ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS
Authors: Weijie Wang
Donny Y. Chen
Zeyu Zhang
Duochao Shi
Akide Liu
Bohan Zhuang
Venue: NeurIPS 2025 poster ()
Keywords: novel view synthesis
feed-forward 3DGS
gaussian splatting
Primary area: deep_learning
TL;DR: ZPressor is an architecture-agnostic module that compresses multi-view inputs for scalable feed-forward 3DGS.

Abstract: Feed-forward 3D Gaussian Splatting (3DGS) models have recently emerged as a promising solution for novel view synthesis, enabling one-pass inference without the need for per-scene 3DGS optimization. However, their scalability is fundamentally constrained by the limited capacity of their encoders, leading to degraded performance or excessive memory consumption as the number of input views increases. In this work, we analyze feed-forward 3DGS frameworks through the lens of the Information Bottleneck principle and introduce ZPressor, a lightweight architecture-agnostic module that enables efficient compression of multi-view inputs into a compact latent state $Z$ that retains essential scene information while discarding redundancy. Concretely, ZPressor enables existing feed-forward 3DGS models to scale to over 100 input views at 480P resolution on an 80GB GPU, by partitioning the views into anchor and support sets and using cross attention to compress the information from the support views into anchor views, forming the compressed latent state $Z$. We show that integrating ZPressor into several state-of-the-art feed-forward 3DGS models consistently improves performance under moderate input views and enhances robustness under dense view settings on two large-scale benchmarks DL3DV-10K and RealEstate10K.

ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS
Weijie Wang'
Donny Y. Chenè
Zeyu Zhang?
Duochao Shi!
Akide Liu?
Bohan Zhuang'
'ZIP Lab, Zhejiang University
Monash University
Abstract
Feed-forwart 3D Gaussian Splatting (3DGS) models have recently emerged as a promising solution for novel view synthesis, enabling one-pass inference without the need for per-scene 3DGS optimization. However, their scalability is fundamen- tally constrained by the limited capacity of their models, leading to degraded perfor- mance or excessive memory consumption as the number of input views increases. In this work, we analyze feed-forward 3DGS frameworks through the lens of the Information Bottleneck principle and introduce ZPressor, a lightweight architecture- agnostic module that enables efficient compression multi-view inputs into a compact latent state Z that retains essential scene information while discarding redundancy. Concretely, ZPressor enables existing feed-forward 3DGS models scale over 100 input views at 480P resolution the views into anchor and support sets and using cross attention to compress the information from the support views into anchor views, forming the compressed latent state Z. We show that integrating 7Pressor into several state-of-the feed-forward 3DGS models consistently improves performance under moderate input views and enhances robustness under dense view settings on two large-scale benchmarks DL3DV-10K and RealEstate 10K. The video results, code and trained models are available on our project page: https:/ //1hmd//mmmmmmmmm
80GB GPU, by partitioning
Introduction
Novel view synthesis (NVS) has played an important role in many everyday applications and is expected become even more crucial in the future as a foundational technique for augmented reality (AR) and virtual reality (VR). It has also received growing attention in the research community with the introduction of 3D Gaussian Splatting (3DGS) [1] and series of subsequent developments [2-6]. Although 3DGS achieves real time rendering and high visual quality, its reliance on slow per-scene tuning significantly limits its practical use in real world scenarios.
