptron from 3D coordinates and viewing directions (2). Despite their effectiveness, NeRF-based methods suffer from slow rendering and training speed. Follow-up works accelerated rendering by employing multi-resolution hash grids (00 or optimizing sparse voxel grids directly (21), though often at the expense of increased memory usage.
3D Gaussian Splatting (3DGS) [ emerged fast and memory efficiiet alternative by representing scenes with anisotropic 3D Gaussians rather than dense voxels or networks. By optimizing the parameters, including mean, covariance, and color, 3DGS achieves real-time rendering at high resolutions using efficient algorithms instead of volumetric ray marching. Recent studies demonstrate that 3DGS matches or surpasses NeRF in rendering quality, speed, and scalability.
Dynamic scene modeling extends this idea into 4D Gaussian Splatting (4DGS), incorporating time as an additional dimension. Dynamic NeRF methods 22 learn deformation fields or multi-space embeddings to handle scene motion, while others utilize representations like temporal tri tri-planes for compression 233,. but they remain computationally heavy and slow to render. In contrast, 4DGS 88 [0] was introduced as unified representation for spatiotemporal data, using a set of 4D Gaussians with per-Gaussian motion trajectories or a deformation network to render moving scenes in real time. Overall, Gaussian Splatting has rapidly become promising alternative to NeRF, offering comparable quality with significantly improved rendering speed, which is crucial for interactive applications.

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2.2 Gaussian Splatting Compression
Explicit scene representations like 3D Gaussian Splatting (3DGS) enable real-time, high-fidelity rendering but inherently introduce substantial memory requirements. These arise from the need to store numerous Gaussian primitives, each characterized by parameters for position, shape, opacity, and color (often represented by complex spherical harmonics). This significant memory footprint has spurred the development of various compression techniques specifically designed for 3DGS.
Recent approaches tailored for compressing 3DGS include Compact3DGS (24 which employs trainable masking to prune redundant Gaussians, CompGS (25), adopting vector quantization to store Gaussians, HAC++ [26] leveraging hash-grid mush-grid-assisterr entropy coding, and Context 3DGS (77) utilizing an autoregressive, anchor-based context model for hierarchical Gaussian compression.
Extending these techniques to dynamic scenes poses greater memory challenges due temporal variations. Recent specialized solutions include Light4GS [4), combining spatio-temporal pruning with entropy coding: 4DGS- IK [15], optimizing Gaussian reuse and visibility masks for ultra-fast fast rendering; MEGA [16). replacing spherical harmonics with lightweight predictors for significant compression; and LGS [17, which prunes Gaussians and deformation fields within framework highly specialized for surgical scenes using stereo vision. These methods highlight the growing focus on efficient and high-quality rendering of dynamic scenes. QUEEN [S] introduces integer- based quantization enabling entropy coding; however, it does not explicitly optimize entropy within rate-distortion framework, leaving rate-distortion optimization for dynamic Gaussian Splatting unexplored. Furthermore, QUEEN's primary objective of per-frame compression for streaming applications fundamentally differs from our goal of compressing the entire dynamic scene model for efficient storage.
2.3 End-to-End Rate-Distortion Optimized Compression
Cate-distortion (RD) optimization balances bitrate against reconstruction quality, core principle extensively used in traditional and neural compression methods. End-to-end image compression frameworks by Ballé et al. (28 introduced autoencoder-based models jointly trained to minimize distortion and bitrate, laying the foundation for differentiable compression. Follow-up studies
improved efficiency through advanced context modeling and latent priors (29 30 31].
Recently, RD-optimization concepts have been applied to neural scene representations. For example, Takikawa al. presented neural field that continuously adjusts detail level and bitrate via tunable parameter [32 Specifically for 3D Gaussian Splatting, Wang et al. [19] proposed an RD- optimization-basee framework that jointly optimizes Gaussian parameters with learnable pruning and entropy-constrarn quantization, achieving substantial reductions in model size with minimal quality loss. Such approaches highlight the benefit of integrating optiiiiiznn directly into the training process, enabling flexible compression tailored for diverse computational environments. While compression techniques exist for 4DGS and RD-optimization has been applied to 3DGS, dedicated end-to-end RD-optimized framework specifically for 4DGS, offering flexible control over the rate-quality trade-off by effectively exploiting spatiotemporal structure, is still lacking. In this work, we develop novel method to apply end-to-ene RD- RD-optimized compression to the 4D domain, which is related to spatiotemporal representation.
Method
3.1
Preliminary
In this section, we first introduce the fundamental concepts behind 3D Gaussian Splatting (3DGS) [ We then briefly describe its extension to 4D scenes and detail our baseline model, Ex4DGS [18], which explicitly models temporal dynamics. Finally, we outline the principles end-to-end rate-distortion optimization as applied Gaussian Splatting.
3D Gaussian Splatting. 3D Gaussian Splatting (3DGS) [1 represents scenes using explicit 3D Gaussian primitives. Each primitive is parameterized by a position (mean) μa E RR3 a covariance matrix Σ, 3x3 an opacity value a, E [0, 1). and view-dependent color represented by Spherical Harmonics (SH) coefficients c, The 3D Gaussian function is defined as:

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G(x) = -(x)= - (x-A.) (x-x)))))
The 3D covariance matrix Σ, defines the shape and orientation and is efficiently parameterized using a scaling vector 8; E R and a rotation quaternion qi E RR, such that Σ, = R.S.STR. where R, the rotation matrix derived from qi and S; is a diagonal scaling matrix derived from 8;. To render an image, these 3D Gaussians are projected onto the 2D image plane and then "splatted". The final pixel color C is computed by alpha-blending the contributions from N Gaussians sorted by depth:
N
1-1
C
Σ 1=1
C
I ==1
where 0; here denotes the projected 2D Gaussian's opacity, derived from ar and the 2D covariance. This differentiable rendering process allows for end-to-end optimization of all Gaussian parameters (H4;, S; q: a, c;) using gradient descent, typically initialized from a sparse point cloud obtained via Pencture-from-Moteee
(SfM). 3DGS employs adaptive density control, involving splitting, cloning, and pruning Gaussians during optimization.
4D Gaussian Splatting, 4D Gaussian Splatting (4DGS) 8 adapts 3DGS for dynamic scenes. We build upon Ex4DGS [18] which separates Gaussians into static (G.) and dynamic (Gu) components. Static Gaussians (G.) maintain constant covariance (E,), opacity (az), and SH coefficients (c,), with
only position (t,(t) changing linearly over the scene duration L:
{ (,(1) 140 T, ddis
where 14 is a pivot position and ddisp is the learned translation. Dynamic Gaussians (9.) use a keyframe approach, storing parameters only at sparse, uniformly sampled keyframes = {t
t t= ==nl, n E 2, E T), where T represents the set of all timestamps in the entire video sequence. Properties at intermediate times t are interpolated: position (a(() using Cubic Hermite Interpolation (CHip), quaternion rotation (t(t) using Spherical Linear Interpolation (Slerp), and temporal opacity (t) using a custom wwo-Gaussian mixture model (parameterized by means a,, ag and variances For dynamic Gaussians in Ex4DGS, scale (s,) and SH coefficients (c.) are typically assumed constant over time.
bg. 69) to handle appearancediisapparaaae
3.2 4D Dynamic Gaussian Compression
Building upon the Ex4DGS framework [8], we introduce an end-to-end compression framework specifically tailored for dynamic 4D Gaussian representations. We follow the general principles established by Wang et al. [19] for compatibility with 3DGS compression, extending them to accommodate the unique challenges presented by dynamic scenes.
Gaussian and SH Coefficient Masking. To reduce model complexity, redundant Gaussians pruned using learnable masks di A soft mask 6TTh = sigmoid (.) is generated, then binarized to a hard mask hard E {0, 1} using a threshold chtres and the straight-through estimator (STE); of hand sg([[ > +tues) Φ gaof Here, sg(.) denotes the stop- graddera operation, and I(-) is the indicator function. This hard mask is applied to key attributes like scale s; and opacity The associated pruning cost, contributing to the rate term Liate (the full formulation of which is detailed in Appendix A). is:
Lisprune
Σ son
N
Similarly, redundant SH coefficients are adaptively pruned using learnable masks ( per Gaussian i and degree 1 2 1. Soft masks (00 sigmoid (o!") are binarized to hard masks o! (1)ard via STE using a threshold Oe.. too. The hard mask applied zero out corresponding coefficients = (()aard ( The weighted pruning loss contributing to Lute is:
LSHpprume
21 + 1
8 (1)sff
(5)
N
(k + 1)2

---

where k is the maximum degree of the SHs. typically set to 3. This masking strategy is uniformly applied to both static and dynamic Gaussians, and experiments confirmed effective pruning across both categories. The results are available in Appendix D
SH Coefficient, Rotation, and Scale Compression. Spherical Harmonics (SH) coefficients, rotation, and scale parameters exist for both static and dynamic Gaussians. These parameters are compressed using entropy-constrannes vector quantization (ECVQ) [3], following Wang al. [19] Recognizing potential differences in compression sensitivity, scalar quantization is selectively applied.
Specifically, rotation parameters for dynamic Gaussians differ from static Gaussians in that they defined at each timestamp. Although storing residual rotations was initially considered, preliminary experiments indicated that the entropy gain marginal compared to the complexity of maintaining additional codebooks. Hence, we opted to store all rotation parameters directly.
Opacity. Unlike traditional 3D Gaussian methods, Ex4DGS parameterizes dynamic opacity tempo- rally by defining a scale function with center parameters (ag,a)) az) and variance parameters (bg, bi). effectively acting as an on/off temporal mask multiplied with a base opacity (a"). Given that the base opacity for dynamic points shares similar properties with the static opacity (ar), we follow Wang et al. [99] and apply scalar quantization to the base opacity. However, the temporal center and variance parameters, being expressed time units rather than opacity values, exhibit different sensitivities to quantization. Empirical tests indicated that variance parameters (bg, b9) were particularly sensitive to quantization artifacts, hence, we avoid aggressive quantization for these parameters to maintain visual fidelity. Detailed empirical validation of this decision presented in Section 4.3
3.3 Wavelet Compression for Dynamic Positions
To efficiently represent the motion trajectories of dynamic Gaussians (Ha(t) E TT*3), we employ single-level Haar wavelet transform along the temporal dimension. The wavelet transform is specifically chosen for its excellent time-frequency localization, enabling an efficient representation of motion trajectories that are generally smooth but may contain localized, high-frequency details (e.g., sharp turns or accelerations). Formally, given the dynamic positions μ4, [P1. Pp..P..... ..P) with Ps E 3 the wavelet transform is applied as:
F= Wp
F F. Fa]
(6)
Here, W denotes the Haar wavelet transform matrix, decomposing the trajectory into approximation (low-frequency) coefficients F. E R x3 and detail (high-frequency) coefficients F. R x3 Recognizing that coarse motion patterns captured by F. are perceptually more significant, we explicitly discard the detail coefficients Fa by zeroing them out. And the compressed trajectory يu is then reconstructed via the inverse Haar transform, which is equivalent to multiplying by the transpose of W due to its orthogonality:
F masked
Fa OJ,
W F musked
This approach significantly reduces storage and transmission requirements while effectively preserv- ing essential dynamic motion information.
3.4 End-to-End Rate-Distortion Optimization
Following Bellé et al. 28 our framework performs end-to-end rate-distortion optimization. Our framework jointly optimizes all relevant parameters by minimizing composite loss function. These parameters include the original Ex4DGS parameters, those from our differentiated quantization (such codebooks and entropy models for ECVQ), and the wavelet-compressed dynamic position repre- sentations. This total loss, Lival, integrates the standard rate-distortion objective with regularization terms inherent to Ex4DGS:
Liotal Ldist + Arate + drogLrog
(8)
5

