HybridGS: High-Efficiency Gaussian Splatting Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
Authors: Qi Yang
Le Yang
Geert Van der Auwera
Zhu Li
Venue: ICML 2025 poster (2025)
Keywords: 3D Gaussian Splatting
Compression
Sparse Representation
Primary area: applications->computer_vision

Abstract: Most existing 3D Gaussian Splatting (3DGS) compression schemes focus on producing compact 3DGS representation via implicit data embedding. They have long encoding and decoding times and highly customized data format, making it difficult for widespread deployment. This paper presents a new 3DGS compression framework called HybridGS, which takes advantage of both compact generation and standardized point cloud data encoding. HybridGS first generates compact and explicit 3DGS data. A dual-channel sparse representation is introduced to supervise the primitive position and feature bit depth. It then utilizes a canonical point cloud encoder to carry out further data compression and form standard output bitstreams. A simple and effective rate control scheme is proposed to pivot the interpretable data compression scheme. HybridGS does not include any modules aimed at improving 3DGS quality during generation. But experiment results show that it still provides comparable reconstruction performance against state-of-the-art methods, with evidently faster encoding and decoding speed. The code is publicly available at https://github.com/Qi-Yangsjtu/HybridGS .

HybridGS: High-Efficiency Gaussian Splatting Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
Qi Yang'
Le Yang² Geert Van Der Auwera Zhu Li!
Current Strstogy
Differest Data Format Comples Keunal Nerwoek
Blihhl Castomized Procosiing
Abstract
Most existing 3D Gaussian Splatting (3DGS) compression schemes focus on producing com- pact 3DGS representation via implicit data em- bedding. They have long encoding and decoding times and highly customized data format, mak- ing it difficult for widespread deployment. This paper presents a new 3DGS compression frame- work called HybridGS, which takes advantage of both compact generation and standardized point cloud data encoding. HybridGS first generates compact and explicit 3DGS data. A dual-channel sparse representation is introduced to supervise the primitive position and feature bit depth. It then utilizes a canonical point cloud encoder to carry out further data compression and form stan- simple and effective
Maittnee
image
Compact Representation
Bitrde
?
Syntheetawc
Training
Enooding
Decoding
Slone-
Sgame Representation
Proposed
Strategy
Mulliviaw Image
Explicit, Compart Represeutation
Ritrat
Synthmiioo View
8 Docadinng
Training
Encoding Fest
Compatible Varriou Standardd Codoe
Cleall Fusi/Proull
Exmpenice Fowining
Figure I. Existing generative compression frameworks and the proposed HybridGS.
However, due to the use of the explicit data format and prim- itive densification strategy. 3DGS has huge data volume, which is challenging for storage and transmission. 3DGS compression has attracted considerable attention from both industry and academia, which is also the focus of this paper.
dard output bitstreams. rate control scheme is proposed pivot the in- terpretable data compression scheme. HybridGS does not include any modules aimed at improv- ing 3DGS quality during generation. But experi- ment results show that still provides comparable reconstruction performance against state-of-the- art methods, with evidently faster encoding and decoding speed. The code is publicly available at https: // //t////iiie htt///tttuu///////// com Yangs jtugsngs HybridGS.
In the recent 148-th Moving Picture Expert Group (MPEG) meeting (Liao et al., 2024c), experts from Video Coding (WG4) and Coding 3D Graphics and Haptics (WG7) reached two consensuses. 1) The generation process of 3DGS has significant room for optimization such us- ing less primitive, since 3DGS is surjective: two more distinctly different 3DGS samples may correspond to per- ceptually close content and quality. This direction will be pursued by WG4 to achieve the compact 3DGS represen- tation, where the input and output can be ground-truth and synthetic views with 3DGS or its variants as intermediate results. Vanilla 3DGS shares the same data format with 3D point clouds. It is reasonable extend existing point cloud encoders such as the Geometry- based Point Cloud Compression (GPCC) to support the compression of 3DGS data. WG7 suggested that in this case, both the input and output should be 3DGS data. We shall refer to the above two approaches as generative compression methods and traditional compression methods.
1. Introduction
3D Gaussian Splatting (3DGS) (Kerbl et al., 2023) exhibits exceptional capabilities in 3D scene reconstruction, over- coming limitations that previously hindered the practical deployment of real-time radiance field rendering methods.
'Scool of Science and Engineering, University of Missouri- Kansas City, Kansas, US Ellcttrcca and Computer Engineer- ing. University of Canterbury. Christchurch, New Zealand : "Qualcomm, San Diego, US. Correspondence to: Qi Yang
In literature, most research efforts have been put into gener- ative compression methods and already achieved impressive compression ratios (Chen et al., 2024b; Liu et al., 2024; Niedermayr et al., 2024; Fan et al., 2024). Meanwhile, tra- ditional compression methods have also been considered in some work (Yang et al., 2024; Huang et al., 2025; Chen
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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HybridGS: High-Efficiency 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
al., 2024a). There are significant pros and cons associated with both types of techniques. Specifically, with generative compression, superior compression efficiency without no- ticeable distortion is realizable. However, current generative compression methods normally embed 3DGS primitive in- formation into neural networks or other highly customized data formats, resulting in long encoding and decoding times. In some scenes, the coding latency could become longer than I min (see Table 2). Empirical video-on-demand and video live streaming studies indicate that Is is probably the limit for the user's flow of thought to stay uninterrupted, and 10s is the limit for keeping the user's attention focused on the dialogue (Nielsen, 1994). Besides, to obtain good reconstruction quality over different datasets, some methods need handerafted selection of hyperparameters or incorpo- rate extra optimization for 3DGS generation, occasionally resulting in compressed data with quality even far exceeding that of the original 3DGS. From the quality evaluation per- spective (Yang et al., 2022), the impact of compression then becomes difficult to quantify. These factors make generative compression methods challenging to be standardized and widely deployed (see the upper part of Figure 1).
Depth (BD) of the primitive position and attributes. A dual- channel sparse 3DGS generation scheme is proposed to obtain the desired explicit and compact 3DGS. The dual- channel sparsity here refers to the sparsity of the primitive at- tributes and position distributions. For compressible 3DGS attributes (i.e., color and rotation), we use the "learnable low-dimensional latent feature + trainable lightweight de- coder" to reconstruct them. This can be considered as a generalized Principal Component Analysis (PCA), leading to the desired low-rank representation of compressible at- tributes with reduced information loss. We integrate newly proposed differentiable quantization method (Ye et al., 2024) into 3DGS generation to quantize the learnable primitive features. For primitive position, we propose a Learnable Quantizer-based Method (LQM) to generate unique primi- tives with integer coordinates. Here, owing to the alignment between scene and 3DGS scaling, position de-quantization is not necessarily needed before rendering. The primitive pruning proposed in (Fan et al., 2024) is used to control the primitive number, resulting in two simple but effective rate control strategies. In the second step, GPCC is adopted to produce the standard output bitstream: we split 3DGS data such that the primitive position is compressed in the Oc- tree mode, while other attributes are compressed by RAHT (De Queiroz & Chou, 2016) as normal point cloud attributes.
In contrast, traditional compression methods work with well- defined explicit data formats. With appropriate algorith- mic optimizations, real-time encoding and decoding can be achieved, as exemplified by video codecs such as H.264 and HEVC (Sullivan et al., 2012). This suggests that ex- plicit data representations are more conducive to high-speed processing, standardization, and practical deployment. How- ever, due to the huge volume of 3DGS data, the achievable bitrates under lossless compression may still be too large. Lossy compression thus is more attractive but we need to re- duce the possible noticeable distortion due to indispensable operations such as quantization (Zaghetto et al., 2024).
A significant advantage of the proposed HybridGS is its full compatibility with canonical point cloud encoders, mean- ing that existing achievements in point cloud compression can be inherited and applied. Compared to of-te-of-thh-rr (SOTA) generative compression methods, HybridGS ex- hibits obviously faster encoding and decoding. To limit the scope of this work and, more importantly, gain interpretabil- ity when evaluating is based on the original 3DGS implementation. We delib- erately do not incorporate any methods to optimize 3DGS generation and improve the reconstruction quality. As such, the upper bound for the reconstruction quality of HybridGS is the vanilla 3DGS. This also implies that optimizations such as Mip-splatting (Yu et al., 2024) targeting 3DGS re- construction can potentially be included in our method in the future. Our contribution is summarized as follows.
cempresssio-relatee losses, HybridGS
With the above observations in mind, we present in this paper novel hybrid 3DGS compression framework, Hy- bridGS, that takes advantage of both the generative and traditional compression approaches, as shown in the lower part of Figure 1. It leverages the generative compression to produce explicit and compact 3DGS data, which have the same format as point clouds and are then further compressed using available point cloud encoders. This design has the distinct feature of being able to accelerate both the encoding and decoding processes, typically ranging between Os and 2s. Thanks to the surjective nature of 3DGS, the operations of lossy processing inherent in the downstream point cloud encoder such as quantization can be taken into account in the generation of 3DGS. This property results in the potential for mitigating the distortion while keeping the feasibility of effectively predicting the quality of the compressed 3DGS.
We develop a new 3DGS compression framework, Hy- bridGS, which takes advantage of both the generative and traditional compression methods.
We propose dual-channel sparse 3DGS generation method. The lossy operations in the downstream encoder are considered in the 3DGS generation process, reducing the amount of distortion. Two effective rate control methods are established based on the sparsification strategy.
e Extensive experiments show that HybridGS can provide comparable reconstruction performance against generative compression methods with greatly decreased encoding and
Correspondingly, HybridGS is implemented using a two- step architecture. In the first step, we pre-define the Bit
2

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HybridGS: Eligh-Efficiency 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
decoding time, as well flexible rate control capability.
Robust Quantizer (RQ) for neural network quantization that mitigate perturbation-induced distortion. It has two steps: Perturbation injected Affine Transform for Quantiza- tion (PAT-Q) and Denoising Affine Transform for Recon- struction (DAT-R).
2. Preliminaries
In this section, we first summarize the requirement of using point cloud encoders for 3DGS compression coding. Then, two quantization methods for neural networks are presented.
PAT-Q: the quantization a feature f is formulated as
A() + A()) 1 Imin (2 1),
2.1. 3DGS Encoding with Point Cloud Encoders
Jmas - Iman +
rownd(A(()) A(F) a [-0.5, 0.5].
Point cloud encoders can be broadly categorized into two classes, hand-crafted and learning-based The hand-crafted encoders are studied by MPEG WG7, which have two sub- categories, Video-based Point Cloud Compression (VPCC) and GPCC (Schwarz et al., 2019). VPCC uses existing video codecs to compress the geometry and texture information of a dense point cloud. This is achieved essentially by con- verting the point cloud into a set of video sequences. GPCC was initially designed to compress highly irregular sparse point cloud samples by exploiting an octree- encoding strategy. The baaming-basel point cloud encoders are inves- tigated under MPEG WG7 AI-PCC. UniFHiD (Pang al., 2024), which is based on SparseConv (Choy et al., 2019), is selected as the study anchor.
a is the perturbation due to UQ. é is a small constant for preventing division by zero. The introduction avoids op- erations such as clipping, which can degrade performance.
DAT-R: the reconstruction (de-quantization) is cast into ridge regression problem. The regularization factor À is introduced, and the de- ee--uuattzzationn is realized via solving
lla q b 1 2
0, 2M
where M is the dimension of f. Taking the partial deriva- tives with respect to a and b, and setting the results to zero yield the stationary points, which are
Cou
b og.
(4)
Varg + X"
where Cou, and Var represent the averaging, covari- ance, and variance operators. Finally, the reconstruction of the learnable feature is calculated using
Point cloud encoders require that the input data be in the form of integers regardless of they representing geometry or attributes. SparseConv requires point uniqueness: if dupli- cated geometry points exist, current learning-basee methods will directly remove them. In summary, the requirements of using point cloud encoders to compress 3DGS data are: 1) explicit point-wise primitive description of dimensionality x (3 M), with n primitives, each having zyz coordinates and M -channel features; 2) integer primitive position and feature; and 3) unique primitive for geometrical positions.
Con
r= R(g) = a a Vor, + (4 4)
(5)
Equation (5) reflects that the reconstructed feature has smooth component / as well as a non-smooth component Covfa
Ver,+X (g q) By suppressing the non-smooth term, which contributes to training instability, RQ can better balance be- tween the feature fidelity and quantization noise by adjusting x. This could facilitate quantization-ava training.
2.2. Quantization
3. Method
Uniform Quantizer For a learnable feature f in integers and subject to lossless quantization with respect to N-bit BD after training, the simplest to use Uniform Quantiza- tion (UQ) combined with Straigh--hhoough Estimator (STE) (Bengio et al., 2013). Quantization can be formulated as (f. - fuii) x 12 13 fmux= fmin
The framework of HybridGS is depicted in Figure 2. It has two parts: a dual-channel sparse representation module to generate explicit compact 3DGS, and downstream point cloud encoder to realize encoding and rate control.
g0
D...,..SSS2 (I)
3.1. Dual-Channel Sparse Representation
where the number of floating values, [æ] is the floor function, and fmar, fmin are the maximum and minimum possible values. De-quantization is achieved using
1. SPARSE REPRESENTATION OF ATTRIBUTES
For 3DGS, the sparse representation of attributes can be realized in two aspects: feature channels and precision of feature values. Feature dimensionality reduction is intro- duced to sparsify feature channels, while quantization is applied to decrease the representation precision of the fea- ture value.
Q x (1 Smin pminn Vi O.... S
(2)
2
Considering that the floor function in Equation (1) is not differentiable, STE is used for backpropagating the gradient for training.
Robust Quantizer Recently, (Ye et al., 2024) presented
Feature Channels. Besides the geometry position, vanilla 3DGS has 56 feature channels: channels for color Direct
3