To address this limitation, feed-forward 3DGS [7, 8] has been introduced to improve the usability of 3DGS. Unlike conventional 3DGS approaches that rely slow per-scene backward optimization, feed-forward 3DGS introduces an "encoder" to extract scene dependent features from input images, allowing the model to benefit from large scale training and predict 3DGS in single forward pass. Despite notable progress [9-13], these methods remain constrained to a small number of input views, limiting their ability fully utilize datasets with dense multiple input views [14-17]. For example, our experiments show that the state-of-the-arr model DepthSplat [12] suffers a significant performance drop and increased computational cost input views become denser (see Tab. and Fig. 1), While better engineering might alleviate this memory issue to some extent, it cannot address the huge
"Corresponding authors: wangweijie edu.end.nn donny.chen @ @outlook.sg. "This work was conducted while Y. Chen was affiliated with Monash University.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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DepthSplat (36 views)
DepthSplat ZPressor (36 views)
Ground Truth
Feet-Fomaadd
DepthSplax
SepthSplat ZProsur
Effiiien Extraction
Images
Thih memory
Target
Buntlneck-smoree compasss
PSNR: 4.65dB
Time: 770% 1
Low compute ScuEOo, ligh
Target
Memory: 80%
Fond-Farward 3DGS Z.Preurr
200
300
700
Inference Time (ms)
Figure 1: We visualize the result of SepthSplat [12] with 36 view input for novel view synthesis and after adding ZPressor. We report PSNR, inference time, and memory usage before and after simply adding the ZPressor, where the radius of the bubble corresponds to memory.
performance degradation. Upon examining the architecture of several representative feed-forward 3DGS models [7, 8, 12], we identify the limited capacity of the whole network from image to 3DGS as the root cause. By design, struggles to scale to denser inputs due to representation overload from excessive feature tokens and the resulting high computational cost.
Rather than introducing yet another ad-hoc encoder design, this work revisits the feed-forward 3DGS framework with inspiration from the Information Bottleneck (IB) [18] principle. IB offers a theoretical foundation for learning compact representations that preserve only task-relevant information. In the context of NVS with dense input views, we hypothesize that latent representation can be learned to capture essential scene information while discarding redundant details in dense multi-view inputs. By encouraging the formation of such compressed yet informative representation, we aim to improve the scalability of feed-forward 3DGS models (detailed in Sec. 3.2). Building on this insight, we propose a lightweight module, termed ZPressor, designed to be seamlessly integrated into the encoder of existing feed-forward 3DGS models to enhance their scalability. Unlike typical efforts that might rely on engineering optimizations such as nemory-efficiie attention [19] activation checkpointing [20], our approach adopts a principled perspective grounded in representation learning, aiming address core architectural limitations under dense input views settings.
To put this idea into practice, ZPressor implements the IB principle by explicitly compressing input view information, as illustrated in Fig. 2. Specifically, we divide the input views into two groups: anchor views and support views. Anchor views serve as the compression states, while information from support views is compressed into them. To ensure the compressed representation retains sufficient scene information, we select anchor views using farthest point sampling to maximize coverage with restricted views. The remaining views are assigned to their nearest anchor based on camera distance, and their features are fused into the anchors through a stack of customized cross- attention blocks. In essence, ZPressor takes multiple input views" features and their corresponding camera poses as input and produces a compact latent representation that preserves scene information. This design is architecture-agnosti and thus can be integrated into various feed-forward 3DGS models.
To validate the effectiveness of ZPressor, we integrate ZPressor into several state-of-the-an feed forward 3DGS models, including pixelSplat [7], MVSplat [8], and DepthSplat [12], and conduct extensive experiments on large-scale benchmarks such as DL.3DV [17] and RealEstate-100 10K [21] Results show that integrating ZPressor consistently boosts the performance of baseline models under moderate number of input views (e.g., 12 views), and helps them maintain reasonable accuracy

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and computational cost even with very dense imputs (e.g., 36 views, as shown in Fig. 1). where the original models typically degrade dramatically or run out of memory. Our contributions are threefold:
We provide a fundamental analysis of why existing feed-forward 3DGS models struggle with dense input views, through the lens of the Information Bottleneck principle.
Inspired by IB, we propose 7Pressor, an architecture-agnosiic module that can be integrated into the encoder of existing feed-forward 3DGS models to compress input view information.
Extensive experiments several large-scale benchmarks show that ZPressor consistently improves the performance of baseline models with a moderate number of input views, and further enhances robustness under dense input settings, where existing models typically degrade significantly.