---

Here, Las represents the reconstruction fidelity loss, typically combining L1 loss and D-SSIM between the rendered image C and the ground truth image CGr:
Caist = (1.0 - A CL (L, Car) + Adssim (1.0 - SSIM(C, CGr))
(9)
The term Lrte quantifies the estimated bitrate cost of the compressed representation. It aggregates several components aimed at reducing model size. These include autropy-based costs derived from quantized attribute representations (e.g. VQ indices) and potentially auxiliary losses that promote sparsity within the model, such terms encouraging the pruning of redundant Spherical Harmonics coefficients or entire Gaussian primitives. These components are collectively balanced by the rate weighting factor AR. When ECVQ is employed, the objective intrinsically balances the rate cost, and the quantization distortion for the respective attributes during codeword selection and codebook learning. Furthermore, freg incorporates regularization terms adapted from Ex4DGS (18] to encourage plausible scene dynamics. This includes terms penalizing excessive linear displacement for static points and terms ensuring temporal smoothness in the motion and rotation trajectories defined by the dynamic keyframes.
By optimizing this comprehensive loss function end-to-end, our framework learns the compression mechanisms (quantization, pruning, wavelet processing via its impact on Ldist and Grate) concurrently with the scene representation itself. This joint process directly balances the bitrate costs against the final rendering quality and dynamic consistency. guided by the various lambda hyperparameters.
Experiment
4.1 Experimental Settings
Datasets. To validate the effectiveness of the proposed method, we conduct various experiments on two real-world datasets, including Neural 3D Video (N3V) [4 and Technicolor [35]. Because the proposed method is built upon EX4DGS [18), we match the evaluation protocol, it is originally adopted by the previous work [36). Specifically, use the entire set of N3V and the five scenes of Technicolor (Birthday, Fabien, Painter, Theater, and Train).
Metrics. As in the previous works, we report standard metrics including Peak Signal-to-Noie Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) (37] for quantitative evaluation. We also measure the storage size for each compression level to evaluate the proposed method achieves reasonable performance across different compression strengths. By varying the compression parameters, we generate rate-distortion (RD) curves to comprehensively analyze the trade-off between visual quality (distortion) and the storage size (rate) of single model that includes the entire video sequence. Additionally, report frames- seame--per-second (FPS) to measure the rendering speed.
Implementation Details. We first train the base model using Ex4DGS 18 until densification
terminates. Subsequently, we apply our proposed RD-optimizee method, incorporating Gaussian point pruning, adaptive spherical harmonics (SH) pruning, entropy-constrainee vector quantization (ECVQ), and the novel pointwise wavelet transform. To systematically analyze performance at varying compression strengths, we define six compression levels (Levels 1-6), adjusting pruning hyperparameters as follows: for Gaussian point pruning, AGSprune is set to [0.05, 0.02, 0.01, 0.005, 0.002, 0.0005]; for SH coefficient pruning, ASHprume is set to [0.5, 0.2, 0.1, 0.05, 0.02, 0.005], respectively. All experiments are conducted on an NVIDIA RTX 3090 GPU. On average, with this GPU setup, the initial Ex4DGS pre-trate trainii takes approximately 1 hour, and our subsequent ECVQ and pruning optimization stage takes another hour, totaling around 2 hours of training time.
4.2 Rate-Distortion Performance
In this section, we analyze the end-to-end rate-distortion (RD) optimized compression performance of the proposed method. Our primary goal is leverage the upatio-tempora characteristies of videos to achieve compression efficiency beyond the limits of existing methods, while offering flexible control over the rate-distortion trade-off. This goes beyond simple masking and quantiza- tion by incorporating video-specific properties. To achieve different operating points on the RD curve, we generate compressed Gaussians of varying sizes by adjusting the strength of Spher- ical Harmonies (SH) and Gaussian point pruning and quantization, similar to RD3DGS [19].

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Level
Level 2
Level
Level
Level
Level
Arg. EXIDGS Perforaanc
Avg ExIDGS Po
PSNR
SSIM
LPIPS
0.94
0.22
N3V
Mac 130
0.92
0.9
fate Bilo
0.29
018
010
0.2
0.1
0.2
20
40
100
120
20
40
00
80
100
120
20
40
60
100
120
Size
(MD)
Size (MB)
Size (MB)
Figure 1: Average RD performance comparison between our method (Levels 1-6) and the
EX4DGS 118 on the Neural 3D Video (N3V) dataset. Per-scene details are provided in Appendix B
Aug. EXIDCS Performance
Aug.
PSNR
SSIM
LPIPS
0.55
deg doloos: 28 3300 Ang c2P0
0.90
2u1 Toaciia
Tochni
0.88
0.30
0.00
0.25
0.54
6.29
0.92
N0p Ex EICDSS
280 1SN0 1a0
8.15
120
240
20
00 100
140
120
Size (HB)
(MB)
Size (MB)
Figure 2: Average RD performance comparison between method (Levels 1-6) and the
Ex4DGS [8] on the Technicolor dataset. Per-scene details are provided in Appendix B
This is primarily controlled by adjusting the weight for Lisprune and LsHpruee Figures and 2 present the rate-distortion curves (PSNR, SSIM, LPIPS vs. Model size in MB) for the N3V dataset (34] and the Technicolor dataset (35), respectively. Due to the space constraint, Furthermore, Figures 4 and 5 provide qualitative
we provide the per-scene results in Table 6 comparisons, showcasing the visual fidelity of method at key compression levels against original Ex4DGS [8]. The results demonstrate that our proposed approach, incorporating tech- niques like differentiated quantization and the wavelet transform, achieves significant model size reduction (e.g., up to 91 x compression) compared to Ex4DGS while maintaining rea- sonable visual quality. This validates the effec- tiveness of our method in providing flexible trade-off between compression rate and render- ing quality suitable for diverse computational requirements.
Table I: Ablation on quantizing opacity compo- nents (a", a a; b?) for compression levels 1 and 6 on the N3V dataset. PSNR (dB) and rel- ative model size reduction (%) are reported. v indicates the component is quantized.
02
39
26.86 (0.00S)
29.57 (0.00%)
26.86 (4. (6.1%)
29.57 (4.01%)
26.86 (3 84%)
29,57 (3.39%)
26.86 (4.97%)
29.57 (5.30%)
26.17 (5.16%)
28.52 (5,58%)
26.17 (5.98%)
28.52 (6.87%)
4.3 Ablation Study
Opacity Quantization. We validate the opacity quantization strategy described in Section 3.2 through ablation experiments summarized in Table Recognizing that each parameter contributes differently to the final visual quality, we designed this experiment to identify the optimal subset of parameters for quantization that best balances compression efficiency and rendering fidelity, rather than naively quantizing all components. Specifically, we examine the quantization sensitivity of four parameters: static opacity (a*), dynamic opacity (a*), center parameters (ag, a7), and variance parameters (b2, B9). Although static and dynamic opacity share similar characteristics, we evaluate them separately, considering the potential heightened sensitivity of dynamic opacity. The results demonstrate that quantizing additional opacity-relater parameters consistently improves storage efficiency. However, notably in the last two rows, quantizing variance parameters significantly reduces PSNR despite achieving marginal additional storage benefits. Based on the observation, we conclude that quantization should be limited to the static and dynamic opacity and center parameters to maintain balanced trade-off between rendering fidelity and compression efficiency.
Wavelet Transform for Dynamic Positions. We further evaluate the effectiveness of wavelet- based compression applied specifically to the dynamic position trajectories (Ha(()). As detailed in Section 3 our proposed method leverages Haar wavelet transform to capture low-frequency motion components while explicitly discarding high-frequency details. Figure compares the
7

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we Wavelet Transform
Ours
Figure 3: Rate-distortion impact of using wavelet transform for dynamic positions on the N3V dataset. We compare the proposed method ('Ours', with wavelet trans- form) against the same model with- out wavelet transform ('w/o Wavelet Transform') ) Axes show avg. PSNR (dB) vs. avg. model size (MB).
Average (dB)
who Wavalet Transform (L6): 14 14MS 29.5
(L62 11
Size
wio Wavelet Transform [L]1: 1.5GMB| 26.86 dB
21:0
Ours 1.28MB
0.81x
Average Size (MB)
EX4DGS
31. 2
B Bpp 29
(L1)
38d8
EX4DGS 38dB B3 20MB
Ours (L6
30:4400 O PHEE
32 90de 8.M
31,3788
216.17MB
31.5 33
26. 32dB 2.32M
Figure Qualitative results on the Technicolor dataset. We compare Ground Truth EX4DGS [88. and ours compression levels 6 and 1. PSNR (dB) / Size (MB) are shown below each image.
rate-distortion performance of our method with wavelet transform against the baseline without it, clearly highlighting the benefit of incorporating wavelet compression. As shown in the figure, the rate-distortion curve of our method ('Ours') ) is consistently positioned to the top-left of the baseline ('woo Wavelet Transform') ) indicating superior performance trade-off. This positioning signifies that for any given model size, our wavelet- based approach yields a higher PSNR, and conversely, for any target PSNR, it requires smaller model size. Notably, employing the wavelet transform consistently improves performance across all tested compression levels, not only reducing the storage size but also enhancing rendering fidelity. Specifically, compression level 1, our method achieves approximately 19% storage reduction alongside a PSNR gain of 0.19 dB. Even at the highest fidelity setting (level 6), we observe around 26% storage savings coupled with PSNR improvement of 0.09 dB. These results emphasize that exploiting the inherent smoothness of point trajectories through wavelet-based compression not only effectively reduces redundancy in dynamic positions but also enhances trajectory modeling accuracy, leading to improved overall RD performance.
Multi-Level Wavelet Decomposition. To further analyze the impact of our wavelet compression, conduct an ablation study on the level wavelet decomposition applied to dynamic position trajecto- ries. In addition our proposed single-level Haar wavelet transform (retaining 1/2 of the coefficients), we test more aggressive 2-level (retaining 1/4) and level (retaining 1/8) decompo- sitions. As summarized in Table apply- ing a deeper multi-level decomposition pro- gressively reduces the model size. For in- stance, at compression Level 1, the size decreases from 1.63 MB to 1.16 MB. How- ever, this gain in compression comes at a consistent cost to rendering quality. Cru- cially, the new operating points generated by the multi-level transforms do not sur- pass the RD curve established by our orig- inal single-level method.
Table Ablation study on multi-level wavelet de- composition for dynamic positions, averaged over the N3V dataset. Columns compare decompositions retaining different fractions of wavelet coefficients.
Compression Level
Level I. (1/2)
Level (1/4) PSNR / Size
Level 3. (1/8)
PSNR Size
PSNR Size
166
30.26 14.67
28.47 11.35
27.74
/9.59/9.14
28.57 28.57/7.51
27.72/ 6.50
14
29.14/6.23
28.14/5.14
27.44 /.44
L3
28.59 4.37
27.81 /.661
27.38/3.11
1.2
28.27 /
27.57 2.44
27.17/2.00
L1
27,39 1.63
26.89 1.36
26.43 1.16