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HybridGS: High- Nigh-EEffciency 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
HybridGS learnable Parameters
Sparsification
Primitive Position
LQM
Color Latent Feature
Color Feature (DC and
Decoder
Rotation Latent Feature
Quantization
Rotation Feature
Vanilla 3DGS
Opacity
s Rendering
Scaling
Training
Rendering Loss
Compact and Explicit 3DGS File
Ground Truth Image
Point Cloud Encoder
Bitstream: 010100...
Figure 2. Framework of HybridGS.
Current (DC) components, 45 channels for color Spheri- cal Harmonic (SH) coefficients, 1 channel for opacity, 3 channels for scaling, and 4 channels for rotation. Exclud- ing opacity, PCA can be used to partition the remaining 55 features into compressible and compression-vulleraae ones. The concentration of the feature variance in limited number of principal vectors suggests that a certain feature is compressible. However, using PCA directly in an offline manner leads to loss of dation terms of reconstruction PSNR, as summarized in Appendix A.2. This indicates training-based approach for representing compressible features should be pursued.
propose a robust training method inspired by neural network
quantization.
Loss
PAT
DAT-R
Rendering
details and degra-
Splice mooes file
Mera dama
Encoder
Zibrate
Transmission
Decoder
E. Primutive Feature
a,b
q Quantieed Feature
Vanilla 5005
t: Recomutracted Feature
me
Noting that PCA works linear autoencoder (AE) (Lind- holm et al., 2022), we employ low-dimensional latent fea- tures / and a lightweight decoder D (Girish et al., 2025), a MultiLayer Perceptron (MLP) with one hidden layer, to reconstruct the high- sigh--imensional compressive attributes. In other words, we only constrain the latent feature dimen- sionality realize low-rank approximation of features (see Appendix A.2). Mathematically, we have
Figure
Robust Quantization 3DGS features.
For the latent representations of color and rotation, opac- ity, and scaling, we initialize a quantizer for each attribute. Using RQ as an example, the training process is shown in Figure The reconstructed features are used to calcu- late the rendering loss, while the quantized features q are used for compression and transmission. For RQ, two addi- tional meta data, and b are required to de-quantize y and compute r. A similar technique can be established when employing UQ. We only need to save different meta data for the de- de-quaatization operation. After quantization, we can calculate the number of bits per primitive. Assume that the channel numbers for latent color and rotation features are ke and k, and the BD for position, color, opacity, scaling, and rotation attributes are BDp, BD., BD,, BD and B D,,
F, P.((f)) R*x4, fr Rax*r, k. 4,
(6)
where F, denotes the 3DGS primitive rotation feature to be compressed. fr and D, are the corresponding latent feature and decoder.
PCA analysis indicates that color and rotation attributes are compressible features, while scaling is compression- vulnerable feature especially for complex samples such as those in the "bicycle" dataset. Therefore, in HybridGS, we use tow-dimensional latent features for color and rotation reconstruction (see Equation (6)). Moreover, for color DC and SH, they share the latent feature and the decoder.
The number of bits per primitive is thus
Pos = 3- (BD, BD.) + k2 BD, + BD, + k, BD,- (7)
3.1.2. PRIMITIVE SPARSIFICATION
Feature Precision. For vanilla 3DGS, all primitive at- tributes are in floating-point numbers. Quantization is thus required before the downstream point cloud encoder can be used. Taking quantization into account in 3DGS gen- eration can significantly reduce quality degradation than using it as post-processing step (see Appendix A.7). We
Primitive positions determine the skeleton of 3DGS. More primitives should lead to more detailed texture informa- tion but larger data volume. However, (Fan et al., 2024) showed that approximately 60% of the primitives contribute marginally to the reconstruction quality. An improperly large number of primitives can even undermine the repre-