2
Related Work
2.1
Information bottleneck and its applications
The challenge of managing and processing vast quantities of information is a central theme in the development of large-scale machine learning models, particularly in the visual domain [22-25]. The Information Bottleneck principle [18] formalizes the problem of extracting a compressed rep- resentation Z from the input X, such that Z is maximally informative about the target Y. The IB principle was subsequently extended to the domain of deep learning [26-29], VIB [30] providing a tractable lower bound on the IB objective, bridging the gap between the theoretical IB principle and practical deep learning applications. A series of works have applied the IB principle to multi-view inputs [31-33], extract information that is common or shared across multiple views while discard- ing view-specific or redundant information. The drive towards efficient 3D scene reconstruction, especially within the context of 3DGS [1], has also seen the adoption of information-theowt StreamGS [34] tackle redundancy in image streams by merging superfluous Gaussians through cross-frame feature aggregation, while other works [35, 36] focus on compressing the learned 3D Gaussians. While demonstrating the effectiveness and necessity of compression in multi- view data and 3D reconstruction, none of the existing works have explored it in the context of feed-forwer forward 3DGS. Our work aims to bridge this gap by introducing the lens of IB for information compression in this area.
ideas,
2.2 Optimization-based NeRF and 3DGS
Traditional novel view synthesis (NVS) methods primarily rely on image blending techniques [37, 38]. More recently, neural network-based approaches [39-42] have advanced NVS by integrating it with deep learning models. In particular, NeRF [42] employs an MLP to map 3D spatial locations and viewing directions radiance color and volume density. Numerous works [43-48] have sought to improve NeRF's efficiency and reconstruction quality. However, its reliance on volume rendering [49] hinders rendering speed, limiting its practicality in real-world applications. Recently, 3D Gaussian Splatting (3DGS) and its variants[2, 36, 50-52] have emerged as efficient solutions for large-scale scene reconstruction and synthesis, offering explicit representations and fast isserization-baser rendering that outperform NeRF's slower volumetric approach. Nonetheless, its requirement for slow per-scene optimization still poses challenges for deployment in downstream tasks.
2.3 Feed-Forward NeRF and 3DGS
To address the limitation of slow per-scene optimization, PixeINeRF [53] pioneered the ferw-forward NeRF (a.k.a. generalizable NeRF) by introducing an additional network that directly encodes input views into NeRF representation. This allows the model to benefit from large-scale training and predict scene representation in single forward pass. This direction has since seen significant advancements [54-58], offering promising path toward practical NeRF deployment. This paradigm has recently been extended real-time rendering with 3DGS [1] replacing NeRF [42]. Among them, 3DGS approach by combining epipolar transformers with depth prediction to predict 3D Gaussians from two input views. MVSplat [8] proposed an efficient wowooooluueeseeeeee fusion strategy to improve multi-view reconstruction, while DepthSplat [12] leveraged monocular depth features to better recover fine 3D structures from sparse inputs. Despite the growing number of feed-for rred-forward 3DGS models [59-63], most follow pixel-aligned design, where
pixelSplat [7] pioneered the
3

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Feature Extractor
Gaussian Prodictor
Input Feature X I: Select anchor views.
Step Assign other views
Step 3: Merge support views Output Feature
to the corresponding group.
features into anchor views
Figure Overview of ZPressor for Ferw-Fo 3DGS. Our proposed ZPressor an architecture- agnostic module designed for feed-forward 3DGS frameworks. It addresses the challenge of pro- cessing dense input views by strategically grouping input view features X' based on selected anchor views, then features within each respective group are compressed as Z.
the number of 3D Gaussians scales linearly with the number input views. This leads to significant memory and computational overhead as input views increase. While works like FreeSplat [10] and GGN [11] attempt to reduce the number of Gaussians by merging them via cross-view projection checking, they lack principled framework. In contrast, work provides theoretical perspective on the representation overload problem in feed-forward 3DGS by introducing the IB [18] principle. And propose architecture-agostii module ZPressor that can be seamlessly integrated into existing feed-forward 3DGS models to improve performance under dense input view settings.