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EX4DGS
Ours (E6)
Ours (
33 24dB 1118 118.88MB
30.69dB 10.34MB
28.38dB 1 1MMB
EXIDGS
(16)
Ours
33.73dB /0222 68MM
31.45d8 10000MMB
28.02d8 1.12MB
4DGS
(L6)
28.7588
MB
S8d8
BMB
EXIDGS
Ours (L6)
Ours (L.1)
33.91dB /101 101.18MB
31. 34dB 8.49MB
23dB A 0.89MM
Figure 5: Qualitative results on the N3V dataset. We compare Ground Truth, EX4DGS [8] and ours at compression levels 6 and 1. PSNR (dB) /Sze (MB) are shown below each image.
Analysis on Higher-Fidelity Trade-offs. Our framework's 's compression is achieved through mul- tiple lossy mechanisms, including pruning. ECVQ, and the wavelet transform. Therefore, we explore whether adjusting parameters beyond pruning can yield higher-quality results. Specif- ically, starting from our Level setting, we test two strategies: (1) reducing the rate-loss weight, XR. in our objective function, and (2) additionally increasing the ECVQ codebook size.
The results, averaged on the N3V dataset, are presented in 3 Reducing An does not affect the performance and size signifi- cantly. In addition, enlarging the codebook provides a minor additional PSNR gain, it nearly doubles the model size, indicating a less efficient trade-off. This study demon- strates that the future direction can be find a new way to control or to push the curve the better front.
Table 3: Analysis of the RD trade-off higher fideli- ties. We compare L6 configuration against variants with reduced (AR) and increased codebook size. Results are averaged over the N3V dataset
Configuration
Avg PSNR (dB)
Size (MB)
Ex4DGS (Baseline)
32.11
115.63
(L66 low Ag, large codebook)
30.29
27.21
Ours (L.6, low Ag)
30.04
14.40
Ours (L.) Ours (L!)
30.26
14:67
27.39
1.63
4.4 Comparative Evaluation
This section evaluates the proposed method against existing models. Our primary goal is a framework for flexible rate-distortion trade-offs in dynamic 4D scene representation, not just state-of-the the-art rendering. The comparison highlights the method's quality-size characteristics and its ability to significantly compress data while maintaining reasonable fidelity.
We report the PSNR, storage size, and FPS for the N3V dataset in Table The most notable advantage of our method its significant size reduction. At compression Level T, our model achieves remarkable 98.9% size reduction compared to the baseline Ex4DGS, resulting in an average size of only 1.26 MB. Even when compared to 4DGaussians, the previously most storage-efficient method, our approach demonstrates over 25 times greater efficiency at the same compression level. Our method significantly outperforms existing models in rendering speed, reaching up to 163 FPS at
"The PSNR and Size values for our Level and 6 models differ from those in Table as different pre- tre-trained model was used for this ablation study, For detailed explanation, please see Appendix

---

Table 4: Quantitative results on the N3V dataset comparing PSNR (dB), average model size (MB), and FPS. Our proposed RD-optimized method offers flexible compression levels. Level achieves significant size reduction (e.g., about 10x reduction to 11 .06MB) compared to the Ex4DGS [[8] while maintaining competitive quality. Level I demonstrates the capability for aggressive compression
(about 90x size reduction to 1.26MB) for highly resource-constrainee scenarios.
PSNR (dB)f
MBL
Frame/s?
Model
Coffee Martini
Cook Spinach
Cut Roasted
Flame Salmon
Flame Steak
Sear Steak
Size (Average)
Beef
Average
FPS
NeRF-based
NeRFPlayer
38
31.53
30.56
29.35
31.65
31.93
29.13
30.69
5130
0.05
HyperReel
36
28.37
32.30
32.92
28.26
32.20
32.57
31.10
360
2
K-Plane
39
29.99
32.60
31.82
30.44
32.38
32.52
31.63
311
0.3
Mix Voxels-L 40
29.63
32.25
32.40
29.81
31.83
32.10
31.34
500
37.7
Mix Voxels-X
30.39
32.31
32.63
30.60
32.10
32.33
31.73
500
4.6
Gaussian Splatting-best -baiid
4DGS x
26.51
32.11
31.74
26.93
31.44
32.42
30.19
6057
72.0
4DGaussians
26.69
31,89
25.88
27.54
28.07
31.73
28.63
34
146.6
3DGStream
27.75
33.31
33.21
28.42
34.30
33,01
31,67
1200
Ex4DGS 18
28.79
33.23
33.73
29.29
33.91
33.69
32.11
115
72.3
Ours [L]]]
24.97
28.38
28.02
24.16
28.33
28.39
27.04
1.26 (9899)
163.0
Ours [1.6]
26.17
30.69
31.45
26.58
31,34
31.70
29.66
11.06 (-00.99)
100.9
Level 1, suitable for real-time applications. While there is an expected trade-off in rendering quality (PSNR) due to aggressive compression,
our model still produces results that are sufficiently competitive. Particularly note- worthy is the comparison at Level 6, where our method not only has smaller model size (1 111.0 .06 MB) but also a higher average PSNR (29.66 dB) compared to 4DGaus- sians (34 MB, 28.63 dB). A similar trend observed for the Technicolor dataset, as summarized in T
Table 5: Quantitative results averaged over the Tech- nicolor dataset. We compare the proposed method (IL1, L6) against the EX4DGS [8] using standard quality metries and model size.
PSNR
SSIM
LPIPS
Size(MB)
FPS())
EX4DGS B
33.62
0.916
0.088
140.2
72.3
Ouns [L6]
32.20
0.100
36.6930099)
Ours [LI]
28.60
0.822
0.232
2.1(98.5%)
213.9
5 Discussion
Conclusion. We presented the first end-to-end rate-distortion (RD) optimized compression framework for 4D Gaussian Splatting (4DGS) to address the challenge of large model sizes hindering deployment. Leveraging Ex4DGS, our method uses static/dynamic decomposition with adaptive RD-guided quantization and pointwise wavelet transform to achieve flexible compression. Although this work builds upon the Ex4DGS framework, the core principles of rate-distortion optimized compression are general and can be adapted to other 4DGS models. Our approach demonstrates significant model size reduction (down to approximately 1.1% of the original size ) while maintaining reasonable visual quality across standard benchmarks and offering user control over the rate-quality trade-off. This significantly improves the practicality of 4DGS for real-time rendering diverse computational platforms, including resource-constraan
devices.
Limitation. Our approach of filtering high-frequency motion via a wavelet transform has inherent limitation in handling excessively dynamic movements, which can manifest as motion blur artifacts on fast-moving objects. particularly at high compression levels. Additionally, dynamic points still account for a substantial portion total storage, highlighting significant room for improvement in compressing dynamic components (Appendices G and Hp. Future work includes refining the loss function, for example by applying separate masking weights for static and dynamic points, to further enhance dynamic point compression and overall visual quality.
Broader Impacts. Our work significantly simplifies the storage, transmission, and deployment of volumetric videos, directly contributing to the widespread adoption and accessibility of volumetric content. the long term, we anticipate that this research can serve as a foundational step toward establishing standards for volumetric video formats. Nevertheless, easier distribution may also enable rapid proliferation of malicious or inappropriate volumetric content, underscoring the need for proactive measures to mitigate such risks.
10

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vector A Detailed Rate Loss Formulation
In Section we introduced the rate term Lrtee which core component of our end-to-end rate-distortion optimization framework. The structure of our rate loss is based on the formulation proposed for static scenes in [19] which we adapt to the unique challenges of dynamic 4D Gaussian Splatting by extending it to handle both static and dynamic components separately.
This section provides the detailed formulation of Lrate- The total rate loss weighted sum of four distinct terms: Gaussian pruning loss (LGSpmine) SH coefficient pruning loss (LsHpmmme) an entropy loss (Lennooy), and vector quantization loss (Lvo)-
The complete expression for the total rate loss is:
Crtte AUSprune CoSprrun + AsHprune CSppruee Lentropy LvQ
(10)
Here, LSmpmme and Lstiprune are the pruning losses defined in Equations (4) and (5) of the main text, which encourage model sparsity. The terms Lentropy and Lvo arise from the quantization (ECVQ) applied to various Gaussian attributes (e.g.. scale, rotation, SH coefficients).
The entropy loss, Lentrpy, estimates the bitrate required encode the quantized parameters. It is formulated as the cross-entropy between the distribution of the quantized symbols and our learned entropy model:
(a,m) Fig
(r.m) 3
(DC,m)
(8H1,m)
(8H2m))
(SH3m
Lentropy
Σ
Fi.
Atamm)
(1sm)
(DG,m))
(SH22m)
X (8H3m
me (staticc dynamic)
(11)
The vector quantization loss, Lvo, measures the distortion introduced by mapping continuous parameter values to discrete codebook vectors during quantization:
Lve
Σ
[(d(am) (afam)) + d,.. + di. d.j
((.m) (DC.m) (SHImm) + dij
(SH2.m) + d
(SH3.m)
me (statiiic, dynamic] 1=1
(12)
In these equations, the superscripts (s, m), (r, m)... denote different attributes such as scale and rotation for both static and dynamic (m) Gaussians. The term rs.j represents the estimated bit cost (rate), while di signifies the quantization error (distortion).
B Additional Quantitative Evaluation Results
We report the detailed quantitative evaluation results, including storage size and rendering fidelity metrics (PSNR, SSIM, LPIPS), for each scene of the N3V and Technicolor datasets in Table The most notable highlight the substantial model size reduction achieved by our proposed method across all scenes, ranging from 79.3% the highest fidelity setting (Level to 99.3% at the most aggressive compression level (Level 1). Although aggressive compression (lower levels) occasionally leads to significant drops in rendering fidelity, our framework provides multiple trade-off options to flexibly adapt according to deployment scenarios and resource constraints. These results indicate promising directions for future work, focusing on enhancing reconstruction fidelity lower compression levels to further broaden applicability.
14