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HybridGS: High-Effiiiency 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
sentational capacity of some primitives (see Appendix A.5). Hence, the sparsification of primitives needs to control prim- itive number via pruning, and it also has to satisfy the re- quirement of position uniqueness as specified in Section 2.1.
nodes are set during 3DGS training: the densification end point Ta, the uniqueness end point Tu, the pruning start point Tp. and the top quality point Trop. We expect the best reconstruction quality to be reached after densifica- tion. We then gradually decrease the number of primitives by removing a certain percentage of less important primi- tives. The uniqueness ensures that the final 3DGS do not have geometrically duplicated primitive. Therefore, we set Ta < Teop < Tp < Tu R T. The uniqueness operation should be less frequent than the densification in order to pre- vent immediately reverting the densification result, and we keep the primitive that has the largest size based on scaling attributes. An example of primitive sparsification training is shown in the lower part of Figure 4. Intuitively, Trop is the theoretically optimal in terms of reconstruction performance for stopping the training. But after operations like pruning, the quality of 3DGS may not degrade much and in some cases, could even exhibit an improvement.
Primitive position can adopt the same quantization method (UQ or RQ) as other attributes. Here, we additionally design a LQM to generate integer position that can be rendered di- rectly without de-quantization. This corresponds that the quantized position can illustrate the same content through adjusting the camera position accordingly.
Define the coordinate range as -2N-1 + 1 <= 2, 2 1. Therefore, for different N, LQM will gener- ate 3DGS samples with different scales. LQM consists of four steps as shown in the upper part of Figure 4: 3DGS warm-up, 3DGS translation and scaling, primitive position decomposition, and primitive uniqueness and pruning. A naïve 3DGS is first generated after warm-up training based on the vanilla 3DGS. Then, we shift the 3DGS sample to ensure that its Bounding Box (Bbox) center is at the origin. Next, we rescale the 3DGS sample according to the space size cireled by BD, in which the primitive position, 3D co- variance matrix, and camera position all need to be adjusted (see Appendix A.3). After that, round the positions coor- dinates to integers and decompose these integer positions as the inner products of two vectors, namely the basis vector and coding vector (see Appendix A.4). Considering that the basis vector is fixed as [2 -2 2 ...,, 4, 2, 1] given a certain BD, the training reduces to optimizing the coding vector consists of -1. 0, and 1, In each training epoch, we use the basis vector and coding vector recover the integer position and calculate the loss after rendering.
3.2. High-Efficiency Coding
The generated 3DGS samples consist of several parts: an explicit and compact point cloud file, meta data for de- quantization, and two small MLPs for latent features. Meta data and MLPs take several kB, which can be directly stored or transmitted, while the compression is performed on the point cloud file. Considering that GPCC is more stable and effective for point cloud geometry and attribute compression, we use it the output encoder.
3.2.1. IMPLEMENTATION
Vanilla GPCC supports two types of input, "xyzr" or "xyzrgb". "P" stands for reflectance and "rgb" denotes the color. Both reflectance and color compression are based on RAHT or Predlift. At the implementation level, there are thus two feasible approaches. 1) We may improve the inter- face of GPCC to allow 3DGS data input. Primitive po- sition then inherits the compression method of point cloud, while using RAHT/Predlift to compress other primitive at- tributes. Since the compression of different channels can be treated independently, parallel computing methods can be utilized to accelerate encoding and decoding (e.g., OpenMP (Chandra, 2001)). 2) Considering that the first method needs to modify the internal codebase, another strategy is to di- vide the generated samples into multiple subsamples with "xyza" or "xyzala2a3", while "a" represents one channel of primitive attributes. The GPCC can be then used directly. After decoding, we may merge the subsamples together to recover the 3DGS file.
NAON-2DGS 2012
Deptt
Decouponticn
Boks Vettar
Reaziinal Priraitio Poeition
Thieer Prodiort
Fodition
i Sesiled Soos
Soottion
Ceding Water
Uneiqpeever and Fiiriing
Top Deality Tie
Chiggaaass
Devilicattonn
Thitiin T
LQM
Figure 4. Scheme of LQM.
For uniqueness and pruning, naïve approach is to adopt one time node after 3DGS converges to stable good perfor- mance, where uniqueness and pruning are performed, and then fix the primitive position until the optimization of other attributes pivoting new geometry skeleton is finished. How- ever, in some cases, pruning and uniqueness may remove few essential primitives, resulting in substantial degradation of the 3DGS quality.
To avoid this, we propose progressive integer primitive uniqueness and pruning training strategy, which can re- duce quality fluctuations and satisfy the rate control for random access. Given the entire training time T, four time
3.2.2. RATE CONTROL
Rate control is important for practical applications. How- ever, with the current generative compression methods,
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HybridGS: High-Efffiieng 3DGS Data Compression using Dual- Sparse Representation and Point Cloud Encoder
which use the rate-distortion (RD) optimization as the loss function, the final data rate is difficult to predict. On the contrary, the proposed HybridGS is easier to realize accurate rate control. The bit number of the explicit 3DGS gener- ated in the first step is easy to calculate (see (7)), since we know the primitive number and the assigned bits for each primitive. For the downstream compression, using lossless compression as an example, the compression ratio of the cur- rent point cloud encoder is relatively stable. For GPCC, the lossless compression ratio is around 3-4x for dense point clouds, 2x for large-scale sparse point clouds. Therefore, we can perform rate control by controlling the size of the explicit 3DGS with downstream compression ratios.
tion is proposed to mitigate pronounced quality fluctuations: the difference between target B and initial Din is A BDini - Dtar. With disabling pruning, we gradually reduce Binni with step size 1 until A = 0.
maw Q(GS).
s.t., R(GS) & B and RGGS) N (pbie AppIe) (9)
Appa (he +1+3+
4. Experiment
4.1. Experiment Settings
Datasets: To comprehensively evaluate the performance of the proposed methods and clearly illustrate the loss caused by compression, we select five scenes from dif- ferent datasets: "playroom" from deep blending (Hedman et al., 2018), "train" from tanks&temples (Knapitsch et al., 2017), "bicycle" and "room" from Mip-NeRF360 outdoor and indoor scenes (Barron et al., 2022), and first frame of (dance) from PKU-DyMVHuman (Zheng et al., 2024). The partition of training and test im- ages follows the same rule as for the vanilla 3DGS project. The results of overall and other scenes in the above datasets are given in Appendix A.8.
Intuitively, there are three factors that can influence the size of the explicit 3DGS: feature channels, feature BD, and primitive number. For given scene, we do not know the vanilla data volume before Toop. which indicates that we need to change these parameters to satisfy the bandwidth requirement after Top.. Changing feature channels during training results in unstable results and extremely expensive training costs, while changing feature BD and primitive number are more promising for rate control. Therefore, we propose two rate control methods here. We only consider lossless mode of the point cloud encoder for simplicity.
"Dance JDance_dunhuang-re
We employ 3DGS as an anchor method and compare both types of representative methods. For generative compression methods, we select Scaffold-Gr GS (Lu et al., 2024), Compact3D (Lee et al., 2024), C3DGS (Niedermayr et al., 2024), CoMPGS(ECCV) (Navaneet et al., 2025), LightGaussian (Fan et al., 2024), Eagles (Girish et al., 2025), HAC (Chen et al., 2024b), and (MM) (Liu et al., 2024); for traditional compression methods, we select GGSC (Yang et al., 2024) and HGSC (Huang et al., 2025). The results of these methods are from their original paper or official code.
Comparison methods:
Method 1 Controlling Primitive Number: the progres- sive pruning proposed in Section 3.1.2 can realize the soft primitive number control. Given the bandwidth limitation as B, the rate control can be formulated as the following optimization problem:
max Q(CS),
s.t., R[GS) & B and RGGS) = Pars L
where GS represents the explicit 3DGS samples, n repre- sents the target primitive number, Q(-) and R((-) represent the quality and bitstream of GS, and L is the lossless com- pression ratio of the downstream encoder. The number of primitives in Trop is ntop the number of primitive need to be pruned is Mp = noop - n. Based on the training epoch and the pruming interval Lp. the pruning is performed F, = T-Tz times. Each time N' Ne primitives are pruned until the training ends.
The BD of the primitive position and other attributes are set to be 16. Training epochs are fixed at 70, 000. Ta. Tp, and Tú are set as 15, 000, 36, 000, and 66, 000 epochs. We report the results of HybridGS with k. 3/6 and k, = 2 as for latent representation. RQ () = 1) 2) is used to introduce quantization during training. Other hyperparameters are shown in Appendix A. Over the course of pruning, the explicit compact 3DGS will has fewer primitives, resulting in a lower bitrate. We select the samples in the 50, 000 and 70, 000 epochs as High and Low (i.e., pruning 47% and 75% primitives) Rate (HR, LR) points of HybridGS. We use the second implementation in Section 3.2.1 with the "xyza" mode of the GPCC test model v23 (WG7, 2023). The lossless octree and RAHT mode are applied (see Appendix A.7 for results using other encoders).
Model Parameters:
Fp
Method 2- - Adapting Feature BD: reducing the feature BD can also lower the bitrate but this is more complex theo- retically. In Section 3.1.1, we have demonstrated that fea- tures show heterogeneous compressibility. The bandwidth savings are ideally derived from the feature that contributes the least to distortion. Accurate modeling of the distortion sensitivity across different features in 3DGS is currently ab- sent. Hence, we focus on the most straightforward method: bandwidth savings are distributed evenly among all features except for the primitive position as (9), where Apba is the bits needed be reduced for each primitive, and A is the BD reduced for each features. A progressive BD reduc-
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HybridGS: High-Efffiienge 3DGS Data Compression using Dual- Dual-Channel Sparse Representation and Point Cloud Encoder
Table 1. Performance of HybridGS. "p" represents the pruning rate of LightGaussian, "X" represents the RD loss weighting factor.
Dutrect
Aastiom [MBs]
Stycle
roomh
darce
Type Anchre
Maiic
PSNE
FSNR
(MB)
PSNR
IMR:
35ka7
156.73
144334
2001
25:00
22:15
05.14
24.30
20800
51.35
135109
34.77
7.28
CumpusSD
4.23
Eagles
21.74
25.00
112.00
36,00
2.93
Genamaiie Comprrsdde
Lightaassisn
p-ity
25115
3:21
25.55
25:10
7:27
03:9
HAC
0.004
30.03
1034
3.95
22.53
12:25
7.96
24.81
25:00
26.90
03:07
31.44
5.54
56.89
39:30
0:055
0.27
Compas
0.001
36.11
6.31
22.08
8.05
24.74
22.09
30.90
8.99
37.45
2.54
(MM)
A-
0.000
28.98
4,85
21.78
0:21
25.45
14.02
20335
1.36
35:35
2220
Traditioall Comprracion
GRSC
Hose
FR
HK
1.8
LX
20.15
28.83
20.20
27.30
20015
131.24
131:21
96.82
20.33
19.90
15.35
16.04
50.16
41.79
57:54
19:30
20.54
20.99
15.25
18:31
223.26
202.20
234.80
100.36
29.46
2907
25.45
30.38
100.93
57.41
4625
76.27
53:25
54.59
33.42
55.46
9:36
1436
7221
6:30
Hslastos
HR
20.00
1215
21.36
6.17
24.08
22.38
6.33
20.25
120
Preposed
Ne N.
TIX
LX
20.2
3:8
20.6
200
23.53
34:10
11.01
59225
336
128
27.65
Au K.
LX
20.05
20051
37.76
4.2. Experiment Results
4.2.2. CODING TIME
4.2.1. QUANTITATIVE RESULTS
One of the advantages of HybridGS is faster encoding and decoding. We use three SOTA methods, i.e., HAC (A = 0.0005), CoMPGS(MM) (A = 0.001) and HGSC, as benchmark techniques. LR and HR coding times of Hy- bridGS with k, = 3, kr = are tested, and the results of "bicycle" and "dance" are shown in Table 2. We employ serial encoding and decoding while recording the CPU com- putation time for HybridGS with GPCC. Data I/O time is excluded. The time for processing primitive position and other attributes is presented separately for the HybridGS LR case. We can see that the coding time of HybridGS is obviously faster than the current SOTA methods. paral- lelization techniques are employed accelerate the GPCC encoder, real-time coding is possible for 3DGS, which is extremely valuable for dynamic 3DGS streaming. We also test de-quantization time ldeg and latent decoding time lmlp as preprocessing before rendering. For "bicycle", color tdeq and lmip are around is and 0.9s, while 0.6s and 0.001s for rotation. For other attributes, the processing time can be estimated proportionally.
Table reports the results of HybridGS and other SOTA 3DGS compression methods. We see that: 1) SOTA genera- tive compression methods, e.g., HAC and CoMPGS(MM)) show impressive compression ratios, sometimes with even better PSNR than vanilla 3DGS; 2) SOTA traditional com- pression methods report significantly larger bitstreams than generative compression methods similar PSNR, e.g., for "playroom", CoMPGS(MMM) reports 28.98 PSNR with 4.89 MB, while HGSC shows 28.83 PSNR with 96.82 MB. Therefore, only using vanilla 3DGS output as the com- pression target without modifying the generation process cannot achieve a satisfactory bitstream size; 3) HybridGS can realize a comparable compression ratio with the SOTA generative compression methods with close PSNR, some- times even better. For example, "playroom", HybridGS HR has the same PSNR C3DGS while having a smaller size. On "dance", HybridGS is better than CMMP in all cases. HybridGS is better than LightGaussian ex- cept "room"; 4) for HybridGS, increasing the dimension- ality of the latent features can improve PSNR, at the cost of a larger bitstream; 4) RD curves of HybridGs, HAC, and CoMPGS(MM) on "dance" and "bicycle" are shown in Figure 5. HAC reports a non-strictly monotonic curve on "bicycle" caused by stochasticity, which lies in the ren- dering nature of 3DGS itself. HybridGS shows slightly higher PSNR than the upper bound on "dance", which is caused by the elimination of redundant primitives via prun- ing. HybridGS does not outperform HAC in terms of the RD curve, but it offers a notable improvement in encoding and decoding speed, as reported in the next section.
4.2.3. RATE ALLOCATION
Table 3 provides detailed explanation of bitrate distribu- tion. Data in the parentheses denote the size before GPCC. The primary position accounts for 10% of the bitstream, while other attributes exhibit a bitstream roughly propor- tional to their respective channel numbers. However, the compression ratio of GPCC on position larger than other attributes, indicating that: 1) RAHT needs to be further improved for 3DGS attributes, and 2) HybridGS generates extremely compact attributes. The size of the latent feature decoders is only related to the feature channels. The quan- tized metadata, as well as the feature decoders, are very small and can be ignored during rate control.
blcyde
cance
34.6
uppar bound
2014
12
4.2.4. RATE CONTROL
362
Two different methods for performing rate control are pro- posed in Section 3.2.2. Four rate points are chosen for "train" and "dance" and the results are presented in Table 4. The
36.
20.0
May
Sizepip:
Figure 5. RD curve.
7

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HybridGS: High- fligh-Efficiency 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
Table 2. Coding time.
Table 3. Rate allocation of HybridGS.
Table 4. Rate control of HybridGS
Melhod
bicycle
room
Components
bicyele
danec
Instae
train
Enc-Doc (sccond)
Tosal Size
2188 MB
Eypc
Mathed
M
HAC
85.03/80.09
17.04/15.90 90
ppiiuttve number
1,286,254
56,250
PSNK
Keal Kire
PSNK
Real Kite
HGSC HR
COMPGS(MM)
HGSC
124.22/52.94
132.32/72.13
36,29/22.47
31.64/13.39
35.65/16.45
7,82/6.28
color (latent festure)
-opacitt
pocitio
scaling
2,40 (2.45)
7,07 (7.36)
6.39 MD (7.36)
2.72MB (7.36)
0.16 MB (0.32)
0.28 MB (0.32)
0.31 MB (0.32)
0.11 MB
T0MB
MMB
KMB
M
21.34
21.15
839
396
135
14:20
3:13
2.50
HybridGS
HybridGS HR
0.6640.92
1.671177
0.4470.47
0,26/0.46
-metadata
rotation Clatent (eature)
4,30 MD (8.91)
4 KBB
0.18 MB (0.22)
4 4KB
6.3 MBB
37.40
-position
0,09/0.08
0.03/0,04
-color docoder weights
13 KR
13 KR
-attribute 0,57/0.84
0.23/0.32
-rotation decoder weighes
KKB
4 KK
2MB
35500
3936
lossless compression ratio L of GPCC in 3DGS takes values within the range of 1.3 to 1.5 based on preliminary experi- ments, we set I, = 1.3 here. We see that: 1) in most cases, method 1 demonstrates a more precise approximation to the target bitrate compared to method 2; 2) "*"" in "dance": method means that we cannot achieve the target rate solely by adapting BD. The primitive position rate is larger than the target rate, resulting in attribute BDs reduced to 0; 3) method reports larger rate error for 10 MB of "train" than other rates. The reason is that different primitive densities will influence the lossless compression ratio. For GPCC, the denser the point cloud, the higher the compression ratio. An improvement of the proposed rate control strategy is to first calculate the point density to obtain a more accurate estimation of L; 4) L on 3DGS is lower than the traditional point cloud. The geometric distribution of 3DGS is charac- terized by local density, global sparsity, and the presence of solid regions, distinguishing it from traditional dense point clouds that are hollow with only surface representations. There is a room to further optimize GPCC on 3DGS com- pression. We also find after Tp. although we allow optimize primitive position, LQM will not generate duplicated primi- tive position at most cases because 3DGS is very sparse, so uniqueness will not influence rate control methods proposed in Section 3.2.2.
improve PSNR. It indicates that selecting a proper BD can facilitate saving bandwidth. For different quantizers, we can see that: 1) for "train", RQ reports higher PSNR un- der low BD cases; 2) for "dance", RQ and UQ have close performance; 3) "train" " is more challenging than "dance", therefore we believe that RQ is a more robust method for complex content and low bitrate conditions.
5. Related Work
Our work is inspired by previous efforts in 3DGS compact generation and 3DGS data compression. In this section, we will first briefly introduce the achievement of 3DGS compact generation in academia, then summarize the explo- rations of 3DGS data compression within MPEG.
5.1. 3DGS Compact Generation
Multiple methods have been proposed to produce a more compact 3DGS, in which additional constraints are injected into the training process to affect and control data generation. Considering that excessive primitive growth is one of the main reasons for the huge data volume, CompGS(ECCV) (Navaneet et al., 2025) and C3DGS (Niedermayr et al., 2024) proposed using the codebook to restrict the number of primitives during densification, which can be regarded as the pioneer of the 3DGS compact representation. Scaffold- GS (Lu et al., 2024) realized structured GS generation by introducing MLP during training, in which data are implic- itly stored in MLP and significantly reduced storage and memory cost, Based on Scaffold-GS, HAC (Chen et al., 2024b) proposed using multiresolution hash coding to train a context model, which can realize approximately 60 x re- duced storage. ComPGS(MM) (Liu et al., 2024) shared a close concept with Scaffold-GS, in which a group of anchor primitives is selected to realize inter- inter-primitive prediction. They also formulated rate-constrained optimization to bal- ance the quality and bitrate. LightGaussian (Fan et al., 2024) proposed a Gaussian pruning strategy, followed by SH distillation and vector quantization. Due to fewer primitives and lower SH dimensions, LightGaussian realizes a 15 x reduction and 200+ FPS. ContexIGS (Wang et al., 2024) designed autogressive model that encodes GS primitives with multiple anchor levels, which achieved higher com- pression ratio than HAC. Based on these studies, MPEG WG4 (Liao et al., 2024b) decided to add 3DGS to the work-
Table 5. HybridGS with different BDs and quantizers.
Dataset
train
Type Kate
RQ
UQ
High Rate
High
Low
BD
PSNR
SIZE
FSNR
SIZE
FSNR
SIZE
PSNK
SIZE
12
20.06
1.54
19.71
0.74
19.98
1.45
19.58
0.70
20.95
2.50
20148
20.72
2.50
20.37
1,23
14
21.14
3.37
20.95
1.62
21.20
3.48
20.80
1.71
15
21
4.96
20.96
2,40
21.42
5.09
20.92
2
21.49
21.04
21.06
21.24
2.89
dunce
12
39.23
0.84
37.58
0.41
39.32
0.84
37.69
0:41
13
37.59
39.35
37.59
0.45
14
39.50
1.15
37.85
0.56
30.33
1.14
37.92
0.56
15
39.43
1.20
37.91
0.58
39.19
1.25
37.93
0.61
16
30.31
1.52
37.78
0.64
39.35
37.85
0.66
4.2.5. INFLUENCE OF BD AND QUANTIZER
Table 5 reports the performance of HybridGS with different
BDs on "train" and "dance". 12 to 16 BDs are tested with k. = 3, hy = 2. We can see that: 1) with the increase of BD. HybridGS generally presents better PSNR and larger size; 2) for "train", we can use BD larger than 16 to fur- ther approach the upper bound; 3) based on "dance", when reaching certain threshold, only increasing BD will not
8