3 Methodology
3.1 Overview of ZPressor
Our ZPressor an architettur--anoosicc module for compressing the multi-view inputs of feed- forward 3DGS, as illustrated in Fig. 2. Formally, given K input views V = (Vi) where
V, E ##* x3 and their corresponding camera poses P = [P:) our ZPressor takes the extracted features from each view as input:
X' F, F
T Pimage (V,P)
F F
where Pimage is a pretrained image encoder. Then our ZPressor adaptively compresses these heavy multi-view features into compact ones 2 7Pressor(\''. Subsequently, we directly unprojects these compact latent representations into 3D space using the camera poses P and a pixel-aligned Gaussian prediction network V pred is employed to estimate the Gaussian parameters:
y= (pi, E, a,, c:) H pred P).
(2)
The Gaussian parameters include 14, opacity a, covariance matrix Σ, and color c, while this pixel-aligned prediction results linear increase in Gaussian primitives with more input views, constraining the model's input capacity.
3.2 Information Analysis of Feed-Forward 3DGS
Existing feed-forward 3D Gaussian Splatting networks suffer from a dramatic performance drop and exponential increase in computational cost when the amount of input view information grows (see Tab. 1), primarily due to information redundancy and the lack of adaptive information compression mechanisms. Specifically, the total information of the scene is represented by the joint entropy H (F,, F2, F Fx), which is not merely the sum of the individual entropies of the features from all views, i.e., ΣK Ei= (F.). Therefore, there is significant amount of redundant information in the

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features, and it is crucial to remove the irrelevant information after feature extraction while preserving its predictive power, which allows for the efficient use of information from the input views. priciple way for modeling this is the Information Bottleneck (IB) [18], which minimizes the IB scores as:
min = 3I(X, 2)
1(2, ))
Compression Score
Prediction Score
where 3 2 O controls the balance between compression and prediction, and [(--)) -) is the mutual infor- mation. The Compression Score component, B1 2), encourages Z to be concise representation of the input X. Minimizing / (X' Z) means reducing the amount of information Z carries about X. which leads to better compression and enhanced efficiency. The Prediction Score component, [(2, ))), measures the predictive power of the latent feature 2 with respect to the target variable V. Maximizing this term ensures that Z retains sufficient task-relevant information about V, which is vital for maintaining or improving prediction accuracy.
As shown in Fig. 2, the Prediction Score is typically modelled by the Gaussian predictor. In the next section, we introduce ZPressor, a novel module applied concatenated with the image encoder to model the Compression Score through three consecutive designs, yielding a compact presentation.
3.3 Information Compression Module: ZPressor
To ensure architecture agnnnsst integration, compression is performed along the view dimension, rather than being entangled with the design of specific model. Concretely, given the K encoded views X, we first divide them into N anchor views Kanchor = {F J and M support views Ksupport = {F, s, } LM
where a,, Sj E {l...., K}, Maanchornxaunpa = and Xanchor UX = X. Here, X anchor are expected to capture the essential information of the scene, while Xsupport contain supporting context but may include redundancy. Compression then achieved by fusing the features of support views into their corresponding anchor views. This raises three design questions: 1) how to select the anchors Kanchor. 2) how to assign Xaupport to specific anchors, and 3) how to fuse the information from Xaupport into their designated Kanchor-
Anchor view selection (Fig. 2 Step 1). Given set of camera positions T = [T. E Rx3 calculated from camera parameters P, where f add random view to the anchor view list S = {T a, }, where Ta, ~ Uniform(T).
F such that f is a bijective mapping, we first
Subsequent anchor views iteratively selected the view with the greatest distance the current anchor view set S:
T Ta--1 arg max min d(Tj, Tk)):
T,ETiS (TEE
where d(.,.) denotes the Euclidean distance. This procedure is repeated until N anchors are selected, resulting diverse and representative set Xanchor-
Support-to-anchor assignment (Fig. 2 Step 2). Once anchors are selected, each support view is assigned to its nearest anchor based camera position. This ensures that support views, which capture complementary scene details, are grouped with the most spatially relevant anchor views, thereby ensuring the effectiveness of information fusion. Formally, the cluster assignment to the i-th anchor view can be denoted as:
C. = (T) E Xeupport
T
To.