---

Table 6: Quantitative results averaged over the N3V and Technicolor datasets. We compare the proposed method (L1-L66 against the Ex4DGS
using standard quality metrics and model size.
[N3V] Coffee Martini
[N3V] Cook Spinach
SSIM
PSNR
LPIPS
Si
SSIM
PSNR
LPIPS
(iB)
Ex4DG8 18
0.918
28.79
0.070
129.77
Ex4DGS B
0.956
33.24
0.042
118.38
Ours [1.6]
0.897
26.17
15.29 (-88.2%)
Ours [L,6]
0.942
30.60
0.064
10.44 (-91.2%)
Ours [[.S]
0.894
25.92
0.098
10.60 (-91.8%)
Ours [LS]
0.939
30.50
0.069
6.75 (-94.3%)
Our [[4]
0.892
25.82
0.101
7.40
Ours [[4]
0.935
30.11
0.075
(-502-9)))%
Ours [[.3]
0.888
25.74
0.108
5.21 (-96.0%)
Ours [L33]
0.930
29.55
0.083
3.12 (-97.4%)
Ouns [1.2]
0.882
25.48
0.119
3.48 (-97.3%)
Ours [1.2]
0.922
28. 28.99
0.099
2.06 (-98. 3%)
Ours
0.868
24.97
0.144
1.92
(-98.5%))
Ours [L1]
0.910
28.38
0.121
(-99.0%)
[N3V] Cut Roasted Beef
[N3V] Flame Salmon
SSIM
PSNR
LPIPS
Stac(MB)
SSIM
PSNK
LPIPS
Size(B)
Ex4DGS [R
0.958
33.73
0.040
122.63
Ex4DGS [X]
0.926
29.29
0.066
128. 11
Ours
0.943
31.45
0.064
10.04 (-91.8%)
Ours [L6]
0.910
26.58
0.079
(-89.5%)
Ours [L5]
0.940
30.86
0.068
6.59 (-94.6%)
Ours [LS]
0.908
26.35
0.082
8.97 (-93.0%)
Ours [L4]
0.936
30.25
0.074
4.42 (-96.4%)
Ours [[4]
0.905
25.92
0.088
6.07 (-95.3%)
Ours [L3]
0.931
29.82
0,082
3.03 (-97.5%%)
Ours [L3]
0.902
25.68
0.095
4.18 (-96.7%)
Ouns [1_2]
0.924
29.04
0.093
2.01 (-98.4%)
Ours [L22]
0.893
24.87
0.107
2.77 (-97.8%)
Ours
0.911
28.02
0.122
1.12 (-99.1%)
Ours [T.1]
0,875
24.16
0.138
1.52 (-98.8%)
[N3V] Flame Steak
[N3V] Sear Steak
PSNR
Size(MiB)
SSIM
PSNR
LPIPS
Size(MB)
Ex4DGS 18
0.963
33.91
0.034
101.18
Ex4DGS 1K
0.960
33.69
0.035
93.69
Ours [1,6]
0,950
31.34
0.059
8.49 (-91.6%)
Ours [L.6]
0.950
31.70
0.056
8.66 (-90.8%)
Ous [LS]
0.947
30.86
0.063
5.41 (-94.7%)
Ours [LS]
0.948
31.22
0.061
5.43 (-94.2%)
[L4]
0,943
30.38
3.56 (-96.5%%
Ours [L4]
0.944
30.62
0.068
(-96.2%)
Our [L.3]
0.938
29.95
0.080
2.40 2.40 (-97.6%)
Ours [L.3]
0.938
29.95
0.077
2.48 (-97.3%)
Ours [1.2]
0.931
29.13
0.093
1.58 (-98.4%)
Ours [T.2]
0.931
29.34
0.093
(-98.3%)
Ours [L]]
0
28.33
0.122
(199.19))
Ours [L]]
0.917
28.39
0.122
0.94 (-99.0%)
Technicolar] TTain
SSIM
PSNR
T.PIPS
Size(MB)
Ex4DGS [8]
0.928
31.37
0.055
216.17
Ours [L6]
0.931
31.55
0.040
33.19
(-84.6%)
Ours [LS]
0.928
31.38
0.055
21.76
(-89.9%))
Ours [T.4]
0.913
30.39
0.060
(:94.19))
Ours [L3]
0.889
29.86
0.092
7.61 (-96.5%%
Ours [L2]
0.845
28.44
0.146
4.55 (-97 (9%)
Ours [[.]]
0.765
26.32
0.247
2.32 (-98.9%)
Technicolor) Birthday
[Technicllor] Fabien
SSIM
PSNR
LPIPS
Siae(MB)
SSIM
PSNK
LPIPS
(ize(B)
Ex40G8 18
0.942
32.38
0.044
162.38
Ex4DGS 13
0,897
35.38
0.123
83,20
Ours [1.6]
0,932
31.33
0.046
(-79.38)
Ours [1.6]
0.871
32,90
0.168
(-92.9%)
Ours [15]]
0.932
31.33
0.047
25.47
(-84.3%)
Ours [LS]
0.867
32.74
0.182
2.86 (-96.69)
Ouss [L4]
0.929
31.08
0.051
18.50
(-88.6%)
Ours [[4]
0.861
32.60
0,200
1.77 (-97.9%)
Ours [L3]
0.924
31.00
0,057
13.59
(-91.6%))
Ours [L3]
0.856
32.06
0.214
1.27 (-98.5%%
Ouns [1.2]
0.916
30.56
0.068
9.34 (-94.2%)
Ours [1.2]
0,850
31.68
0.228
0.90 ( (-98.9%)
29.46
0,092
5.39 (-96.71))
Ours [L]]
0,840
30.44
0.254
(-99.31%)
[Technicalor] Painter
[Technicalor] Theater
SSIM
PSNR
LPIPS
SizeMMB)
SSIM
PSNR
LPIPS
Size(MB)
Ex4DGS 1k
0.932
36.73
0.091
106.30
Ex4DGS LK
0.883
31.84
0.129
132.89
Ours [L66
0.914
35.05
0.110
9.19 (-91.4%)
Ours [L6]
0.871
30.16
0.138
16:27 (-87.8%)
Ours [1.5]
0.905
34.00
0.122
5.55 (-94.8%)
Ours [T.5]
0,869
30.12
0.150
9.31 (-93.0%)
Ours [T4]
0.891
33.29
0.142
3.47 (-96.7%)
Ours [[.4]
0.858
29.70
0.172
5.58 (-95.8%)
Ours [L3]
0.873
32.20
0.168
2.30 (-97.8%)
Ours [L3]
0.849
29.42
0.191
3.57 (-97.3%%
Ours [L2]
0.848
30.96
0.210
1.47 (-98.6%)
Ours [L2]
0,833
29.00
0,221
(-18.-9884%)
Ours [L]]]
0.804
28.93
0.293
0.79 (-99.3%)
Ours [L]]]
0.804
27.84
0.274
(.99.19))%
C Rate-Distortion Performance for Each Scene
In addition to the numerical results provided in Appendix B we illustrate detailed per-scene rate- distortion (RD) curves for both datasets. Specifically, Figures and show RD curves for the N3V and Technicolor datasets, respectively. These figures visually reaffirm the notable size reductions achieved by our method, consistently demonstrating drastic reductions in model sizes. closer examination reveals that fidelity degradation for the Technicolor dataset is relatively moderate compared to N3V, likely due to the simpler and less intricate motions inherent in Technicolor scenes. Conversely, N3V contains complex and detailed actions, such as precise tool movements or dynamic interactions (e.g., operating kitchen tongs or using a torch to sear meat), making compression-induced fidelity degradation more pronounced. This observation highlights opportunities for further enhancing
15

---

compression methods, specifically targeting more effective exploitation of detailed volumetric motion information. Another noteworthy finding appears in the Technicolor dataset's "Train" scene, where certain compression levels achieve superior rendering fidelity compared to the baseline Ex4DGS method. This result indicates the potential benefit of model simplicity, characterized by fewer points and lower entropy, which can unexpectedly enhance view-synthesis quality by effectively reducing model redundancy.
Level
Level
Leve
Level
EX40Gs Perf
EXADOS Point
LPIPS
coffee. martin!
29.00
0.02
20.00
27.00
Note
0300
Ex40es
7798
0.20
0.85
0.10
0.10
20
50
80
100
130
60
50
100
120
20
40
60
100
120
cbok. Spinach
0.23
0.20
lass: DOW
0.22
E24000
3013 LUS T0ANME
0.32
100
120
20
100
120
20
120
cut roasted beee
$2.00
CHEDGR 120 100VE
Ialal Like
$0.00
0.18
0.92
0.15
100
fleme salnoon. 1
0.25
28.00
FAEEAN FILTION IVER
0.20
22.00
24.00
34.00
Rtamee steak
$2.00
EUDOS DO TIPE 14TM
ExaroN TEL FEL Un
0.00
034
25.00
34.00
0.5
Sear steak
DiIDO2 vonere CO40
20.00
0.35
22.00
CVEDOS
ALAAEELLLE LE 3.DOD
40
Size
(MB)
Size (MB)
Size
(MB)
Figure 6: Per-scene RD performance comparison between our method (Levels 1-6) and the EX4DGS 18] on the Neural 3D Video (N3V) dataset.
16

---

Level
Level
Level
Level
Level
Level
Es4DOS Perf
EX40OS Point
PSNR
SSIM
LPIPS
0.20
32.00
Birthday
$0.00
Nate: 2214
0.15
0.12
90
75
160
125
25
50
75
100
125
150
25
50
125
150
Fableen
34.00
0.50
Centaor 123240 SOOE
0.35
$2.00
30.00
0.54
40
60
0.50
Faaros TEE YEUR Un TOME
Painter
50.00
12.00
20.00
0.20
E500550 130 9960
20
120
100
32.00
0.53
Theeter
31.00
0.80
E
20.00
0.61
11..0
20.00
Celoco 316 (ut 1910
0.50
Adte the
Thain
29.00
28.00
SHE000 236-11M ALLIME
(MB)
Figure 7: Per-scene RD performance comparison between our method (Levels 1-6) and the EX4DGS 18 on the Technicolor dataset.
17

---

D Analysis on the Number of Pruned Points
To verify that the proposed method effectively reduces redundancy in both dynamic (Ga) and static (G.) components, we examine the reduction ratios across different compression levels, as reported in T The results indicate that at the highest compression setting (Level 1), the reduction rates for dynamic and static points are similarly significant. However, at the lowest compression level (Level 6), the reduction ratio of dynamic points consistently surpasses that of static points. This suggests that when fidelity prioritized, our method tends to prune dynamic points more aggressively, likely due to the smaller spatial coverage of dynamic points compared to static points. Nonetheless, given that dynamic points typically correspond to salient foreground elements, overly aggressive pruning of dynamic points may adversely impact overall visual quality. This highlights an avenue for future improvement: dynamically adjusting the pruning strategy-for example, by assigning separate weighting coefficients to static and dynamic points within the pruning loss to better preserve visually salient dynamic components while maintaining high compression efficiency.
Table 7: Reduction in static (G.) and dynamic (Ga) point counts for Technicolor scenes.
G.
Ga
Go
Ga
705377
1028398
87284
259209
Birthday
62457 (-91.19)
53578
(-94.8%)
Fabien
7010 (-92.0%)
2455 (-99.1%)
419283 (-40.6%)
344054 (-66.5%)
6
47383 (445.7%)
60256 (-76,8%)
18T0464
241302
103811
463955
Train
57078 (-96.8%)
10013 (-95.9%)
Painter
4342 (-95.8%)
6307 (-98.6%)
1221 140 (33266%
57536 (-76.2%)
58010 (44.1%)
103793(77.69))
E
Comparison with Concurrent Work: Light4GS
This section provides direct comparison with the concurrent work Light4GS 14]. To ensure fair comparison, we retrained our model to match the experimental protocol of Light4GS, which uses a different resolution (1024 768) and slightly different subset of the N3V dataset. The results are summarized in Table g
The comparison highlights the different strengths of the two methods. While Light4GS achieves higher absolute PSNR, our method demonstrates superior performance terms of compression ratio and rendering speed, particularly in the high-compression setting. Specifically, our high-compression model is more than 2.5x smaller (1.48 MB vs. 3.77 MB) and runs approximately 4x faster (~~160 FPS vs. 40 FPS).
Furthermore, the PSNR gap is not a fundamental limitation of our compression framework. As demonstrated in our ablation study on higher-fidelee fidelity trade-offs (Table relaxing the rate-loss weight (Ax) can significantly boost our model's PSNR. This suggests the performance difference also stems from different design priorities and baseline models (Ex4DGS for ours, 4DGS for Light4GS), rather than the compression methodology itself. Our framework's primary advantage lies its flexibility, offering wide, user-controllable spectrum of rate-distortion-speed trade-offs suitable for diverse deployment scenarios.
Table 8: Direct comparison with Light4GS on the N3V dataset, following their experimental protocol. Our method excels in high-compression and rendering speed, while Light4GS achieves higher peak PSNR.
Model
PSNR (dB)
Size (MB)
FPS
Ours Compprssoo))))ii
27.90
1.48
160
Ours (High-Quality)
30.80
12.56
100
Light4GS (High-Compresssion)
31.48
3.77
40
Light4GS (High-Quall) Sush-Quallty)
31.69
5.46
~37
18

---

Note on Pre-trained Models for Ablation Studies
Our RD-optimized framework follows a two-stage training process: initial pre-training using the Ex4DGS methodology, followed by RD-optimized fine-tuning. For consistency, all primary ex- perimental results reported in this paper (except for Tables 2 and originate from single, fixed pre-trained model.
The ablation studies presented in Tables and were conducted supplementary experiments. As the original pre-trained model was unavailable at the time of these additional experiments, they utilized separately pre-trained model. Our methodology involves initiating the RDD-optimized fine- tuning after the densification stage of the Ex4DGS schedule, which is prior to its full convergence. Consequently. minor performance variations in the starting point for fine-tuning can occur between different pre-training runs. This explains the slight differences in the baseline performance for the Level and Level configurations between these tables and others in the paper. However, this does not affect our main conclusion: once pre-trained model is fixed, our framework consistently demonstrates flexible and effective rate-distottion trade-off controlled by the fine- fin-tuning parameters.
19