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HybridGS: High-Efffiieng 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
ing draft and standardize its compact generation method. Several exploratory experiments are presented and discussed in the 147-th MPEG meeting, such as performance analysis of LightGaussian compression using neural network com- pression (Kim et al., 2024) and 3DGS for light field video transmission (Kawai & Nakagami, 2024). The industry held an optimistic outlook on the practical applications of 3DGS, advancing the standardization process of 3DGS compres- sion and streaming.
primitives occupy the same spatial location; therefore, primitive uniqueness is necessary step after quantization before using the SOTA LPCC method. Based on the results in Appendix A.7, it will incur severe quality loss. Other re- lated topics were also studied: (Gao et al., 2024) compared the 2D rendering loss and 3D reconstruction supervision for 3D GS compression, they found 2D rendering loss is more effective than point-wise point cloud quality metric (i.e., point-to-point). Although SOTA point cloud metries (Meynet et al., 2020; Yang et al., 2022; Zhu et al., 2024; Yang et al., 2023) have not been tested, it indicates that 3D GS requires specialized quality assessment studies with respect to lossy compression.
5.2. 3D GS Data Compression
The vanilla 3DGS primitive consists of multiple attributes: position, DC color, SH coefficients, opacity, scale, and ro- tation. It can be regarded as a sparse point cloud with high-dimension point features. Inspired by the point cloud compression method, GGSC (Yang et al., 2024) is the first traditional 3DGS compression benchmark, which is based on graph signal processing and uses two branches to com- press the primitive center and other attributes. At the same time, MPEG WG7 established joint EE with WG4 to plore how to make 3DGS compatible with the current 3D data compression codec, such as GPCC (WG7, 2023), which is the so-called traditional compression method. Some pre- liminary experiments have been conducted base on current GPCC codec. The proposal (Fujii et al., 2024) treated each 3DGS as a point cloud with attributes of opacity, size, quater- nion, and SH coefficients for coding. They used Octree to code the center and RAHT (De Queiroz& & Chou, 2016) to code other attributes. All attributes are quantized by being multiplied by 1000 and then converted to an integer. The conclusion is that the current GPCC implementation is not effective enough to deal with GS attributes. To better ana- lyze the reason, proposal (Zaghetto et al., 2024) explored the influence of center quantization. The conclusion is that the optimal quantization BD is around 14 to 18 BD correspond- ing to different datasets: 16 to 18 BD for Mip-NeRF360 (Barron et al., 2022), 18 BD for Tanks& Temples (Knapitsch et al., 2017), and 14 BD for Deep Blending (Hedman et al., 2018). Compared with general point cloud samples used in the PCC study, 3D GS samples require a higher BD. There- fore, 3DGS center quantization introduces quite a large distortion corresponding to the current PCC method.
Taking into account the above evidence, using the point cloud codec to compress 3DGS data deserves commensurate attention and technological improvement. Therefore, in this paper, we solve the problem of 3DGS quantization and primitive uniqueness, serving as catalyst for new research endeavors on 3DGS compression.
6. Conclusion
This paper presents new 3DGS compression method Hy- bridGS, which first generates explicit and compact 3DGS file and then uses canonical point cloud encoders to realize high- nigh-efficient coding and flexible rate control. HybridGS has dual-channel sparse representation during 3DGS gener- ation, including feature dimensionality reduction, quantiza- tion, and progressive primitive position control. HybridGS reports comparable performance with SOTA methods and faster coding and decoding, as well as demonstrating char- acteristics of interpretability, compatibility, and alignment with the demands of standardization.
The optimal compression efficiency of Hy- bridGS lower than end-to-end generation compression methods using RD loss (Liu et al., 2024) as supervision.
Limitations:
Future Work: HybridGS requires hyperparameters like latent feature dimension and BD. An adaptive parameter selection algorithm may help HybridGS achieve a better quality and size tradeoff. For rate control, different from reducing the BD and primitive pruning, decreasing the latent feature dimension during 3DGS generation can lead to fea- ture space collapse, as well as training a new decoder with extra time. How achieve smooth latent feature dimension reduction without significantly affecting the rendering qual- ity is thus a research topic worthy of investigation. Besides 2D rendering loss, explicit 3DGS generation might bene- fit from new loss functions in 3D space such as Chamfer distance to optimize primitive distribution to improve point cloud encoder efficiency (Yang et al., 2023).
Besides GPCC, Learning-basedd Point Cloud Compression (LPCC) framework studied in MPEG AI-PCC (WG2& & Requirements, 2024) should theoretically also be able to handle 3DGS data. The requirement of adding radiance field coding as a new section for MPEG AI has been proposed (Liao al., 2024a). SparseConv (Choy et al.. 2019) is the key technology used in the current SOTA LPCC framework, such as PCGC (Wang et al., 2021) and SparsePCGC (Wang et al.. 2022). However, SparseConv requires that the spa- tial coordinates be integers, and each spatial location must contain only one primitive. Vanilla 3D GS allows multiple
9

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HybridGS: Hlih-Effiiiency 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
Acknowledgments
Gao, X., Li, S., Li, H., Liao, Y., and Yu, L. Be2: Comparison
of 2d rendering and 3d reconstruction supervision in 3dgs compression. ISOЛTEC JTC I/SC 29/WG 4 m68849, 2024.
This work is supported in part by an award from NSF 2148382, and gift grant from Qualcomm.
Girish, S., Gupta, K., and Shrivastava, A. Eagles: Efficient accelerated 3d gaussians with lightweight encodings. In European Conference Computer Vision, pp. 54-71. Springer, 2025.
Impact Statement
This paper presents work whose goal is to advance the field of 3D Gaussian Splatting Compression and Machine Lear- ing. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.
Hedman, P., Philip, J., Price. T., Frahm, J.-M., Drettakis, G., and Brostow, G. Deep blending for free-viewpoint image-based rendering. ACM Trans. Graphics, 37(6); 1-15, 2018.
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HybridGS: High-EEffiiiene 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
A. Appendix
A.I. Model Parameters
For LQM, before generating the naive 3DGS, we first conduct outlier removal to clean the output of COLMAP, facilitating the following 3DGS translation and scaling (see Appendix A.3). We use the function "remove_statisticall _outlierr from Open3D (Zhou al., 2018) realize outlier removal. Two parameters used in this function are set "nb_ iib_neighbors 50" and "std ratio 2.0" suggested by the official documents. The first t 7, 500 epochs are used for 3DGS warm up, followed by 3DGS translation and scaling. After 3DGS primitive position decomposition, Adam optimizer is used for coding vector training with initial learning rate of 1 x 10-5. The initial learning rate of the scaling attribute is set to 0.2 x log(k) x scaling_Ir(t)) after 3DGS scaling, where scalin (ealing_lr(t)) ) is the original scaling learning rate in t epochs. The uniqueness, densification and pruning intervals are 500, 100, and 2500. For uniqueness, it is activated from 7,500 to 66,000 epochs. After 66,000 epochs, we fix the primitive position and update only other attributes. For the position that has more than one primitives, we keep the primitive that has the largest size based on scaling attributes, other primitives are removed directly (given a primitive with scale [S.,, Sy, S2., the size are defined V S2 x Sy S2). For pruning, each time we remove 0.1% % primitives. For color and rotation latent feature decoders, we use single-layer MLP with 50 hidden units and ReLu as the activation function. The learning rate of latent features and decoders for color are 0.001 and 0.001, while 0.005 and 0.015 for rotation. Other parameters are the same as in vanilla 3DGS, All the experiments are tested on Intel Core 19- 1-9409H, NVIDIA RTX 4090 Laptop.
A.2. PCA Analysis and Learnable Low-Rank Approximation
We randomly select 10, 000 primitives from the "bicycle" and "dance" datasets. The PCA results for color, scaling and rotation attributes are shown in Figure 6. We can see that: 1) for color, a significant portion of the feature energy is concentrated within the first 20 principal components under both "bicycle" and "dance"; 2) for scaling, the energy is more evenly distributed under "bicycle", while under "dance", most energy in the first principal component. These results indicate that the scene characteristics affect the scaling attribute distribution greatly. Considering that the scaling features not always compressible and they have three channels only, we do not attempt extract their latent representation; 3) for rotation, most energy is concentrated within the first two principal components. Therefore, we use fr = 2 in Section 4.
bicycle color
bicycle scaling
bicycle rolation
25
250
240
70
EigenValuve
15
EgenValue
930
ElgenValus
220
10
210
50
200
190
0
30
6
10
16
20
25
30
35
40
45
ElgenVector Index
ElgentVector Index
EigenVector Index
diance color
dance rotation
dance scaling
1200
80
80
1000
FigenValus
15
Eigenvalle
800
EicenValus
10
600
200
20
30
35 40
2
ElgenVector Index
EigenVector Index
ElgenVector Index
Figure 6. PCA results of "bicycle" and "dance".
We use data from "dance" to illustrate the difference between using PCA and the proposed learnable low-dimensional latent features with lightweight trainable decoder. After obtaining vanilla 3DGS samples, we perform dimensionality reduction
12