S T Ta Vlj Vj i
(5)
Views information fusion (Fig. 2 Step 3). Upon obtaining the anchor- support view- baseb clusters, we aim to fuse the information within each cluster. The fusion mechanism should satisfy the following properties: 1) use the anchor views as the base, while effectively integrating information from the support views to enhance them, and 2) capture the similarity between the two sets of views, maintaining compactness while avoiding redundancy.
5

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Building on the above analysis, the fusion within each cluster can be effectively achieved via cross- attention:
2 A,ttentionn(, V),,
Xauchor;
K, V
X support:
(6)
where the features Kanchor extracted from the anchor views are used as queries, while the features Lauppart from the support views provide keys and values. In this way, information from the support views is effectively integrated into the anchor views, satisfying the first property. Additionally, ensures gradient flow from the prediction to both anchor and support views, enabling the model to capture correlations between the two sets of views, thus satisfying the second property. Moreover, since most existing feed-forwart 3DGS encoders adopt Transformrrbbsee architectures, the attention module can be readily integrated into these frameworks.
Training. Upon obtaining the compressed information Z, we can apply the IB principle to regularize the information flow. Recall that, as in Eq. (3), our current goal should be to minimize the Compression Score ; Nsupport: 2) and maximize Prediction Score [( y). For the Compression Score, we apply constraint on its complexity, i.e., setting the number of anchor views N to value acceptable for training. For the Prediction Score, we have
I(2:)) = H()) H() 2):
where ()) is a constant representing the 3D Gaussians that model the underlying 3D scene. Hence, maximizing (() is equivalent minimizing H(Y
2), which essentially encourages the
predicted 3D Gaussians y to resemble the original scene.
Then incorporate the IB principle into feed-forward 3DGS training, using the efficient estimate of Eq. (3) following [26, 30]. Formally, the training objective is defined as:
[- log go ( 2)] + 13 r(2)]]
(8)
2=po(2
where á denotes the parameters of 3D Gaussians prediction network and log 4s ( Σ) can be modeled by the rendering loss (such as the MSE and LPIPS loss), denotes the parameters of the network preceding Z and pa (Z X²) is the posterior probability estimate of Z, r(Z) N/Z Ho, 20) is the Gaussian prior of Z.
In our implementation, we append an additional oeff-attention layer to further enhance information flow within each cluster. addition, we stack several blocks (each containing both cross- and self-attention) to further improve the effectiveness of the IB principle. These two general-purposs purpose engineering techniques help boost performance, as shown in Tab.
Experiments
4.1 Experimental Settings
Datasets. We validate the effectiveness of our ZPressor on the NVS task, following existing works [7, 8, 12], and conduct experiments on two large-scale datasets: DL3DV-10K (DL3DV) [17] and RealEstate 10K (RE10K) [21]. DL3DV a challenging large-scale dataset that contains 51.3 million frames from 10,510 real scenes. We used 140 benchmark scenes for testing and the remaining 9896 scenes for training, with filtering applied to ensure that there is strictly no overlap between the training and test sets. RE1OK offers large-scale collection of indoor home tour clips, comprising 10 million frames from around 80,000 video clips sourced from public YouTube videos. It is split into 67, .77 training and 7,289 testing scenes. Both datasets feature real-world captured scenes, with camera intrinsics and extrinsics reconstructed using COLMAP [64, 65].
Baselines and metrics. To evaluate the effectiveness and flexibility of our proposed ZPressor, we integrate as a module into three representative baselines, including DepthSplat [12], MVSplat [8], and pixelSplat [7]. For fair comparisons, we insert ZPressor into the official implementations of each baseline and strictly follow the experimental settings described their respective papers. Specifically, compare with DepthSplat on DL3DV, following its training strategy by first pre-training on RE10K

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Table 1: Quantitative comparisons on DL3DV [17]. We evaluate both DepthSplat [12] and DepthSplat with ZPressor with 12, 16, 24, 36 input views and test on eight target novel views.