---

G Visualization of Static and Dynamic Points
We visualize the static and dynamic components separately across different compression levels to further investigate the impact of our proposed compression method, as shown in Figures to 10 Consistent with the quantitative findings discussed Appendix[D] our method tends to prune dynamic points more aggressively than static points, despite dynamic regions generally holding higher visual saliency. Particularly noticeable in the highly compressed results (Level 1) shown Figures and rapidly moving objects often exhibit noticeable local blur due to aggressive dynamic point pruning. Nevertheless, it's worth emphasizing that even at moderate compression (Level 6), our method successfully achieves substantial storage savings (approximately 90% average) while preserving rendering quality across most scenes without significant visual artifacts. This demonstrates our method's practical balance between storage efficiency and visual fidelity.
EX4DGS
lev1
lev6
GT
Static
Dynamic
EX4DGS
lev1
lev6
GT
Static
Dynamic
All
Ex4DG5
lev1
lev6
GT
Static
Dynamic
Figure 8: Visualization of static and dynamic points N3V dataset. From top bottom: Coffee Martini, Cook Spinach, and Cut Roasted Beef.
20

---

EX4DGS
lev1
lev6
GT
Static
Dynamic
All
EX4DGS
lev1
lev6
GT
Static
Dynamic
All
EX4DGS
lev1
lev6
GT
Static
Dynamic
All
Figure 9; Visualization of static and dynamic points N3V dataset. From top bottom: Flame Salmon, Flame Steak, and Sear Steak.
21

---

Ex4IDSS
levi
Static
Dynamic
EX4IGS
Inv
levé
GT
Static
Dynamic
All
Ex4DGS
lev
GT
Static
Dynamic
All
EX4DGS
lev1
lev6
GT
Static
Dynamic
EX4DGS
levi
lev6
Static
Dynamic
Figure 10: Visualization of static and dynamic points on Technicolor dataset. From top to bottom: Fabien, Birthday, Theater, Train, and Painter.
22

---

H Analysis of Compressed Representation Size
This appendix details the storage allocation (in MB and percentage) for individual components of our RD-optimized model across various scenes from the Technicolor Table TO and N3V Table ? datasets. Results are shown for Low Rate (Level 1) and High Rate (Level 6) settings. These tables provide granular view of bit distribution for key components such as sorting indices for the masked SH parameters (sort idx), entropy-coded indexes (Indexes), masked wavelet coefficients masked. dynamic position displacements disss)) static position (1g)), codebooks, logits, and opacity variance parameters (33).
Across both datasets, it can be observed that components related to the explicit representation of Gaussians and their dynamic properties (e.g., F masked. Indexes, sort idx) generally constitute a significant portion of the bitstream, especially at higher rates where more detail is preserved. The relative proportions vary across scenes, reflecting differences in scene complexity and motion characteristics.
Table 9: Storage allocation (MB and percentage) for components in our RD-optimized 4DGS model. N3V Scenes at Low (LI) and High (L6) rate settings.
Coffee Martini
Cook Spinsch
Low Kaic [-]]
High Rine [16]
Low Kate [_]]
High Rasz [E6]
Componest
Rase
Proportion
Rate (MB)
Propection
Componeut
(MB)
Proportion (1)
(MB)
Propection
Funnted
23.715
Indexes
0.413
20.66%
4,051
25.20%
Codehooks
0.241
20.01%
2,869
26,20%
sen kdl
0.319
15.81%
2.574
16.05%
Indexes
0.234
19.41%
1
12.87%
Codebooks
0.284
14,95%
1.566
10.45%
soct idx
0.151
12,50%
0,862
7.87%
0.214
10,625
1.676
10.45%
0.097
8.07%
0.851
7.77%
Piciap
6.23%
Maccpp
0.057
8.17%
Logals
2.11%
52
0.033
1.60%
0.146
0.91%
P
0.021
1.78%
0.135
1.24%
Header
0.000
0.005
1.000
0.009
Heades
0.000
0.005
0.000
0.0046
Total
1:223
15,292
Total
1.149
10.448
Law Kate [T]]
Hlghh Rone (66)
Low Rate [.]]
Hligh Kine [L,)
Comporent
Rano (ane(11))) (X15)
Priperion (94)
Kaln (315)
Propectio (56
Component
Rano (M15)
Proportion (9)
Rate (MR)
Pripectin
F
0.303
23%
1.5%
$2.25%
0.303
19,015
3.507
27.05%
Codehooks
25.62%
0.305
3.453
Indexsss
0:271
17.01%
8.41%
sert ids
0:256
16,099
10.95%
Po
0.101
8.58%
0.885
8.41%
0.175
10.99%
1,542
10.95%
0.101
8,58%
0.849
8.06%
0.175
10.00%
0.973
6.91%
T Logik
0.077
6.57%
0.252
2.39%
0.088
5.51%
0.283
2.01%
0.020
0.133
1.27%
0.025
1.43%
0.143
1.02%
Header
1.031
15412
Flam Stak
Sear Steak
Rase [.1
High [L6]
Componest
Propottion
Propection
Component
(MBB
Proportion (16)
Propoction
Codebooks
23.55%
1,500
30.10%%
Codebooks
0.225
22.87%
30.15%
0.183
19.54%
2.200
24.71%
F Freste
0.189
19.21%
2,278
25. (5.08%
Indexes
0.364
17.89%
1,263
14.15%
Indexes
0.171
17.31%
1,282
14.12%
ide
0.121
12.50%
0.834
9.36%
sect idx
01.34
13.50%
0.830
9, 3,14%
Ao
8.91%
#4p
8.07%
Pexup
0.050
Logas
0,072
7.20%
0.198
2.23%
Logis
0.073
7,43%
0.200
2.24%
0.014
1475
0.132
1.48%
0.014
1
0.131
1.44%
Header
0.000
0.01%
0.000
0.00%
Header
0.000
0.01%
0.000
0,00%
Total
8.491
0.41
8,561
23

---

Table 10: Storage allocation (MB and percentage) for components in our RD-optimized 4DGS model. Technicolor Scenes Low (T.1) and High (T.6) rate settings.
Train
Theater
Low Rate
High Rate [16]
Low Rate [L]]
High Rate [16]
Component
Kate (MH)
Ratio (%)
Kate (MB3)
Katio (%)
Component
Kare (MB))
Ratin (%)
Rate (MIH)
Ratio (56
0.537
22.08%
10.230
29.40%
F Fantal
0,242
86%
5.660
33.17%
Indexes
0.488
18.81%
7.327
21.06%
Codeboaks
0.231
18.96%
4.272
25.04%%
Frasted
0.361
14.84%
7.327
21.06%
Indeses
0.222
18.24%
2,698
15.81%
Patep
0.343
14.09%
6,394
18.37%
sort idx
0.189
15.51%
1.258
7.37%
Mo
0.343
14,09%
2,072
5.95%
Matap
0,101
8.33%
1,080
6.33%
Codebooks
0.230
9.46%
0.866
2.49%
Mo
0.101
8.33%
1.080
6.33%
Logits
0.080
3.31%
0.461
1.32%
Logits
0.077
6.33%
0.883
5.18%
0.080
3.31%
0.123
0.35%
0.054
1.43%
0.130
0.76%
Header
0.000
0.00%
0,000
0.00%
Header
0.000
0.00%
0,000
0.00%
Total
2.319
33.187
Total
1.162
16.271
Painter
Fabien
Low Rate [_]]
High Rate [L6]
Low Rate [L]]
High Rate [1,]]
Compoment
Rote (MB)
Ratio (%)
Rate (MIB)
Rutio (%)
Component
Race (MB)
Ratio
Rate (MB)
Ratio
Firaded
0.227
27.48%
3.737
38 38.79%
Codebooks
0.188
31 099%
2.169
55%
Codehooks
0.196
23.66%
2.499
25.948.
Frested
0.089
14.67%.
1,516
24.539
Indexes
0.150
18.12%
1.295
13.44%
Indexes
0.084
13.89%
0.861
13.94%
idx
0.085
10.33%
0.831
8.62%
idx
0.076
12.57%
0.182
7.81%
Logits
0.065
7.91%
0.472
4.90%
Logits
0.063
10.4%
0.480
7.77%
0.051
6.13%
0.348
3.62%
Malay
0.042
7.00%
0.285
4.61%
Massp
0.026
3.18%
0.348
3.62%
0.042
7.00%
0.285
4.61%
0.026
3.18%
0.104
1.0%%
ge
0.020
3.29%
0,100
1.62%
Header
0.000
0.01%
0.000
0.00%
Header
0.000
0.01%
0.000
0.00%
Total
0.789
9.188
Total
0.576
5.892
Birthday
Rate
High Rate
Component
Rate (MID)
Ratio (%))
Rate (MB)
Ratio (%)
Fraste
1.999
34.13%
12.356
35.14%%
Indexes
1,280
22.65%
8.057
22.86%
soi idx
0.929
16.43%
6.107
17. 17.32%
B2
2,753
Maiep
0.375
6.63%
2,516
7.14%
Mo
2.516
Codebooks
0.250
4.43%
0.794
2.25%
Logits
0.085
Header
0.000
0.00%
0.000
0.00%
Total
5.391
33.617
24