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HybridGS: High-Efficiency 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
via PCA for color and rotation attributes with the first and principal components retained, which is then followed by reconstruction. Meanwhile, considering that PCA works linear auto-encoder (AE), without nonlinear activation functions such as ReLu and re-train HybridGS with k. and k, = 2.
one-hidden-layer MLP
Table 6. PSNR comparison under different compressive latent feature extraction methods.
Method
PSNR
vanilla 3DGS
39.83
PCA
21.50
HybridGS (Linear Decoder) kip = 6, kip
39.27
HybridGS (with ReLu)
39.31
hic = 6, k = 2
We see that the PSNR of the proposed HybridGS with trainable latent feature and decoder is obviously higher than directly applying PCA. The reason that making both the latent feature and decoder learnable under the supervision of the reconstruction loss enables a better low-rank 3DGS feature representation, as explained below.
Using PCA to realize dimensionality reduction and reconstruction is equivalent to linear AE in the sense that the objective learn low-dimensional representation z E f2ex1 of the 3DGS feature R where q < p. The associated encoder and decoder can be formulated
2 = fo() = Wex + be, x= x= ho() = Waz + by-
(10)
By the minimization of the squared reconstruction error, we have, with 0 {We, Wa).
arg min Σ || (WaWea, + Wab + ba)l1
(11)
Since be is free parameter and the encoder mapping simplifies W., the optimal value for ba is
ba
n M
c WaW,.,) (I WaW.)=,
(12)
where DE F4. The objective Equation (11) thus simplifies to
We, Wa =arg min W., Wa
| - WaWoro..1?
(13)
arg min We
|oo-
Xalla
where 20,1 = x4 -1 I, Xo = [20,1, Fo.n] and Xo = [ ž0 .... 2o with žo.: = WaWezo, is the reconstruction of the centred ith data point. PCA can be considered finding the optimal rank-q approximation X, of the centered data matrix Xo in the sense of Frobenius norm minimization. Applying the singular value decomposition (SVD) to Xo results in Xo = -UVT. Using partitioned matrix notation, we have
Σ
0
U = U11 U2). Σ
V
[V]
V2]
(14)
0
Σ2
where U1 E Rox9, Σn E R9x9, and Vi E Raxq, Therefore, the best rank-q approximation of X, is then obtained by replacing Σn by zero matrix, and consequently,
= - W.W.Xo.
(15)
Choosing W. = Uf and Wa = U₁ attains the desired result:
Σ
0
VTT
W.W.X, U Uz]
(16)
Σ2
2
13

---

HybridGS: High-Efffiieng, 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
The encoder thus represents the input data X in low-dimensional space using Z W. = =
More generally, the low-dimensional representation Z' may be found via solving
min 1 Uf XII
6'
(17)
s.t. 2 fg fg)(() (X).
where f (.) is an encoder having both linear and nonlinear transformations. Introducing nonlinear transformations into the encoder may be able to improve the reconstruction performance, while the use of 2-norm loss function should tend push the majority of the energy into a low-dimensional feature space. However, the above dimensionality reduction typically induces loss of information, particularly in the high frequency region.
Considering that 3DGS data X are surjective, search for, through training, decoder fo"(Z") that maps an unknown but learnable oow-dimessional feature matrix 2" to reconstruct X. Mathematically, we are going to solve
may drop the PCA-based compression projection Uf and directly
min 8 .2"
I.fer XI.
(18)
Ideally, if X truly has rank q (i.e., the SVD of X has only non-zero singular values), we should be able to losslessly recover X from 7" using even a linear decoder, as can be done with PCA. When the decoder fo"( is further equipped with nonlinear activation functions, improved performance can be obtained, as shown empirically in Table 6, as Z" may contain significantly more information than the linear operation-baan -bsee PCA.
A.3. 3DGS Translation and Scaling
The naive 3DGS sample g' might be located in any position in the 3D space due to the random position of initial point cloud P. Limiting the primitive positions between N- + and 2 requires first shifting and then rescaling the Bbox of g", as shown in Figure 7.
Z
transiate
Original 3DGS
Translated 3DGS
scale
translate and scale
A depth
- 22)--) -1
2* +--0
y Scaled 3DGS
Translated 6 Scaled 3DGS
Figure 7. 3DGS translation and scaling.
3DGS translation: for 3DGS g' with primitives and the ith primitive position being pc; [2,, 2/). The Bbox center of G' is C = [xx:) + (xx ) (wx)ee + (y)) (z))mo + (24) = [6XX, 6Y, 82). Shifting g' to the origin can be realized by pc; = pc, - C, i m).
3DGS scaling: for 3D Gaussian primitive (x(x) - = (x=-))² Σ (x-w), where μ is the point mean and Σ is the 3D covariance matrix, scaling g' (x) with & requires scaling the point position and covariance matrix at the same time: kr, Σ = 1.2 E- y = kr easy to understand, we provide a proof of E 2 Σ here.
14

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HybridGS: Hlig-Effiiiency 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
Proof. Assuming 9" (v) is the scaled g' (ar) with y = kx,
g" (v) = g" (kar) = G Rx'0 = - ( -M)
= e के (2- (pー kp) (v- (v-ka)
(19)
=e Hwー -0- (F
where ji = k 1 1----11=- = ..: ..:E Σ = 12 E.
For the scaling ratio kt we set kt = 28-1-1, pCmaz = max([="] Imaz: by Imaz mmzz) (xr Imaz represents the maximum absolute value of the x coordinates, and the same for and z coordinates. 3DGS translation and scaling will not change the rendering quality because it does not change the relative position and overlapping of the primitives. The camera positions also need to be adjusted accordingly after the 3DGS translation and scaling.
penas
outlier
unused position
unused position
translate
densification
Original SDGS
Translated 5 Scaled 3DGS
Densified SDGS
I remove outlier
new primitive
smaller ibox
tranolate and scale =
densification r
Filtered 3DGS
Translated 5 3DGS
Densified SDCS
Figure 8. Influence of outlier removal.
To take full advantage of finite space, an outlier removal is applied before initializing the point cloud to 3DGS G, i.e., g == I(O(P)) O()) is the outlier removal algorithm and Z(-} is the initialization function mapping point cloud to a 3DGS. Outlier removal will influence the results of GS translation and scaling. Toy examples are used for illustration in Figure 8.
Figure 8 shows the influence of outlier removal on 3DGS translation and scaling. The sparse point cloud generated by COLMAP might have a lot of noisy scatter points that are far away from the target reconstruction content. The noisy points, which will incur an oversized Bbox for the initialized 3DGS, cause spatial waste and primitive densified in relatively small Bbox after GS scaling. It consequently reduces the resolution of 3DGS in the following quantization learning process, and results in suboptimal visual quality.
Both outlier removal (s1) and 3DGS translation (s2) are designed to improve space utilization in LQM. To highlight the effectiveness of these two modules, we evaluate the performance of LQM on "truck " by disabling either or both of these two modules. The results are shown in Figure 9. To highlight the influence of sl and s2, we only consider uniqueness in this section and other compression operation, such as dimensionality reduction, feature quantization, and primitive pruning disabled. We train 30000 epochs vanilla 3DGS. We that: 1) these two modules show more obvious performance improvement in low BD, considering that low BID offers less available space; 2) the impact of 3DGS translation is less prominent than that of outlier removal. We think the results are not always stable: the effectiveness of 3DGS translation has a strong correlation with the spatial position of initialized 3DGS, which may be highly random.
A.4. Primitive Position Decomposition
Given a primitive position pc,- = [2,, ye, 2:] after 3DGS translation and scaling, whose coordinates are floating numbers and -2 + <= F, yi, 24 <= 2 N-1 1, a rounding function is first applied to calculate the nearest integer, i.e., R(cc)). Then, the integer is represented by a NV encoding, which is actually the inner product between a basis vector N2 2N-3... ,..., 4, 2, 1 E ((-11)x1 and coding vector [ti, (x-1]T R (N-1)x1 with 1, E (11,0,1))
15

---

HybridGS: Efffc-effccencyy 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
truck
PSNR
LOM (wlo st, wio s2)
LGM (wia st]
LOM (wia s2)
5
LGM (Full Model)
BD
Figure 9. Ablation study on outlier removal and 3DGS translation.
i.e. =(pc;))(<< e t > ely > e >}. Considering that the primitive positions share the same BD and the same basis vector, using z coordinate as an example, the primitive position matrix can be expressed in the following matrix form
R(pc)= Le,
(20)
where L [t,, E....... "T T and t. = [t1, .2., IN-
In the vanilla 3DGS generation, pe is the learnable parameters that updated by gradient descent derived from the rendering loss. After primitive position decomposition, L replaces pc and is updated during training. To ensure L is coding vector of finite integers, we limit the value range and rounding operation is used during training with the STE to realize gradient propagation.
A.5. Influence of Pruning
To generate 3DGS samples with different rates, we introduce primitive pruning at iteration T₁- 36, 000. The influence of pruning is shown in Figure 10. We can see that: 1) the decrease in the number of primitives results in general deterioration of reconstruction quality; 2) for the low rate of "bicycle", the testing PSNR reports a stable value, while training PSNR exhibits increased variability. indicates that there is an inconsistency in the trends of reconstruction quality between training views and testing views. Considering that the final quality of experience is influenced by both training and testing views, we recommend future 3DGS compression studies pay more attention training views rather than only reporting quality results on testing views.
bicycle
dance
25
*
Primitive
24
Train
41
PSNR
Primitvve numbor
PSNR
106,
106,302
40
Train
22
39
2'3
212.02.84
38
27 017
350
400
450
500
550
600
660
700
360
400
460
500
550
600
650
700
[terationxx102)
(rerationxx10))
Figure 10, Iteration vs. PSNR curves of HybridGS with pruning.
16

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HybridGS: Effec-Efficencyy 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
Table 7. Ahlation study on primitive position uniqueness.
Dataset
drjohnson Size (MB)
BD
Mothed
PSNR
PN(K)
PSNR
Size (MB)
PN(K)
LQM
28.51
28.98
47.03
198
10
w/o
UQ
28.72
552. 14
151.39
2334
30.21
30:16
284.35
78.70
1202
332
w/o
28.61
578.35
24-15
30.23
270.26
1143
LQM
31.00
136.45
576
12
w/o
28.85
$48.04
2317
30.63
281.54
1190
331.53
31.06
143.76
608
w/o
29.04
579.36
2449
31,16
277.46
1173
LQM
28
361
51.20
200.18
846
14
w/o
DQ
28.84
29.04
550.52
438.00
2322
1851
31.38
31.34
281.96
2000.66
1192
847
w/o 29.05
566.75
2396
31,22
283.40
1198
LQM
28.95
368.00
1555
31.25
216.36
914
w/o 28.90
557.94
2359
31.36
285.49
1207
16
UQ
29.17
461.07
1949
31.29
219.13
927
29.14
564.32
31.37
285.27
1206
LQM
28.86
369.58
1562
31.04
219.98
930
28.86
31.11
290.02
1226
18
UQ
29.25
462.36
1954
31,36
224.19
549
31.28
281.87
1191
A.6. Influence of Uniqueness
Our proposed primitíve position uniqueness method is for compatible with the SOTA learning-based methods based on SparseConv. Discard this module and the results are shown in Table 7. "'droohsoo" and "room" are used as test examples. To highlight the influence uniqueness, the other compression operation, such as feature dimension reduction, feature quantization, primitive pruning, and downstream point cloud encoder are disabled. We train 30000 epochs as vanilla 3DGS. We see that for most test conditions, disabling this module will incur significant primitive number and data size growth, while close PSNR with enable this module. For example, by disabling this module, LQM and UQ generate 1202K and 1143K primitives for "room" given 10 BD, which is almost 3 to 4 x data sizes compared to enable this module. Consequently, it reveals that for 3DGS, more primitives do not necessarily lead to better reconstruction quality. The primitive densification method proposed in vanilla 3DGS generation can be further improved if the data size is one of reference.
A.7. Different Point Cloud Encoders on Position
To analyze the results of using 3DGS primitive position as the input of SOTA point cloud encoder, we select GPCC v23 (WG7, 2023) and SparsePCGC (Wang et al., 2022) as representations. Nine BDs: BD = 10 to 18, are tested to collect bitrate with lossless module. We focus on primitive position in this section, therefore, pruning and the compression operation related to other features are disabled. We train 30000 epochs as vanilla 3DGS. The curves of PSNR vs. bitrate of "bicycle" and "truck" are shown in Figure 11. "3DGS+UQ" means training a vanilla 3DGS then using UQ quantize position as postprocessing, "LQM" means using the method proposed in Section 3.1.2 to generate primitive position, and "UQ" means using the feature quantization strategy proposed in Section 3.1.1 via UQ for primitive position.
We see that: 1) for the same bitrate, LQM and UQ report higher PSNR than 3DGS+UQ, indicating that introducing quantization into the generation of 3DGS is an effective strategy for reducing information loss; 2) for 3DGS+UQ, GPCC shows higher reconstruction quality than SparsePCGC. The reason that SparsePCGC based on SparseConv, which cannot deal with the case where multiple points are located at the same coordinate. default duplicated point removal operator is performed before data compression, leading to loss of 3DGS quality. GPCC has two different settings considering duplicated points, i.c., keeping or removing, therefore, it can realize "real lossless" for 3DGS; 3) LQM and UQ can generate 3DGS without duplicated primitive position, facilitating SparsePCGC realizes "real lossless" 3DGS compression; 4) for the same bitrate, LQM reports slightly better performance than UQ. Besides, LQM can generate positions that do not need de-quantization before rendering, which means LQM can further save a position de-quantization time for real-time applications.
A.8. Overall and Per Frame Results
Here we report overall and per frame results of HybridGS on Deep blending, tanks&temples, and Mip-NeRF330. We can see that: 1) for overall quality, HybridGS has 0.5- dB PSNR loss compared with the SOTA generative compression methods; 2) and better quality can be reached by increasing the dimension of latent features. Some demo videos are available at https:
17