Views
Methods
PSNRT
SSIMT
LPIPS
SppthSplat DepthSplat + ZPressor
19.23
0.666
0.286
36 views
23,88
0.815
0.150 0.13
SppthSplat DepthSplat + ZPressor
20.38
0.711
0.253
24 views
24.26
0,820 v0.10
0.147 a.106
SepthSplat DepthSplat + ZPressor
22.07
0.773
0.195
16 views
24.25
0.819
0.147 au147
DepthSplat DepthSplat + ZPressor
23.32
0.807
0.162
12 views
24.30.
0,821 .0.014
0.146
Table 2: Quantitative comparisons on RE10K [21]. We test pixelSplat [7] and MVSplat [8] on eight target views, "OOM" represent that model cannot infer on an 80G GPU.
Views
Methods
PSNR1
SSIM1
LPIPS,
pixelSplat
OOM
OOM
OOM
pixelSplat + ZPressor
26.59
0.849
0.225
views
MVSplat
24.19
0.851
0.155
MVSplat + ZPressor
27.34
0.893
0.113
pixelSplat pixelSplat + ZPressor
OOM
OOM
OOM
26.72
0.851
0.223
views
MVSplat MVSplat+ + ZPressor
25.00
0.871
0.137
27.49-2.49
0.895 HUIZA
0.111 -.0026
pixelSplat pixelSplat + ZPressor
OOM
OOM
OOM
26.81
0.853
0.221
16 views
MVSplat
25.86
0.888
0.120
MVSplat + 7Pressor
27.60-1.4
0.896-au
0.110
pixelSplat pixelSplat + ZPressor
26.19
0.852
0.215
26.86
0.854
0.219.a.04
8 views
MVSplat MVSplat+ ZPressor
26.94
0.902
0.107
27.72
0.897-4.05
0.109.0.02
and then fine-tuning on DL3DV, Comparisons with MVSplat and pixelSplat are conducted on RE10K. Following these baselines, we report quantitative results using PSNR. SSIM [66], and LPIPS [67]. As our module focuses on information compression, we additionally report model efficiency in terms of runtime and memory consumption.
Implementation details. We use the same computing resources to train the baseline and our method. Due to the memory limit, for pixelSplat. For all of our experiments, we adopted the same learning rate as the baseline, utilized the AdamW optimizer. and trained the models for 100,000 steps on A800 GPUs. Following the setting of the baseline, we use the 256 x 256 input resolution on REIOK, and 256 448 input resolution on DL3DV, All training losses match those of the baseline, with additional data or regularization introduced.
6 context views for DepthSplat and MVSplat, and 4 context views
4.2 SoTA Comparisons and IB Analysis
Comparisons with SoTA models. We train all models on DL3DV and REIOK using 12 input views with anchor views set to our ZPressor, and evaluate them under varying numbers of input views ranging from 8 to 36. As shown in Tab. I and Tab. 2, integrating ZPressor into DepthSplat, MVSplat, and pixelSplat consistently improves their performance across input view settings and evaluation metrics, demonstrating the effectiveness of approach.
7

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Inputs
DepthSplat [12]
DepthSplat ZPressor
Ground Truth
Figure 3: Qualitative comparison on DL3DV [17] under dense input conditions (36 views). DepthSplat [12] performs poorly due redundancy dense views, ZPressor effectively compresses this information, achieving significantly improved visual results.
Inputs
MVSplat [8]
MVSplat + ZPressor
Ground Truth
Figure 4: Qualitative comparison on RE10K [21] with 36 input views. MVSplat [8] with ZPressor
performs the best in all cases.
Notably, the performance gain becomes more significant the number of input views increases. This is because existing feed-forward 3DGS models struggle with dense inputs due to representation over- load, leading to performance degradation. contrast, ZPressor mitigates this issue by compressing the input through redundancy suppression while preserving essential information, improving model robustness, and maintaining strong performance under dense input settings. Moreover, we observe that pixelSplat fails to run with more than 8 input views due to out-of-memory (OOM) caused by the large number of predicted pixel-aligned 3D Gaussians. In contrast, our Z.Pressor helps merge input information, reducing the number of predicted Gaussians and enabling testing with up to 36

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DopthSplat
DepthSplar-PPresorr
Gaussians(K)
Inference Time(ms)
Memory(GB)
4000
600
15
3000
400
10
1000
200
32
Figure 5: Efficiency analysis. We report the number of Gaussians (K), inference time (ms) and peak memory (GB) of DepthSplat [12] and DepthSplat with ZPressor.