---

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Reviews:
[{"review_id": "SbdpoioyjJ", "reviewer": "Reviewer_QNNe", "summary": "This paper presents a 4D Gaussians compression method with flexible control of quality-cost tradeoff (rate-distortion) and a temporally smoothness formulation of dynamic positions.\n\nThe method is an offline compression system given the input of already trained 4DGS representations (Ex4DGS in this case). Then the proposed method will prune the attributes and apply existing quantization technique ECVQ to compress them. For the dynamic positions, the authors propose to use a compact truncated wavelet representation to represent the dynamic trajectory. There are some detailed choices on not compressing certain attributes (rotations for example) for fidelity. The final optimization scheme considers the RD tradeoff with a rate term along with a fidelity (image reconstruction) term with some regularization from Ex4DGS.\n\nThe paper presents some good compression rates and quantitative/qualitative results and comparisons to support their design choices.", "questions": "Some parts of the method are not very clear.\n\n\n1. The loss function of L_{rate} is not clearly defined. I do see Eq. 4 and 5. But It would be great to clearly define the loss function. Furthermore, on this rate loss term, what is the difference compared to ECVQ? From the current writing, I feel the two are the same or highly similar. It would be great to highlight the difference (and clearly define the loss function). Furthermore, please also clarify in the method section (3.2, 3.3), the difference to the ECVQ paper.\n\n\n2. Line 162: I assume the sg() function is the stop gradient operation. Please confirm and clearly define every term/function in the method section.\n\n\n3. The regularization term (In Eq. 8), is it exactly the same as the one in Ex4DGS? Please confirm. If not, please highlight the difference as well.\n\n\n4. The design choice of truncated Wavelet for smooth dynamic positions, though sounding reasonable, is not very well validated.\n- (a) Firstly, let me confirm the actual computation process, with the cubic Hermite interpolation of the keyframe positions from Ex4DGS, the proposed system would firstly evaluate those positions (via interpolation) to obtain the \\mu_{d} in Line 190 for every frame, and then apply the wavelet conversion. Is that right?\n- (b) Could we use DCT or Fourier transform as well? What is the actual advantage of Wavelet transformation? It would be great to explain and provide some empirical analysis.\n- (c) In Eq. 7, the method would remove the high-frequent components of the wavelet and enforce some smooth output on F_{masked}. It would be great to see the actual effects by varying this frequency cut hyperparameter as an ablation study.\n- (d) With this low-frequency “projection”, we can get a smooth output, but the method faces another risk of losing capacity to handle fast motion. Will the method have this limitation? Any results on fast motion?\n\n\n\n\nSome questions on evaluation metrics and results:\n\n\n5. All the model sizes reported in this paper: I assume they are the model size of the full sequence, not per-frame, is that correct? Please confirm. It would be great to write it clearly. For sequences of different lengths, it would be great to report per-frame model size.\n\n\n6. The ablation in Fig. 3 for wavelet formulation of dynamic positions: what is the case of “without transform”? Directly storing \\mu_{d} for all frames, for keyframes only, or applied ECVQ? Currently it’s not clear how to interpret the results.\n\n\n7. Fig. 1 and 2 shows the RD tradeoffs. Even the highest quality case (L6) still presents some distance to the uncompressed case (upper bound): sometimes >2 in PSNR difference. I do see the high compression rate (L1-6 are mostly at the left side of the graphs). Can we further improve quality (L > 6 for example) and trade in more model size? What is the limit of reconstruction fidelity? The current results appear to have some inevitable quality loss.\n\n\n8. What is the runtime of the proposed system?\n\n\n9. Comparisons:\n- (a) Visual comparisons. There are only comparisons to the upper bound case Ex4DGS and L1/L6 of the proposed method. Can we see visual comparisons among the existing methods?\n- (b) It seems that Tab 2 could add recent work on dynamic 3D scene compression such as TeTriRF (Wu, Minye, et al. \"Tetrirf: Temporal tri-plane radiance fields for efficient free-viewpoint video.\" CVPR 2024) and QUEEN (Girish, Sharath, et al. \"Queen: Quantized efficient encoding of dynamic gaussians for streaming free-viewpoint videos.\" NeurIPS 24). It seems that TeTriRF might have a similar level of performance on compression (but please validate this with results) and QUEEN with higher visual quality. It would be great to add them and provide some analysis/discussion.\n- (c) It would be great to provide video results, as the method works on 4DGS / 3D video compression. Static evaluation of per-frame image quality does not fully represent the visual fidelity (for videos).\n\n\n10. Novelty\n - (a) A concern is that this proposed method is very close to the case of applying ECVQ techniques on Ex4DGS with an updated wavelet-formulation of positions. I think it is a good effort nevertheless. But it is important to highlight the actual difference and improvement\n - (b) Furthermore, the system is built to compress Ex4DGS, which is one of the current 4DGS representations. Will it work on other 4DGS representations (polynomial based, or the one in “FreeTimeGS: Free Gaussian Primitives at Anytime and Anywhere for Dynamic Scene Reconstruction”)?", "limitations": "I would hope the authors to address the potential limitations in the following aspects:\n- Fidelity loss (in item 7 in “Questions” section above)\n- The difference and novelty based on ECVQ and Ex4DGS seem limited (in item 10 in “Questions” section above)\n- Runtime (no numbers provided yet)", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strength:\n- The proposed method overall makes sense: using a low-frequency wavelet representation to enforce some spatial smoothness for dynamic positions, incorporating the RD optimized formulation from work such as ECVQ into 4DGS compression.\n- Good compression rates based on the results. \n- The paper is overall well-written.\n\n\nWeakness:\n- Some parts of the method are not very clear (see item 1 to 4 in the question section).\n- There are still some questions on evaluation metrics and results (see item 5 to 9 in the question section).\n- The novelty can be limited as the paper seems to be applying ECVQ techniques on Ex4DGS instead of 3DGS. The wavelet formulation of position trajectory is also not surprising (and not very well validated — please add discussion and experiments). It would also be great to highlight the difference to ECVQ in the method section.", "was_revised": true, "final_justification": "The rebuttal addressed most of my concerns. From the current results, I increased my rating to borderline acceptance. The reason I did not raise it to acceptance or higher, is due to the limited visual (video) results. I understand that the rebuttal process does not allow uploading visuals.", "rebuttal": "## **Thanks for Reviewer QNNe**\nWe thank the reviewer for their thoughtful comments and detailed questions. We have found the feedback very constructive and address each of the points raised below.\n\n---\n## **General Comments to Reviewers**\nWe thank all the reviewers for their insightful feedback. Before addressing each point, we wish to briefly clarify three key aspects of our work:\n\n**Core Contribution** Our primary contribution is an e2e RD-optimized framework, not just another lightweight model. This provides flexible, user-controlled tradeoffs between quality and size, unlike methods with a single fixed compression level.\n\n**Novelty** Extending RD-optimization to 4D is non-trivial due to new temporal redundancies. Our novel use of a wavelet transform to enforce temporal smoothness is a principled solution to this, analogous to the evolution from image to video compression.\n\n**Model Size** We report size for a holistic model of the entire video sequence, not a per-frame streaming size. This is a critical distinction for storage comparisons. For instance, our compressed coffee_martini model is just 15 MB, while a streaming approach like QUEEN requires ~25 GB to store the full sequence. Our method compresses the complete 4D representation itself.\n\nWe hope these clarifications help frame our contributions and will now address your specific questions.\n\n---\n## **Q&A**\n\n### **On the L_{rate} Formulation and Its Relation to ECVQ (Q1)**\nThanks for the feedback. While our framework is based on ECVQ principles, our key contribution is adapting it for the unique dynamic parameters found in 4DGS. This required developing a new, sensitivity-aware quantization strategy for these temporal components, a process guided by our ablation study, to achieve an optimal rate-distortion trade-off. We'll clarify this distinction in the revision.\n\n---\n### **Clarification of Notations (Q2)**\nThank you for the suggestion. We confirm that sg() is the stop-gradient operation. We will define it, along with other key notations like I(), k, T, and L_{rate} which are missing in the first version, in the revision.\n\n---\n### **Clarification on the Regularization Term (Q3)**\nWe thank the reviewer for the clarifying question. Yes, the regularization term is adapted directly from the one used in Ex4DGS.\n\n---\n### **Clarification on Trajectory Computation (Q4-a)**\nYes, the reviewer's understanding is correct.\n\n---\n### **Rationale for Choosing Wavelet Transformation (Q4-b)**\nThank you for the excellent question. We chose the Wavelet Transform over DCT/Fourier for its superior time-frequency localization.\n\nWhile generally smooth, our interpolated motion paths contain localized discontinuities (kinks). Wavelets excel at efficiently capturing these local events using very few coefficients. Conversely, global transforms like DCT/Fourier are inefficient for such signals: a single kink corrupts all coefficients, causing artifacts (e.g., the Gibbs phenomenon) and hindering compression.\n\nWe also considered other methods like B-splines and PCA but deemed them unsuitable for this compression task. We will add this justification to the paper.\n\n---\n### **Effect of Varying the Number of Wavelet Coefficients (Q4-c)**\nWe thank the reviewer for suggesting an ablation on the wavelet cutoff. We ran comprehensive multi-level DWT (Discrete Wavelet Transform) experiments (testing 2-level & 3-level decompositions) on all 6 N3V scenes across our 6 compression strengths. The averaged results are below.\n\n**Ablation** Multi-Level DWT on N3V (Avg)\n\n|Compression Lev|L1 DWT (1/2)|L2 DWT (1/4)|L3 DWT (1/8)|\n|-|-|-|-|\n|||PSNR/Size||\n|L6|28.73/14.05|28.47/11.35|27.74/9.80|\n|L5|29.59/9.14|28.57/7.51|27.72/6.50|\n|L4|29.14/6.23|28.14/5.14|27.44/4.44|\n|L3|28.59/4.37|27.81/3.61|27.38/3.10|\n|L2|28.27/2.91|27.57/2.42|27.17/2.05|\n|L1|27.39/1.63|26.89/1.36|26.43/1.16|\n\nA higher-level DWT reduces size but also lowers PSNR. These new points trace our established RD curve instead of improving it. This confirms our single-level method's excellent size/fidelity trade-off. We thank the reviewer, as their suggestion helped validate our design and explore the solution space.\n\nWe will add this summary to the ablation study and full per-scene results to the supplement.\n\n---\n### **Discussion on Smoothness and Fast Motion (Q4-d)**\nThank you for this valid point. You are correct; handling fast motion is a limitation due to a deliberate trade-off. To maximize compression, we uniformly filter high-frequency motion details, a process analogous to how video codecs like H.264 create motion blur at low bitrates. As future work, we will propose an adaptive strategy to selectively preserve these details for fast-moving points.\n\n---\n### **Clarification on Model Size Reporting (Q5)**\nYes, you are correct. We will clarify this in the final version for better comparability.\n\n---\n### **Details of the Wavelet Ablation Study (Q6)**\nThank you for the question. In the \"w/o Wavelet Transform\" ablation, our entire RD optimization framework is active. \n\nThe key difference is the target of the compression:\n\nOurs: Compresses the wavelet coeffs of the motion trajectories.\n\n\"w/o Transform\" (Ablation): Compresses the keyframe parameters directly.\n\nThis experiment is designed to measure the compression gains from representing motion in the wavelet domain. We will update itto make this clearer.\n\n---\n### **Discussion on the Rate-Fidelity Trade-off (Q7)**\nYou are correct that our L1-L6 configurations have a quality ceiling. This is an expected result, as our framework's compression is achieved through a combination of lossy mechanisms (ECVQ, wv transform) beyond simple pruning. \n\nWe have investigated this by running new experiments on the ECVQ module for the next step: relaxing its rate-loss and increasing the codebook size. The averaged N3V results below address this:\n\n**Further Analysis on RD Trade-offs**\n\n|Level|Avg PSNR|Size (MB)|\n|-|-|-|\n|Ex4DGS|32.11|115.63|\n|**Ours (L6 + Relaxed Rate & Larger Codebook)**|**30.29**|**27.21**|\n|**Ours (L6 + Relaxed Rate Reg)**|**30.04**|**14.40**|\n|Ours (L6)|28.73|14.05|\n|Ours (L1)|27.39|1.63|\n\n1.  Relaxing the ECVQ rate-loss gives a **1.31 dB gain** with a minimal size increase (14.05→14.40 MB), improving the RD-tradeoff.\n\n2.  Conversely, increasing the codebook size yields little gain for a large size penalty, proving an inefficient tradeoff.\n\nThank you again for this valuable feedback. We will incorporate this analysis into the final paper.\n\n---\n### **Runtime (Q8)**\nWe thank the reviewer for the question regarding runtime. \nOur system's performance is analyzed in two parts: an offline training phase and an online inference phase.