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HybridGS: Efficeeff 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
bicycle
bicycle
24
22
20
PSNR
20
PSNR
18
3DGS-UQ
3DGS+UD
16
LGM
14
LOM
UQ
2
GPCC
Bitrate
(Mb)
SparsePCGC Bitrate
(Mb)
truck
truck
22
PSNR
22
PSNR
20
20
18
3DGS-UQ
18
3DGS+U0
LOM
UQ
14
0
2
8
10
GPCC
Bitrate
(Mb)
SparsePCGC
Bitrate
(Mb)
Figure 11. PSNR bitrate curves of GPCC and PCGC.
//drive. google.
com gorive. hrtpa///....aaaooooo//ooooaaaaan
Table 8. Overall results
Dataset
Mank&Temple
Deep Blending
MipNeRF360
Method
PSNR
SIZE
FPS
PSNR
SIZE
FPS
PSNR
SIZE
FPS
3DGS-30K
23.14
411.00
154
29.41
676.00
137
27.21
734.00
134
HybridGS
HR
22.90
8.85
207
28.51
11.52
201
25.64
15.82
199
kc=3, kr=2
I.R
22.66
4.27
247
28.32
5.59
223
25.40
7.63
220
HybridGS
HR
23.12
11.10
195
29.05
16.35
191
25.97
21.73
189
kc=6, kr=2
LR
22.83
5.27
214
28.82
7.92
212
25.75
10.47
210
18

---

HybridGS: Eligc-Efficiency 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
Tample
Daep Rianding
MiNNaRFFe8 360
044-
30.2
HMC
24.9
NAC
Bushearz
Buthearca
37.5
Depins
sn lourd
27.
uE0er Dound
560
20.
Stze|MBb
25.5
SizelMoi
6
Figure 12. Overall RD curve.
Table 9. Tanks&Temples per scene results.
Scene
Method
Training PSNR
Testing PSNR
Size (MB)
GS
27.50
25.39
611.58
HybridGS
HR
26.96
24.53
13.50
truck
Ke 3, lrp 2
LR
26.66
24.35
6,50
HybridGS
HR
27.19
24.75
16.56
Ke 6, hr = 2
LR
26.90
24.62
7.92
GS
25.37
21.89
256.73
HybridGS
HR
24.42
21.26
4.19
train
Ke Kúp
23.58
20.96
2.03
HybridGS
HR
24.86
21.49
5.63
he 6, hir = 2
ER
24.01
2T.04
2.62
Figure 13. Snapshots of HybridGS (k. 3, kr = 2) on Tanks& Temples.
Ground Truth
3DGS
HybridGS
HybridGs LR
Table 10. Deep Blending per results.
Scene
Method
Training PSNR
Testing PSNR
Size (MB)
GS
37.60 7766
30.03
550.67
HybridGS
HR
33.52
29.89
12.15
playroom
3, k,
LR
32.40
29.49
5.88
HybriGSS
HR
33.81
29.89
16.08
h: 6, ly
LR
3
29.68
7.79
GS
36.21
29.06
779.93
HybridGS
HR
32.01
27.12
10.88
drjohnson
3, ky
LR
31.22
27.15
5.29
HybriGSS
HR
33.50
28.21
16.62
h: 6, hr = =2
LR
31.86
27.95
8.04
19

---

HybridGS: 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
Ground Truth
3DGS
Hybridos HR
Hybridos LR
Figure 14. Snapshots of HybridGS (k, 3, k, 2) Deep Blending.
Ground Truth
3DGS
HybridGS
HybridGs
Figure 15, Snapshots HybridGS (k.- 3, k, on MipNeRF360.
20

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HybridGS: High- Efficienf 3DGS Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
Table IL. MipNeRF360 per frame results.
Scene
Method
Training PSNR
Testing PSNR
(MB)
GS
24.82
24.45
1443.84
HybridGS
HR
21.03
24.08
22.88
bicycle
LR
20.51
23.53
11.01
HybridGS
HR
21.36
24.10
30.21
h0c 6, k. =2
LR
21.34
23.76
14,52
GS
33.44
32.28
296.75
HybridGS
HR
29.35
29.61
7.48
bonsai
16 3, Rr- 2
LR
28.
29.25
3.62
HybridGS
HR
30.65
30.86
11.62
h. 6, k,
LR
29,66
30.35
3.62
GS
30.46
29.07
284.30
HybridGS
26.88
7.07
counter
h2 3, k. 2
LR
28.11
26.
3.43
HybridGS
HR
29.50
27.16
7.67
h. 6, k. 2
LR
28.26
27.02
3.72
23.22
21.27
856.45
HybridGS
HR
21.32
20.25
17.21
flowers
h. 3, R.. 2
I.R
20.90
20.13
8.27
HybridGS
HR
21.89
20.50
22.51
h. GS
21.41
20.33
10.78
28.29
26.33
1392.64
HybridGS
HR
26.80
26.37
36.13
garden
k; 3, k..
LR
25.31
26.09
17.40
HybridGS
26.85
26.44
42.49
h. 6, k. 2
LR
25.23
26.
20.42
GS
33.13
31.38
26.33
HybridGS
HR
27.20
27.07
9.76
kitchen
hic k.
26.75
27.04
4.70
HybridGS
HR
27.78
27.56
12.29
h. 6, k.
2
LR
27.16
27.55
5.95
GS
34.40
31.55
370.14
HybridGS
31.95
29.52
6.43
room
h. 3, k. 2
LR
31.04
29.23
3.14
HybridGS
HR
32.38
29.75
8.28
h. 6, R 2
I.R
31.40
29.61
4.00
GS
29.76
26.24
1157.12
HybridGS
HR
25.59
24.92
18.24
stump
h: 3, &r 2
LR
24.76
24.67
8.83
HybridGS
HR
26.19
25.09
35.00
k. GS
LR
25.82
25.02
16.78
23.44
22.23
890.63
HybridGS
HR
20.80
22.05
17.22
trechill
k. 3, ks
LR
20.63
21.91
8.29
HybridGS
HR
21.67
22.24
25.51
h 6,
20.78
22.04
12.41
21