Contual Gup-50
Table 3: Analysis of Information Fusion. "de- fault" denotes our setting where support views are fused into anchor views. "w/o fusion" re- moves the fusion step, and "fuse anchors" fuses repeated anchor views instead. "default" per- forms best, indicating that ZPressor improves performance by effectively fusing complemen- tary information from the support views.
Context Gap-100 22.4
24.42
PSNpRCCkp-- 1000
24.38
22.0
21.34
24.30
21.6
Method
PSNR
SSIM
LPIPS
Figure 6: Analysis of the bottleneck constraint. We compare the performance of ZPressor dif- ferent scale of scene coverage.
default
24.30
0.821
0.146
fuse anchors
24.23
0.817
0.148
w/o fusion
23.80
0.810
0.162
views. These observations are further supported by the qualitative comparisons shown in Fig. 3 and Fig. 4, where DepthSplat and MVSplat exhibit noticeable artifacts under 36 input views, whereas their ZPressor- auggeeer- versions produce significantly cleaner renderings.
Analysis of model efficiency. Compressing the input view information not only improves robustness and performance, but also enhances efficiency. To validate this, we compare the model efficiency of DepthSplat with and without ZPressor under 480P resolution, evaluating the number of 3D Gaussians, the test-time inference latency, and the peak memory usage. As shown Fig. 5, the benefits of integrating ZPressor clear. In particular, the number of context views increases, the baseline model's predicted 3D Gaussian numbers, memory usage, and inference time all grow linearly, whereas ZPressor helps maintain stable resource consumption across all aspects.
Analysis of the bottleneck constraint. In the context of NVS, the information corresponds the overall region that the scene covers. Since we evaluated on the static video data DL3DV, longer sequences usually cover larger regions. Therefore, we use the frame distance between input views as proxy for both scene coverage and information content.
We then analysis the effect of bottleneck constraint (the number of anchor views) under scenes with varying information content by adjusting the frame distance between input views. As shown Fig. 6, we conduct experiments under two settings, Context Gap 50 (CG50, in blue) and Context Gap 100 (CG100, in pink), where context gap refers to the frame distance between input views. When the scene's information content is relatively low (e.g., small camera baseline, similar views, as proxied by small CG like 50), a smaller number of anchor views (e.g., 7) is already sufficient to capture the essential scene information. Adding more anchor views in such a scenario might introduce redundancy or ambiguity, as these additional anchors might not observe genuinely new regions but rather re-observe already covered areas from slightly different perspectives. This could lead to less compact and potentially noisier latent representation, hence the performance drop. In contrast, scenes with higher information content (larger CG like 100), more anchor views are beneficial as they help cover more diverse perspectives and capture richer scene details. These results

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Table 4: Ablation study of our method with DepthSplat [12] on the DL3DV dataset [17]. Models are evaluated by rendering eight novel views using 12 input views.
Methods
PSNRT
SSIM
LPIPS.
Time (8)
Peak Memory (GB)
DepthSplat + ZPressor
24.30
0.821
0.146
0.184
3.80
w/o multi-blocks
24.18
0.817
0.149
0.140
3.79
w/o self-attention
23.85
0.810
0.1.6
0.183
3.80
DepthSplat
23.32
0.808
0.162
0.260
6.80
highlight the effectiveness of our ZPressor as an instantiation of the IB principle and show that the information bottleneck is critical in balancing compression and information preservation.