\n\n**Inference (Rendering)** Our method achieves real-time rendering speeds ranging from 101 to 214 FPS, depending on the scene and compression level. Detailed results for each experiment are provided in Tables 2 and 3.\n\n**Training** The one-time, offline optimization process requires approximately 2h/scene on a single RTX 3090.\n\nWe appreciate the feedback and will clarify it in the supplement for enhanced clarity.\n\n---\n### **Additional Visual Comparisons (Q9-a)**\nWe agree more qualitative comparisons are crucial. We initially focused on Ex4DGS as it is both a strong and direct foundation for our framework. While rebuttal policy prevents adding new figures now, we commit to including a comprehensive set of visual comparisons against other key baselines in the final supplement.\n\n---\n### **Comparison with Recent Methods (Q9-b)**\nWe thank you for suggesting these relevant works. We will add a discussion of them to the paper. We emphasize that they address different goals and are not directly comparable for the following reasons:\n\nTeTriRF: As a NeRF-based method, TeTriRF's slow rendering speed (~4 FPS) is unsuitable for the real-time applications that our method targets (>100 FPS).\n\nQUEEN: QUEEN is designed for streaming, while our method focuses on e2e scene compression. This fundamental difference in objectives leads to two key distinctions:\n\nStorage: QUEEN's per-frame streaming model results in a significantly larger total file size for an entire sequence.\n\nRD-Optimization: Our core contribution is an e2e RD-optimized framework that generates a full RD curve for flexible quality-size control. In contrast, QUEEN applies post-training quantization to offer only a few fixed points and lacks this essential RD-optimization flexibility.\n\n---\n### **Videos (Q9-c)**\nWe agree that videos are essential. We are unable to provide them now due to policy constraints, but will include a link to a project page with video results in the final manuscript.\n\n---\n### **Highlighting Differences and Improvements Over Prior Work (Q10-a)**\nWe introduce the first e2e, RD-optimized framework for 4DGS, which is more than a simple combination of methods due to two key novelties:\n\n**Wavelet Motion Compression** We apply a wavelet transform to motion trajectories. This leverages temporal smoothness to simultaneously reduce storage and improve rendering quality, as confirmed by our ablation study.\n\n**Selective Temporal Quantization** Naive ECVQ is ineffective for sensitive 4D params (e.g., opacity var). We therefore perform selective, RD-aware quantization, a critical adaptation for 4D.\n\nWe will revise the manuscript to emphasize how these non-trivial adaptations form a holistic and specialized framework.\n\n---\n### **Generalizability (Q10-b)**\nOur method is a general RD framework adaptable to other 4DGS models. \nFor a model like FreeTimeGS, we would apply our compression mechanism directly to its parametric motion model. \nThe core optimization remains unchanged, governed by the same unified loss function (L_{total} = L_{dist} + \\lambda_R * L_{rate} + \\lambda_{reg} * L_{reg-FreeTimeGS}), where only the regularization term would be adapted. \nThis demonstrates the framework's generalizability, which we will discuss in the paper."}, {"review_id": "JyjSgR6hKj", "reviewer": "Reviewer_mDN9", "summary": "This paper proposes a novel method for reducing model size in 4D Gaussian Splatting and evaluates its performance. To achieve model compression, the authors apply aggressive pruning to the 3D Gaussians and Spherical Harmonics (SH) coefficients, use vector quantization (VQ), and represent positional changes of the 3D Gaussians using wavelet transforms. In particular, for representing 3D Gaussian positional changes with wavelet transforms, only the low-frequency components of the wavelet coefficients are retained, leveraging the inherent smoothness of spatio-temporal motion in videos.", "questions": "1. As noted in the “Strengths and Weaknesses” section, the superiority of the proposed method over Light4GS remains unclear. The authors should discuss the advantages of their method in comparison to Light4GS, referencing its experimental results.\n2. Why were other lightweight methods[3][4][5] not included as comparison baselines in the experiments?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Quality**\n1. The paper lacks sufficient variation in the choice of comparison methods. While the experiments include comparisons with standard 4D Gaussian Splatting approaches such as 4D Gaussians[1] and Ex4DGS[2], performance and model size should also be evaluated against other lightweight methods[3][4]. For instance, LGS[5] could serve as a relevant baseline, as its implementation is publicly available.\n2. The Light4GS[3] paper also evaluates its method using the same Neural 3D Video (N3V) dataset as this paper (excluding the “Coffee Martini” sequence from their evaluation). Light4GS reports a PSNR of 31.48 dB with a model size of 3.77 MB. In contrast, according to Table 2 of this paper, the proposed method (L1) achieves a PSNR of 27.04 dB with a model size of 1.26 MB. Although the proposed method achieves a more compact model, the rendering performance degrades significantly. This performance drop appears disproportionately large relative to the reduction in model size.\n\n**Clarity**\n1. The paper is well written, and the proposed methodology is clearly presented, making it easy for readers to understand.\n2. The authors clearly articulate the rationale behind the design choices of the proposed model structure and justify the necessity of its architecture.\n\n**Significance**\n1. Model size reduction in 3D Gaussian Splatting is a critical issue in the field, and this paper specifically addresses this challenge in the context of 4D Gaussian Splatting. The proposed ideas are straightforward and practical, making them easy to implement.\n\n**Originality**\n1. The wavelet transform-based compression proposed in this paper is novel and has not been explored in existing methods.\n\n[1] Yang, Z., Yang, H., Pan, Z., & Zhang, L. (2023). Real-time photorealistic dynamic scene representation and rendering with 4d gaussian splatting. arXiv preprint arXiv:2310.10642.\n\n[2] Lee, J., Won, C., Jung, H., Bae, I., & Jeon, H. G. (2024). Fully explicit dynamic gaussian splatting. Advances in Neural Information Processing Systems, 37, 5384-5409.\n\n[3] Liu, M., Yang, Q., Huang, H., Huang, W., Yuan, Z., Li, Z., & Xu, Y. (2025). Light4gs: Lightweight compact 4d gaussian splatting generation via context model. arXiv preprint arXiv:2503.13948.\n\n[4] Yuan, Y., Shen, Q., Yang, X., & Wang, X. (2025). 1000+ FPS 4D Gaussian Splatting for Dynamic Scene Rendering. arXiv preprint arXiv:2503.16422.\n\n[5] Liu, H., Liu, Y., Li, C., Li, W., & Yuan, Y. (2024, October). Lgs: A light-weight 4d gaussian splatting for efficient surgical scene reconstruction. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 660-670). Cham: Springer Nature Switzerland.", "was_revised": true, "final_justification": "The authors' rebuttal largely addressed my concerns. The experimental results presented in this paper demonstrate a substantial reduction in model size, which we consider a significant contribution to the field. Based on this, I would like to raise my rating by one point.", "rebuttal": "## **Thanks for Reviewer mDN9**\n\nWe extend our sincere thanks to the reviewer for their constructive feedback and insightful questions. We believe that addressing these points will significantly strengthen our paper. Below, we provide a detailed response to each concern.\n\n---\n## **General Comments to Reviewers**\n\nWe thank all the reviewers for their insightful feedback. Before addressing each point, we wish to briefly clarify three key aspects of our work:\n\n**Core Contribution** Our primary contribution is an end-to-end Rate-Distortion (RD) optimized framework, not just another lightweight model. This provides flexible, user-controlled tradeoffs between quality and size, unlike methods with a single fixed compression level.\n\n**Novelty** Extending RD-optimization to 4D is a non-trivial challenge due to new temporal redundancies. Our novel use of a wavelet transform to enforce temporal smoothness is a principled solution to this, analogous to the evolution from image to video compression.\n\n**Model Size** We report size for a holistic model of the entire video sequence, not a per-frame streaming size. This is a critical distinction for storage comparisons between ours and streaming-based models (e.g., QUEEN and 1000+ FPS 4D Gaussian Splatting). For instance, our compressed “coffee_martini model” is just 15 MB, while a streaming approach like QUEEN requires ~25 GB to store the full sequence. Our method compresses the complete 4D representation itself.\nWe hope these clarifications help frame our contributions and will now address your specific questions.\n\n---\n## **Q&A**\n\n\n### **As noted in the “Strengths and Weaknesses” section, the superiority of the proposed method over Light4GS remains unclear. The authors should discuss the advantages of their method in comparison to Light4GS, referencing its experimental results.**\n\nWe thank the reviewer for their insightful feedback and for bringing the concurrent work, Light4GS, to our attention. We agree that a comparison is necessary.\n\nWhile Light4GS reports a higher peak PSNR on the N3V dataset, its primary focus is on a narrow operational range. Our framework's key advantage is its flexibility across a wide rate-distortion-speed spectrum, a direct result of our end-to-end rate-distortion (RD) optimized design. This makes our method highly practical for the diverse ecosystem of real-world devices.\n\nTo provide a more direct comparison, we **re-trained our model to match the experimental protocol of Light4GS (1024x768 resolution instead of 1352x1014, excluding the \"Coffee Martini\" scene)**. The results are as follows:\n\n|Model|PSNR|MB|FPS|\n|----|---|---|---|\n| Ours (High-Compression) |\t27.90 |\t1.48 |\t~160 |\n| Ours (High-Quality) |\t30.80 |\t12.56 |\t~100 |\n|Light4GS (High-Compression) |\t31.48 |\t3.77 |\t~40 |\n|Light4GS (High-Quality)\t | 31.69 |\t5.46 |\t~37 |\n\n```\n  32 |          \n     |                     \nP    |          L1----L2  (Light4GS -- short range of RD)          \nS 31 |                                   _____ Ours L6\nN    |                            ______/\nR 30 |                     ______/\n     |              ______/\n  29 |       ______/\n     |      /\n  28 |   Ours L1 \n     |\n  27 +------------------------------------------------------------>\n     0    1.5   3     4.5   6     7.5   9     10.5   12    13.5\n                            Size (MB)\n```\n\nFurthermore, to demonstrate that this initial PSNR gap is not a fundamental limitation, we conducted further experiments. By simply relaxing the rate regularization on our Vector Quantization (VQ) entropy cost, we were able to significantly boost performance in our high-quality setting (Level 6). Because the experimental setting (resolution) is different from Light4GS for this experiment, it cannot be directly compared to the table above; however, we expect that the relaxation of VQ entropy cost might boost about 1 of PSNR in the same setting as shown in the response in **Discussion on the Rate-Fidelity Trade-off (Q7)** of Reviewer QNNe.\n\nThis result is critical. It proves our RD-optimized framework, which leverages entropy-constrained VQ, has the flexibility to be tuned for higher fidelity and efficiently close the gap with Light4GS. It suggests the performance difference stems from our initial focus on a wider compression range and the choice of baseline model, rather than a limitation of our compression method itself.\n\nThis comparison highlights our framework's strengths:\n\n**(Superior Compression & Speed)** At the most compressed levels, our method achieves a much smaller footprint (1.48 MB vs. 3.77 MB) and is significantly faster (~160 FPS vs. ~40 FPS), making it suitable for highly resource-constrained scenarios.\n\n**(Flexible Trade-offs)** Our method provides a user-controllable trade-off, allowing for smooth scaling between high-speed, low-storage rendering and higher-fidelity outputs. Light4GS operates in a more limited performance window.\n\nWe concede that the absolute PSNR of our method is lower. We believe this difference partially stems from our choice of baseline; our method builds upon Ex4DGS, whereas Light4GS uses 4DGS. We are confident that applying our RD-optimized framework, including the novel wavelet transform for temporal compression, to a different baseline could yield even more competitive peak quality.\n\nWe will revise our manuscript to include this direct comparison, further clarifying that our primary contribution is the novel, flexible RD-optimized framework itself.\n\n---\n\n### **Why were other lightweight methods not included as comparison baselines in the experiments?**\n\nWe thank the reviewer for suggesting these relevant papers. We have investigated them and provide our analysis below.\n\n(**LGS**) This method is highly optimized for the specific domain of surgical scene reconstruction. A fair comparison on our general benchmark datasets (N3V, Technicolor) is infeasible because its framework **relies on strong priors that are unavailable in our monocular datasets**, such as depth-specific losses and, most critically, the assumption of binocular (stereo) vision. We attempted to adapt the public LGS codebase by removing these components, but this resulted in training instability (**diverges to Not-a-Number loss**).\n\n(**Light4GS and 1000+ FPS 4DGS**) These are highly relevant studies that were published as concurrent work around the same time as our submission. As of this rebuttal, neither has a publicly released source code, which makes a direct experimental comparison and reproduction impossible. Furthermore, these methods differ from ours in their core objectives. For example, 1000+ FPS 4DGS is specifically designed for the real-time streaming of dynamic scenes. In contrast, our work concentrates on the post-training compression of the final model for efficient storage and deployment. This fundamental difference in goals means that a direct performance comparison would not be straightforward, even if the code were available.