Reviews:
[{"review_id": "oh6xbzL1fH", "reviewer": "Reviewer_rykQ", "summary": "The authors propose HybridGS to compress 3D Gaussian splatting. The method first generates compact 3D Gaussians using dimension reduction, quantization of features, and positions. Then, it uses existing point cloud encoders to further compress the generated 3D Gaussians. The method achieves compression performance similar to SOTA methods with faster coding and decoding speeds.\n\n## update after rebuttal\nAfter reviewing the rebuttal, I feel that some of my concerns have been addressed, so I have decided to move the recommendation to weak accept.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1).\tAveraged metrics for the entire dataset\n\nWe shall include a table and figures to show the averaged metrics for the entire dataset. The table is given below. The proposed HybridGS exhibits 0.5dB to 1.5dB loss in PSNR compared with HAC and CompGS under the same bitrate.\n\nThe figures can be accessed via \nhttps://drive.google.com/drive/folders/1V1mxZq1IPXz2H0kF6_a7IsP8_iGOLCUu?usp=sharing . \n\n|                        |       | Tank&Temple | | | DeepBlending | | | MipNerf360 | | |\n| ---------------------- | ----- |:----------:|---|---|:------------:|---|---|:----------:|---|---|\n|                        |       | PSNR | SIZE  | FPS  |  PSNR | SIZE  | FPS  |  PSNR | SIZE  | FPS  |\n| 3DGS-30K               |       | 23.14 | 411.00 | 154 | 29.41 | 676.00 | 137 | 27.21 | 734.00 | 134 |\n| HybridGS kc=3, kr=2 | HR    | 22.90 | 8.85   | 207 | 28.51 | 11.52  | 201 | 25.64 | 15.82  | 199 |\n|                        | LR    | 22.66 | 4.27  | 247 | 28.32  | 5.59  | 223 | 25.40  | 7.63  | 219 |\n| HybridGS kc=6, kr=2 | HR    | 23.12 | 11.10  | 195 | 29.05 | 16.35  | 191 | 25.97 | 21.73  | 189 |\n|                        | LR    | 22.83 | 5.27  | 214 | 28.82  | 7.92  | 211 | 25.75  | 10.47 | 210 |\n\n2). Novelty aspect\n\nWe would like to apologize for not well motivating this work. We shall better address the motivation and importance of our work in the revised paper. \n\nWe agree with the reviewer that both GGSC and HybridGS use existing point cloud encoders to realize data compression. But HybridGS adopted completely different strategies, making it superior to GGSC empirically. \n\nGGSC uses vanilla GS to generate GS samples, then carries out quantization to convert GS attributes into integers and finally employs GPCC to compress geometry. The disadvantages of this approach were discussed in the Introduction and Experiment sections of the original paper. They include \n\n(a) Using quantization as postprocessing introduces obvious distortion (see Fig. 11 in the Appendix). HybridGS integrates quantization into the GS generation rather than using it postprocessing, leading to a maximum of 4.5dB PSNR gain. \n\n(b) The quantization method of GGSC exhibits duplicated GS positions, which requires that the downstream point cloud encoder is able to handle duplicate points. This renders the use of SOTA point cloud encoders based on SparseConv infeasible as they take unique GS positions only (see Section 2.1). HybridGS achieves uniqueness during the GS generation to make it compatible with all existing point cloud encoders. \n\n(c) Vanilla GS produces a large volume of data. Applying point cloud encoders directly incurs very long encoding and decoding latency. As shown in Table 2, GGSC spends over 2 mins for “bicycle”. HybridGS tackles this problem using dimension reduction for achieving low-rank feature generation and an LQM module to induce sparse and dequantization-free geometry, decreasing the encoding and decoding time from tens of seconds or over a minute to 0.4s to 1.6s.\n\nThe main contribution of this work, in our opinion, is to establish a framework capable of integrating the developed compression method as well as the rate control scheme with existing point cloud encoders to attain fast encoding/decoding while maintaining comparable compression ratio and quality. More justifications of the importance of reducing the encoding/decoding latency can be found in our response to the first question of Reviewer nV7F. They will be included in the revised paper.\n\n3). Compression ratio\n\nAs discussed above, the goal of this work is a better balance between the compression ratio and coding speed. In the “Conclusion Limitations” Section of the paper, we did admit that the compression ratio of HybridGS is lower than some end-to-end methods. We also did not include any optimization techniques for improving the quality either. The gain is significant improvement in the coding speed, which is important and meaningful for dynamic content delivery and streaming scenarios. \n\n4). Computational complexity\n\nThanks for the comment. The fast coding speed of HybridGS is due to using the point cloud codec as well as the sparse characteristic of the explicit GS data. On the other hand, the dimension reduction, quantization, and pruning during the GS generation requires marginal overhead. In a whole, the GS generation time of HybridGS is close to the vanilla GS under the same training epochs. As an example, for “dance” with 7k training epochs, HybridGS requires 6 mins while vanilla GS uses 5.6 mins. Another evidence on the low computation time for dimension reduction and quantization has been reported at the end of Section 4.2.2. After decoding, for the largest sample ``bicycle’’, the dimension reduction and quantization for color only takes 1s and 0.9s, while 0.6s and 0.001s for rotation. \n\n5). Editing issues\n\nThank you for your careful reading. We shall fix them all in the revision.", "claims_and_evidence": "The claims are supported.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "There is no proof to verify.", "experimental_designs_or_analyses": "In the main paper, the authors demonstrate their methods on five selected scenes. However, there is a lack of averaged metrics for the entire datasets. Although PSNR and SIZE of additional results are reported in the appendix, it would be more conclusive to include the average trend in Table 1 and Figure 5.", "relation_to_broader_scientific_literature": "The method is a combination of learning-based quantization and dimension reduction methods, as well as signal-processing-based compression methods.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Weakness:\n1. The HybridGS method does not present enough novelty. The idea of using the existing point cloud encoders, such as G-PCC, has already been explored in GGSC [1]. The other components for dimension reduction and quantization do not provide enough novelty to be accepted at this venue.\n2. The compression rate of the method is still lower than other end-to-end method [2]. \n3. Although the encoding and decoding time significantly decrease thanks to existing point cloud encoders, the computation time for dimension reduction, quantization, and pruning of the Gaussians is not discussed in detail.\n\n\n[1] Yang, Q., Yang, K., Xing, Y., Xu, Y., and Li, Z. A benchmark for gaussian splatting compression and quality assessment study. In Proc. ACM Int. Conf. Multimedia in Asia. Association for Computing Machinery, 2024. doi: 10.1145/3696409.3700172.\n\n[2] Liu, X., Wu, X., Zhang, P., Wang, S., Li, Z., and Kwong, S. Compgs: Efficient 3d scene representation via compressed gaussian splatting. arXiv preprint arXiv:2404.09458, 2024.", "other_comments_or_suggestions": "1. At the top of every page, the title line has become \"Title Suppressed Due to Excessive Size.\"  \n2. Line 134, right column, \"where ̄, Cov, and Var represent ...\", the average operator symbol is above the comma, which looks confusing at first glance.", "questions_for_authors": "No.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Hym8FRxA6I", "reviewer": "Reviewer_BMP7", "summary": "HybridGS aims at the data compression of 3DGS. It takes advantage of both generative compression technique and traditional compression technique by first generating a compact explicit 3DGS representation and then encoding it with a standard point cloud codec​. It achieves a higher encoding and decoding speed compared to other generative compression methods. Its key innovations include: (1) Dual-Channel Sparse Representation; (2) Rate Control Scheme: progressively prune more primitives to reduce point count, and/or lower the quantization precision of features (bit-depth) uniformly across attributes​.\n\n## update after rebuttal\nAfter reviewing the rebuttal, Most of my concerns have been addressed, so I maintain my score.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1). FPS (rendering speed), averaged and complete results\n\nThanks very much for your suggestions. Please refer to our response to Question 1 of Reviewer rykQ for complete results and newly generated FPS results over different datasets.\n\n2). Essential references \n\nWe shall update the reference information of the lightGaussian work in the revised paper. We would like to apologize for not using the latest information.\n\n3). Compression ratio\n\nIn the “Conclusion Limitations” Section of the paper, we admitted that the compression ratio of the proposed HybridGS method is in fact lower than some end-to-end methods. However, the goal of this work is to achieve a better balance between compression ratio and coding speed. More on the motivation for putting efforts to reduce the coding time can be found in our response to Question 1 of reviewer nV7F. For some use cases, coding speed is of great importance, such as dynamic content delivery and streaming, where existing methods can hardly meet the high-speed 3DGS encoding and decoding requirements. The results in Table 2 of the paper showed that with HybridGS, we can reduce the encoding and decoding time from tens of seconds or over 1 minute to 0.4s to 1.6s. This lays down a nice starting point for future researches on real-time GS coding and decoding.\n\n4). Implementation complexity\n\nThanks for the comments. HybridGS has two steps. The first step is to generate a compact GS file, which is then compressed by an existing point cloud encoder in the second step. To facilitate the understanding and applications of the proposed technique, we shall provide an easy-to-use script for realization. One advantage of our method is that it is compatible with many downstream point cloud encoders. In other words, you can integrate the latest encoder in a straightforward manner.\n\n5). Memory consumption of HybridGS\n\nWe agree with the reviewer that one disadvantage of keeping data explicit is that it requires a larger memory. Explicit data is more friendly for realizing fast coding speed, while implicit data is more friendly for reducing data size. In our opinion, compared with vanilla 3DGS, HybridGS itself incorporated a few techniques that can help to decrease the memory cost. For example, we introduced primitive uniqueness and pruning in HybridGS to reduce the number of generated GS primitives. In the future work, we shall consider how to further reduce the memory cost while keeping the characteristic of fast encoding and decoding speed.\n\n6). Video rendering\n\nFor illustration purpose, we have provided some snapshots in Appendix A.9. We shall show some video results. Due to time limitations, please check the link for some demo videos, more video results will be provided later. Due to the video coding format, VLC media player is recommended to play the video, other players might show some blur.\nhttps://drive.google.com/drive/folders/1YaFvkHLDQ10CAV0NLGmT9KhQitBnrdIU?usp=sharing\n\n7). Paper writing\n\nWe would like to apologize for not motivating this work well. In the revised paper, we shall rewrite the last several paragraphs of the Introduction section to reduce the technological details, while highlighting more the following motivations:\n\n(a) Encoding and decoding over explicit data is much faster than over implicit data. Therefore, in this paper, we choose to generate compact and explicit GS data file first, then followed by applying an existing point cloud encoder to generate the bitstream. \n\n(b) The volume of explicit data will influence coding time significantly. Therefore, we use LQM to produce sparse primitive distribution, and use feature dimension reduction to generate low-rank feature matrices. The size of explicit data to be compressed is thus reduced significantly. \n\n(c) We introduce quantization and uniqueness during the GS generation, which means practical encoding does not need any preprocessing like quantization. Besides, this also facilitates the reduction of encoding and decoding time. \n\nWe re-plot Fig. 2 for better illustrating our method, which can be found in\nhttps://drive.google.com/drive/folders/1CwMbhm4l44oXD5MnCP3slbJHgw_vZ48c?usp=sharing", "claims_and_evidence": "The major claims in the submission is the higher encoding and decoding speed for Gaussian data compression with comparable rendering performance. This claim is supported by clear comparison in the experiments.", "methods_and_evaluation_criteria": "The evaluation criteria is adequate. However, the FPS(rendering speed) is better to be involved.", "theoretical_claims": "The paper is largely an empirical study, so no formal proofs to verify. However, it does introduce some theoretical reasoning. They present two optimization formulations (8) and (9) for rate control under bandwidth constraint​, which are consistent with known practices.", "experimental_designs_or_analyses": "The experiments are sound.\n\nI appreciate that the authors list the specific results of each scenario in the supplementary materials, but it would be better to have a complete comparison in Mipnerf360 in the main paper, which will make it easier for other researchers in the community to compare different works.", "relation_to_broader_scientific_literature": "This paper combines the technique in neural rendering compression and point cloud compression, which brings the compression technique to 3D Gaussian Splatting(3DGS). It could benefit the 3DGS community, but the broader impact is limited.", "essential_references_not_discussed": "The references are adequate. However, as one of the highly related work in this paper, the reference info of lightGaussian is out-of-date. It would be better to update it. (It has been accpected by NIPS2024, but the paper still cites its arxiv version.)", "other_strengths_and_weaknesses": "Strengths:\n1. HybridGS offers a practical solution for Gaussian Compression.\n2. The processing(encoding and decoding) speed is much higher.\n3. The evaluation is adequate.\n\nWeaknesses:\n1. While HybridGS is close to SOTA, it doesn’t surpass the best neural methods in pure compression efficiency. HAC or CompGS can often reach smaller bitrates at the cost of encoding time. \n2. The method with combined compression tecnique is somewhat complex to implement compared to a purely learned approach. \n3. HybridGS outputs an explicit point cloud plus small MLPs. The memory required to hold the decoded point cloud in memory could be larger than for a fully implicit decoder.", "other_comments_or_suggestions": "As a rendering-related work, I strongly suggest that the author should include video results after compressing.\n\nMy current score is more like a boardline. For me, the biggest problem with this paper is the writing. Although I am familiar with 3DGS and some compression work, I still need to spend some time to get the core contribution of this paper. For example, in the Introduction section, the author spent a lot of time introducing the specific implementation process of the method, which makes it difficult for readers to understand how the focus of this article is to achieve the acceleration of encoding and decoding time.", "questions_for_authors": "See above", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gqKTbijxXX", "reviewer": "Reviewer_nV7f", "summary": "This paper aims to compress 3d Gaussians into very small sizes for storage efficiency. The core idea of the proposed HybridGS is to combine traditional point cloud compression method and the generative coding compression method. The most advantage of HybridGS compared to previous 3DGS compression method is it requires less encoding and decoding time.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1). Importance of compression speed\n\nThanks very much for your comments on the importance of compression speed. Please allow us to clarify. \n\nThe processing latency of visual media, especially the encoding and decoding time  [R1], has become an essential utility factor, considering that 5G network can provide larger bandwidth and network transmission is now much faster than that in past decades [R2]. As an emerging type of visual media, GS has many streaming use cases [R3], where the encoding and decoding speed sometimes is even more important than the compression ratio itself. According to International Telecommunication Union (ITU) [R4], a latency higher than 400ms will result in user dissatisfaction. For highly interactive scenarios, this threshold drops to 250ms. The findings from studying the response time [R5], and empirical video-on-demand and video live streaming cases indicate that 1s is probably the limit for the user’s flow of thought to stay uninterrupted, and 10s is the limit for keeping the user’s attention focused on the dialogue. As a result, it can be expected that the quality of experience (QoE) will be greatly degraded if it takes close to 1 minute to decode the GS from the received bitstream. \n\nCurrent 3DGS compression work, on the other hand, mostly concentrate on achieving high compression ratio, without paying sufficient attention to the coding speed. Therefore, there exists a clear gap between the academic and industrial needs for lightweight encoding and decoding schemes, and the current literature and practice. We hope our work will bridge this gap to some extent. This is also the reason behind our decision of not including any optimization techniques for improving the delivery quality. \n\nWe shall include these discussions in the revised paper to address your comments.