\n\nWe will update our related work section to cite and thoroughly discuss all three papers. We will contextualize LGS as a domain-specific solution and position Light4GS and 1000+ FPS 4DGS as important concurrent research, highlighting their unique approaches to provide readers with a comprehensive overview of this rapidly advancing field."}, {"review_id": "T3WV3ZnW0G", "reviewer": "Reviewer_CQ8T", "summary": "This paper presents a rate-distortion optimized compression framework for 4D Gaussian Splatting. Built on Ex4DGS, the method introduces wavelet-based trajectory compression and adaptive quantization to significantly reduce model size while preserving rendering quality.", "questions": "1. Clarification on RD novelty: How does your RD framework differ in principle and implementation from Compact3DGS, RD3DGS, and recent 4DGS works like Light4GS or QUEEN that also use entropy-aware coding and SH pruning? A table or paragraph contrasting them would help.\n2. Ablation on baseline vs. proposed methods: To what extent does the wavelet transform or adaptive quantization contribute to gains over just Ex4DGS + pruning + VQ? Could you show RD curves or numbers with each module ablated?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. Strong empirical performance: The proposed method achieves up to 98.9% reduction in storage at Level 1 compression with competitive PSNR and SSIM values across both N3V and Technicolor datasets.\n2. Effective trajectory modeling using wavelet transform: The paper leverages temporal smoothness priors via a Haar wavelet transform, which improves compression efficiency while preserving fidelity.\n3. Clarity and completeness: The paper is well written, structured, and covers a comprehensive set of ablations and comparisons, including against NeRF-based and 4DGS-based baselines.\n\nWeaknesses:\n1. Prior art on RD optimization for splatting may be under-acknowledged: While the paper claims novelty in RD-optimized 4DGS, similar ideas (e.g., RD cost applied to SH pruning in Compact3DGS, RD3DGS, or Light4GS) have appeared in recent papers. The authors should clarify the distinction from prior RD-based compression methods, particularly for dynamic scenes.\n2. Ablation study (Table 1) suggests limited contribution from some proposed modules. This raises the question of whether much of the gain is from the baseline design rather than the proposed enhancements.\n3. Dynamic points remain a bottleneck: Dynamic Gaussians still dominate the storage cost at higher quality levels. Although some improvement is shown using wavelets, more sophisticated modeling (e.g., motion basis or predictive coding) could further help.", "was_revised": true, "final_justification": "The paper presents a rate-distortion optimized compression framework for 4D Gaussian Splatting, using wavelet-based compression and adaptive quantization to reduce model size while maintaining rendering quality. All my concerns have been addressed.", "rebuttal": "## **Thanks for Reviewer CQ8T**\n\nWe sincerely thank the reviewer for their thorough evaluation and constructive feedback. The comments have helped us identify areas where we can improve the clarity and positioning of our work. We will address the weaknesses and questions by grouping them into three main topics.\n\n---\n\n\n## **General Comments to Reviewers**\n\nWe thank all the reviewers for their insightful feedback. Before addressing each point, we wish to briefly clarify three key aspects of our work:\n\n**Core Contribution** Our primary contribution is an end-to-end Rate-Distortion (RD) optimized framework, not just another lightweight model. This provides flexible, user-controlled tradeoffs between quality and size, unlike methods with a single fixed compression level.\n\n**Novelty** Extending RD-optimization to 4D is a non-trivial challenge due to new temporal redundancies. Our novel use of a wavelet transform to enforce temporal smoothness is a principled solution to this, analogous to the evolution from image to video compression.\n\n**Model Size** We report size for a holistic model of the entire video sequence, not a per-frame streaming size. This is a critical distinction for storage comparisons. For instance, our compressed “coffee_martini model” is just 15 MB, while a streaming approach like QUEEN requires ~25 GB to store the full sequence. Our method compresses the complete 4D representation itself.\nWe hope these clarifications help frame our contributions and will now address your specific questions.\n\n---\n## **Q&A**\n\n### **On the Novelty of our RD Framework for 4DGS (W1 & Q1)**\nWe thank the reviewer for the opportunity to clarify the novelty of our work. Our primary contribution is the development of the first end-to-end, bit-level, rate-distortion (RD) optimized compression framework specifically designed for dynamic 4D Gaussian Splatting. We define this as a framework that jointly optimizes for distortion (L_{dist}) and an explicit, entropy-based bitrate estimation (L_{rate}) within a single loss function, enabling flexible control over the rate-quality trade-off.\n\nThe key distinctions from prior art are as follows:\n\n**vs. Pruning/Lightweight Methods (e.g., Compact3DGS, LGS).** These methods primarily focus on reducing model size by \"pruning\" redundant Gaussians. While effective, they do not typically optimize against an explicit bitrate estimation in the loss function. Our framework moves beyond simple model reduction by learning an optimal representation for a target point on the RD curve.\n\n**vs. RD3DGS.** RD3DGS shares the closest philosophy, and we explicitly build upon its principles. However, the crucial difference is the domain. RD3DGS was designed for static 3D scenes. We tackle the new and non-trivial challenges of temporal redundancy that arise in dynamic 4D scenes. This required integrating two core novel techniques. One is the wavelet transform to exploit the smoothness prior of motion trajectories. Another is the differentiated quantization strategy based on our sensitivity analysis of new temporal parameters unique to 4D representations.\n\n**vs. QUEEN.** QUEEN is a valuable contribution targeting the specific application of streaming, which operates in a per-frame manner. This differs from our goal of compressing an entire dynamic scene for flexible, real-time rendering. Consequently, for a full sequence, QUEEN's storage footprint can be substantial (e.g., their official repository indicates model sizes often exceeding 25 GB for the N3V dataset). More fundamentally, the primary distinction lies in the optimization process. Our method is an end-to-end RD-optimized framework that uses an entropy-aware loss to generate a full RD-curve. This allows users to flexibly select a model that fits their specific hardware constraints. In contrast, methods like QUEEN typically apply entropy coding after standard training, providing a few fixed operating points (e.g., S, M, L) with limited flexibility. To the best of our knowledge, our work is the first to introduce this level of RD-optimization flexibility to the 4DGS domain.\n\n**vs. Light4GS.** Light4GS is an important concurrent work, but its strength lies in a different area. It operates effectively within a narrow, high-quality range. In contrast, as shown in the graph below, the novelty of our framework is its flexibility and wide operational range, from extreme compression to high quality, which is a direct outcome of our RD-optimization approach.\n\n```\n  32 |          \n     |                     \nP    |          L2----L1  (Light4GS -- short range of RD)          \nS 31 |                                   _____ Ours L6\nN    |                            ______/\nR 30 |                     ______/\n     |              ______/\n  29 |       ______/\n     |      /\n  28 |   Ours L1 \n     |\n  27 +------------------------------------------------------------>\n     0    1.5   3     4.5   6     7.5   9     10.5   12    13.5\n                            Size (MB)\n```\n\nFurthermore, additional experiments confirm the initial PSNR gap is not a fundamental limitation. By relaxing the rate regularization on our VQ entropy cost, we achieved a performance boost of approximately 1 PSNR  in the same setting as shown in the response in **Discussion on the Rate-Fidelity Trade-off (Q7)** of Reviewer QNNe.\n\nThis proves our framework's flexibility to be tuned for higher fidelity. It suggests the gap stems from our initial tuning for a wide compression range and the chosen baseline, not from a flaw in our compression method itself.\n\nIn summary, our contribution is not merely applying known techniques to 4DGS, but designing the first holistic, end-to-end RD-optimized framework for this new domain, which required novel strategies (wavelets, adaptive quantization) to solve unique 4D challenges. We will revise our related work section to make these distinctions sharper.\n\n---\n### **On the Contribution of Proposed Modules and Ablation Studies (W2 & Q2)**\nWe thank the reviewer for their sharp observation. We agree it is essential to demonstrate the individual contributions of our proposed modules. This analysis is provided in Section 4.3. Ablation Study.\n\n**Contribution of the Wavelet Transform.** Your question about the wavelet transform's gain over a baseline (Ex4DGS + pruning + VQ) is directly answered in Figure 3. This figure compares our full method ('Ours') against the same model with the wavelet transform disabled ('w/o Wavelet Transform'). The results clearly show that our wavelet approach significantly improves the RD performance across all compression levels. For instance, at Level 1, it provides an additional 19% storage reduction while also improving PSNR by 0.19 dB.\n\n**Justification for Adaptive Quantization (Clarification on Table 1).** We understand why Table 1 might appear to show a limited contribution. However, its purpose was not to be a conventional ablation study showing gains from a new module, but to provide an empirical justification for our differentiated quantization strategy.\n\nThe table demonstrates that naively quantizing all parameters is suboptimal. While quantizing base opacities and center parameters works well, quantizing the variance parameters (b*^o) causes a significant PSNR drop (from 29.57 dB to 28.52 dB at Level 6) for a negligible compression gain.\n\nTherefore, this analysis validates our core design choice: a careful, selective quantization approach is crucial for adapting RD frameworks to the 4D domain. This nuanced strategy, justified by our ablation, is a key part of our contribution.\n\nWe believe these results in Figure 3 and Table 1 directly address your questions. To prevent future misinterpretation, we will revise the main text to make the purpose and conclusions of these ablation studies more prominent and readily accessible to the reader.\n\n---\n### **On the Limitations and Future Directions (W3)**\nWe sincerely thank the reviewer for their insightful and constructive feedback. We concur that dynamic points remain a storage bottleneck and recognize the need for more sophisticated motion modeling as a key area for improvement.\n\nInspired by your suggestion to explore more advanced techniques, we have conducted a new set of experiments to analyze the impact of more aggressive temporal compression via multi-level wavelet decomposition. We extended our analysis to include 2-level (keeping 1/4 of the coefficients) and 3-level (keeping 1/8) Haar wavelet decompositions, in addition to our original single-level approach. These new configurations were tested across all 6 scenes of the Neural 3D Video (N3V) dataset and for all 6 of our framework's compression strengths, allowing for a complete and rigorous evaluation of this hyperparameter's impact.\n\nThe averaged results over the N3V dataset are summarized in the table below.\n\n|Compression Level (Paper) | Level 1 DWT (1/2 Coeffs.) | Level 2 DWT (1/4 Coeffs.) | Level 3 DWT (1/8 Coeffs.)|\n|-|-|-|-|\n||| **PSNR (dB)/Size (MB)** ||\n| **L6** | 28.73 / 14.05 | 28.47 / 11.35 | 27.74 / 9.80 |\n| **L5** | 29.59 / 9.14 | 28.57 / 7.51 | 27.72 / 6.50 |\n| **L4** | 29.14 / 6.23 | 28.14 / 5.14 | 27.44 / 4.44 |\n| **L3** | 28.59 / 4.37 | 27.81 / 3.61 | 27.38 / 3.10 |\n| **L2** | 28.27 / 2.91 | 27.57 / 2.42 | 27.17 / 2.05 |\n| **L1** | 27.39 / 1.63 | 26.89 / 1.36 | 26.43 / 1.16 |\n\nAs the results demonstrate, employing a more aggressive multi-level decomposition successfully reduces the model size further. For example, at our framework's Level 1 setting, the size decreases from 1.63 MB (1/2 coeffs) to 1.16 MB (1/8 coeffs), at the cost of a drop in PSNR.\n\nWhile this new experiment provides readers with more information on the impact of advanced techniques, we agree that this is just a first step. Your valuable guidance on exploring more fundamentally sophisticated models, such as a motion basis or predictive coding, has provided a clear and promising direction for our future research in truly overcoming the current limitations."}]

Author rebuttal:

Meta-review: This work proposes a smoothness-aware rate-distortion optimized framework for 4D Gaussian compression. Three reviewers provided final ratings of 2 x borderline accept and accept. The reviewers appreciated the work for its well-motivated design choices, strong performance and clarity of exposition. The reviewers' concerns were addressed during the author-reviewer discussion phase and all reviewers maintained a positive rating post discussion. The AC concurs with the reviewers' consensus and recommends acceptance. Congratulations! The authors should incorporate the changes that they have promised in their rebuttal into the final camera-ready version of their paper.
Decision: Accept (poster)