\n\n[R1] Kim et al., \"C3: High-performance and low-complexity neural compression from a single image or video,\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.\n\n[R2] Wang et al., \"Inferring end-to-end latency in live videos,\" IEEE Transactions on Broadcasting 68.2 (2021): 517-529.\n\n[R3] Sun et al., \"3DGStream: On-the-fly training of 3D Gaussians for efficient streaming of photo-realistic free-viewpoint videos,\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.\n\n[R4] Recommendation ITU-T G.1051, \"Series G: Transmission systems and media, digital systems and networks - Multimedia quality of service and performance - Generic and use-related aspects,\" 2023\n\n[R5] J. Nielsen. Usability Engineering. Morgan Kaufmann, 1994.\n\n2). Rendering quality\n\nThanks for the insightful comments. The rendering quality of the proposed HybridGS has an upper bound, which is the quality of the vanilla 3DGS. This is shown in Table 1 and Figure 5. As an example, the vanilla GS `bicycle’ has a PSNR of 24.49dB, while HybridGS provides a PSNR of 24.10dB. In our experiments, we set the latent dimension of color to be 3 or 6. Increasing the dimensionality of color and rotation latent features and/or applying a larger bit depth would lead to improved PSNR results, better approaching the performance of vanilla 3DGS. For example, if we increase the dimension of color latent features to 9 while keeping other settings unchanged, HybridGS can now provide a PSNR of 24.25dB for ‘bicycle’. On the other hand, HAC reports a PSNR of 25.00dB for ‘bicycle’, a quality level that even requires introducing techniques to improve the quality of the vanilla 3DGS. As pointed out in the answer to your previous comment on the importance of compression speed, we did not include any optimization techniques in this work. But the design of HybridGS does allow additional quality enhancement method to be incorporated, which is an important direction for future research.\n\n3). Table 3\n\nWe would like to apologize for this clarity issue. Table 3 in the paper gives the size of different GS features before and after compression via GPCC. It illustrates the bitrate distribution of the proposed HybridGS. In fact, HybridGS first generates a compact and explicit GS file (.ply file) that consists of primitive position (xyz), color latent feature, opacity, scaling, and rotation latent features. These data can be considered as point cloud data and thus can be further compressed by GPCC, with each of them having an individual bitstream. Take ‘bicycle’ as an example. In Table 3, ‘position 2.72 MB (7.36)’ means that before applying GPCC, the size of the GS primitive position information is 7.36 MB, while after using GPCC, the bitstream size reduces to 2.72 MB. This indicates around 2.7x lossless compression. After the GPCC compression, the bitstreams of the aforementioned data, as well as metadata and the parameters of two small MLPs, will be stored or used for streaming. They form the final output bitstream. \n\nIn the revised paper, we shall add more space between Tables 2 and 3 for better clarity.", "claims_and_evidence": "Yes. This paper claims that the proposed method can compress 3DGS at a very fast speed, which is verified by subsequent experimental results in table 2.", "methods_and_evaluation_criteria": "Yes. Five popular scenes from four datasets (deep blending, tanks&temples, Mip-NeRF360, PKU-DyMVHumans) are included in the comparisons. And some more scenes from deep blending and Mip-NeRF360 are provided in the appendix.", "theoretical_claims": "Yes. the Dual-Channel Sparse Representation and High-Efficiency Coding. The two components make scenes.", "experimental_designs_or_analyses": "Yes. In table 2, the proposed method achieves significantly better compress efficiency compared to other methods.", "relation_to_broader_scientific_literature": "The feature channel compression in Dual-Channel Sparse Representation is widely used a lot of previous studies such as Scaffold-GS, LightGaussian.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Strengths:\n1. The proposed method can compress 3DGS at a fast speed, which is different from previous methods.\n2. This paper provides extensive experiments on various datasets.\n\nWeaknesses:\n1. The rendering quality is worse than other 3DGS compression methods such as HAC and CompGS as shown in table 1.", "other_comments_or_suggestions": "1. It is hard for me to understand the components column in table 3. And there seems to be something wrong with the spacing of the table 2 and 3.", "questions_for_authors": "1. I am not very sure about if reducing the encoding and decoding time of 3DGS compression really necessary. I would consider raising the score if it is clearer why compression speed is important.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "eiYXAqmSZy", "reviewer": "Reviewer_15YA", "summary": "In this work, the authors propose a compression framework for 3DGS. A lightweight decoder $D$ composed of a one hidden layer MLP is introduced to compress original high-dimensional GS features into Low-dimensional latent features $f$  for quantization and compression, where the rate is controlled by adjusting the number of GS primitives or quantization depths.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1). Paper writing\n\nWe would like to apologize for not motivating this work well and presenting our method clearly. In the revised paper, we shall replace Fig2 with a better illustrated one given in\nhttps://drive.google.com/drive/folders/1CwMbhm4l44oXD5MnCP3slbJHgw_vZ48c?usp=sharing. \n\nBesides, we will include the following arguments with proper literature citations to better motivating this work:\n\na). Current generative GS compression methods, such as HAC and CompGS, provide impressive compression ratios but at the cost of very slow encoding and decoding speed, due to their use of implicit 3DGS bitstreams.\n\nb). For the scenarios of visual media streaming, it has been shown that users start to feel dissatisfaction when the latency is larger than 400ms. Furthermore, 1s is about the limit for the user’s flow of thought to stay uninterrupted, and 10s is the limit for keeping the user’s attention focused on the dialogue. Therefore, the quality of experience (QoE) will drop sharply if it takes about 1 min to decode the GS from the bitstream. \n\nc). For dynamic 3DGS content, the decoding speed will also affect the FPS and content fluency.\n\nThird, we shall completely rewrite the last three paragraphs of the Introduction section. They will now cover the following aspects:\n\na). To realize fast 3DGS coding and decoding, we propose to generate compact 3DGS in the explicit domain instead of the implicit domain, which enables the use of reliable and effective point cloud encoders to generate bitstream.\n\nb). For explicit and compact 3DGS generation, a dual-channel sparse representation is adopted to reduce the data volume. This reduces the computation burden of downstream point cloud encoders and has less compression-related distortion. The first channel produces sparse 3DGS primitive distribution, where a learnable quantizer-based method (LQM) is employed to obtain dequantization-free primitive positions. The second channel generates sparse features for each primitive. Feature dimension reduction and a robust quantizer (RQ) are utilized to find low-rank primitive features.\n\nc). With the same bitrate, HybridGS suffers from around 0.5-1.5dB loss in PSNR, compared with SOTA generative compression methods. But using HAC as a baseline, we decrease the encoding and decoding latency from tens of seconds or more than 1 minute to around 0.4s to 1.6s. This clearly demonstrates the practicality of the proposed framework.\n\n2). Compression ratio\n\nThanks very much for the comment. Actually, in the “Conclusion Limitations” Section of the paper, we summarized that the compression ratio of the proposed HybridGS is lower than that of some end-to-end methods. As discussed in our response to your previous comment, this work aimed at achieving a better balance between compression ratio and the complexity. HybridGS is among the few existing work that successfully reduces the coding time to a level potentially meeting the requirement of use cases such as GS-based dynamic content delivery and streaming. For more explanation about why coding speed is important please refer to the first question of reviewer nV7f. It also has rooms for further performance improvements through integrating e.g., 3DGS quality optimization and new point cloud encoders.\n\n3). Division of generated samples into subsamples\n\nThanks for the question. Please allow us to clarify. The purpose of dividing generated samples into subsamples is to make them compatible with point cloud encoders. As mentioned in Section 3.2.1 of the paper, current point cloud encoders support data format with “xyzrgb” or “xyzf” only, because point clouds generally do not have as many feature channels as 3DGS. Considering that the point cloud shares the same data format with 3DGS, we can have two different methods for using existing point cloud encoders to compress 3DGS data. \n\nThe first approach is to divide 3DGS data into subsamples. For example, vanilla GS has x, y, z, r, g, b, sh1, sh2, …, sh45, opacity, scaling1, scaling 2, scaling3, rotation1, …, rotation4. They can be grouped as sample1: x, y, z, r, g, b; sample2: x, y, z, sh1, sh2, sh3, etc. These subsamples can be directly input to point cloud encoders. This does mean we need to call the GPCC encoders multiple times though.\n\nThe second method is to modify current point cloud encoders to make them be able to handle the one-time 3DGS sample loading. This approach can further exploit OpenMP to process GS attributes in parallel, leading to even faster coding time. We do not follow this way because compared with the first method, it will has same the compression ratio. \n\n4). Qualitative comparisons \n\nQualitative results are given in Appendix A9, where the snapshots of compressed 3DGS samples are shown. We also provide average performance on three datasets in the first question of reviewer rykQ. Additional videos for more illustrations are now available via \nhttps://drive.google.com/drive/folders/1YaFvkHLDQ10CAV0NLGmT9KhQitBnrdIU?usp=sharing.", "claims_and_evidence": "NA.", "methods_and_evaluation_criteria": "The benchmarks may be reasonable.", "theoretical_claims": "This work does not include theoretical claims needed to be proved.", "experimental_designs_or_analyses": "Some experiments may be missing, please check the strengths/weaknesses section.", "relation_to_broader_scientific_literature": "No.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Strengths\n\n(1) The authors proposed a 3DGS compression framework without 3DGS optimization;\n\n(2) The proposed method has higher compression efficiency than baselines.\n\nWeaknesses\n\n(1) For me, the whole framework is hard to understand. I would suggest the authors to re-write the papers and add clearer diagrams to demonstrate this method. The confusing presentation of this work may make it hard to be accepted as a ICML submission.\n\n(2) The performances of the proposed method shows quite inferior performances compared to existing methods like ComGS, and HAC. Although some improvements show up in the coding time as shown in Table. 2, the dropping of performances cannot be ignored;\n\n(3) I don't quite get the dividing of generated samples into subsamples. Does it mean that we compress multiple point clouds attached with different GS attributes repeatedly?\n\n(4) There is not any qualitative comparisons in this paper, which makes it difficult to intuitively judge the performance differences.", "other_comments_or_suggestions": "Please check the typos in this work,e.g., Diffenert in Fig.1.", "questions_for_authors": "Please check the strengths/weaknesses section.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "EDevOHnz4m", "reviewer": "Reviewer_jCTH", "summary": "This paper proposes a new 3D Gaussian Splatting (3DGS) compression framework, HybridGS, which combines the advantages of generative and traditional compression methods. It improves the encoding and decoding speeds while ensuring the reconstruction performance.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1). Insufficient experiments \n\nWe would like to thank you for positive recommendations. We provide some new results on BD-Rate and PSNR to compare the performance of the methods in consideration. They can be found via \nhttps://drive.google.com/drive/folders/1V1mxZq1IPXz2H0kF6_a7IsP8_iGOLCUu?usp=sharing.\nGenerally, our method has around 0.5-1.5dB PSNR loss compared with HAC and CompGS with the same bitrate. However, the motivation of this paper is to realize a balance between compression ratio and coding time. We reduce coding time from tens of seconds or more than 1 minute to around 0.4s to 1.6s, which is the main advantage of this method.\n\n2). Limitations on the current rate control scheme\n\nThanks for your insightful comments. We agree with the reviewer that there are more parameters that can influence the rate control. For example, one factor of interest is the latent feature dimension during training. Different from reducing the bit depth and removing some primitive, decreasing the latent feature dimension during GS generation can lead to feature space collapse. It also requires training a new decoder, which will induce extra training time. How to achieve smooth latent feature dimension reduction without significantly affecting the rendering quality is thus a research topic worthy of investigation. We shall look into this problem in the future. \n\n3). Code availability\n\nWe have uploaded the code to GitHub. Due to the double-blind reviewer policy, we currently set it as a privacy repository. We will be more than happy to share it with the whole community after the paper is accepted.", "claims_and_evidence": "Please see Other Strengths And Weaknesses.", "methods_and_evaluation_criteria": "Please see Other Strengths And Weaknesses.", "theoretical_claims": "Please see Other Strengths And Weaknesses.", "experimental_designs_or_analyses": "Please see Other Strengths And Weaknesses.", "relation_to_broader_scientific_literature": "N/A", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Strength\n\n(1)The idea of this article is very novel. The author cleverly links 3DGS compression with point cloud compression, and realizes data format control and conversion through PAT-Q and DAT-R. This method can effectively take advantage of advanced point cloud codecs and greatly improve the compression efficiency of 3DGS. In addition, the author has simultaneously attempted to use G-PCC (traditional) and SparsePCGC (deep learning-based) as point cloud codecs for compression, which shows better generalization ability.\n\n(2)The codec time of this framework is significantly better than that of previous methods, and it is easier to deploy the model in practice.\n\n(3)The subjective effects presented in the supplementary materials demonstrate the excellent performance of this method.\n\n\nWeakness\n\n(1)The author's analysis of the experimental performance is insufficient. For example, from Table 1, it is difficult to analyze the actual compression effects of each method. It is recommended that the author use the commonly used BD-Rate and BD-PSNR metrics in the compression field to intuitively reflect the performance gaps among different methods.\n\n(2)As the author mentioned in the \"Limitations\" section, the research on rate control in this article is not comprehensive enough. Generally, it is necessary to calculate the dependency relationship between the bit-rate and various quality control hyperparameters. This could be one of the future improvement directions for this work.\n\n(3)The author did not include the reference code for this project in the supplementary materials, which has had a certain impact on the reproducibility of this thesis.", "other_comments_or_suggestions": "I suggest that the author supplement BD-Rate and BD-PSNR for better analysis of experimental results.", "questions_for_authors": "N/A", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The paper proposes a novel 3DGS compression framework HybridGS which integrates the strengths of both generative and traditional compression methods to reduce encoding and decoding times while maintaining comparable reconstruction quality to SOTA methods. The reviewers acknowledged several strengths of the paper, particularly its innovative approach of combining traditional point cloud compression techniques with generative coding methods, leading to faster encoding and decoding speeds. Reviewer nV7f emphasized this point, noting the practical implications of improved compression speeds.

During the rebuttal period, the authors provided solid explanation with additional experimental results and successfully addressed the concerns raised by the reviewers and therefore both Reviewer rykQ and Reviewer nV7f upgraded their ratings to weak accept.

Given that the paper presents a valuable concept and has the potential to advance the field of 3D compression, the AC recommends accepting this paper to ICML 2025. Congratulations! Please be aware that the authors are strongly encouraged to address reviewer’s concerns and enhance clarity (see Reviewer 15YA) in the camera-ready version.
Decision: Accept (poster)