Analysis of information fusion. To further confirm that our ZPressor effectively fuses information from support views into anchor views rather than simply discarding it, we conduct experiments by varying the fusion strategy. As shown in Tab. 3, removing the information fusion step ("w/o fusion") leads to a performance drop, highlighting the importance of fusing support views into anchor views. To ensure that the introduction of support information does result performance improvement, we conduct a control experiment by fusing repeated anchor views instead of support views ("fuse anchors"). Since this does not introduce new information, performance is lower than our default setting, which achieves the best results by fusing complementary information. These comparisons further validate that our design effectively implements the IB principle.
4.3 Ablation Study
As mentioned at the end of Sec. 3, the default ZPressor uses several stacked attention blocks, each combining self-attention and cross-attention. This section presents an ablation study to validate our design.
We report results DL3DV with 12 input views, following the setting in Tab. 1, using DepthSplat as the backbone. As shown in Tab. 4, removing the stacking design and using only one block ("w/o multi-blocks"" slightly degrades performance, suggesting that stacking improves fusion of information from support to anchor views. Additionally, removing the self-attention ("w/o self attention") also reduce performance, showing that self-attention complements cross-attention by enhancing internal feature interactions. Overall, all variants of ZPressor outperform the DepthSplat baseline, confirming the existence of information bottleneck forward 3DGS models and the effectiveness of our ZPressor in addressing it.
5 Conclusion
We have provided a fundamental analysis of the model capacity limitations in existing feed-forward 3DGS models through the lens of the Information Bottleneck principle. Building on this insight, we have introduced ZPressor, a lightweight, multi-view inputs, enabling models to overcome inherent limitations and scale to handle more input views. We have validated our ZPressor by integrating it into several representative feed forward 3DGS models. Our experiments on several large-scale benchmarks have demonstrated that ZPressor not only consistently improves the performance of existing models under moderate view settings, but also helps them maintain competitive efficiency under denser inputs. We believe that ZPressor significantly enhances the scalability and practicality of ford-forward 3DGS models, opening the door to more effective applications in weal-world scenarios.
schitecture-agnostie module that efficiently compresses
Limitation and discussion. Our ZPressor may be less effective in extremely dense view settings. For example, given 1000 input views, ZPressor can only compress them around 50 views in order to maintain the information compactness as regularized by the IB principle. However, handling 50 views of 3D Gaussians still presents significant computational challenges for typical GPUs. Future work could explore combining ZPressor with 3D Gaussian merging or memory -fffiieefffiiennt rendering to extend reed-forward 3DGS handle extremely dense input views.
Acknowledgements. We thank Biao Wu for his enlightening discussions and inspiration on network architecture.
10

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A More Experimental Analysis
A.1 Cross Dataset Generalization
Following SVSpl [8], conducted experiments using pretrained model on the RealEstate 10K (RE10K) dataset [21] (as detailed in Tab. 2) and tested its performance on the ACID dataset [68] evaluate the generalization capabilities of our proposed ZPressor across diverse datasets. As demonstrated in Table A, MVSplat with ZPressor exhibits remarkable efficacy in cross-dataset generalization. Notably, this performance advantage becomes progressively more pronounced with an increasing number of input views.
Table Quantitative comparison on ACID [68] with trained model on RE10K. Trained on indoor scenes (RE10K), MVSplat [8] and pixelSplat [7] with ZPressor perform much better as evaluated on ACID dataset.
Views
Methods
PSNRT
SSIM
LPIPS./
pixelSplat pixelSplat Ours
OOM
OOM
OOM
27.78
0.823
0.238
36 views
MVSplat
24.89
0.812
0.179
MVSplat + Ours
28.16+3.27
0.853+0.41
0.145 0.034
pixelSplat pixelSplat Ours
0OM
OOM
OOM
27.91
0.825
0.235
24 views
MVSplat
25.46
0.829
0.167
MVSplat + Ours
28.33
0.856
0.142-a.0 -0025
pixelSplat pixelSplat Ours
OOM
OOM
OOM
27.97
0.826
0.234
16 views
MVSplat
26.08
0.844
0.156
MVSplat Ours
28.42
0.858
0.141
pixelSplat
26.69
0.807
0.260
pixelSplat Ours
28.05+136
0,828
0.234-oxa -0926
8 views
MVSplat
27.89
0.864
0.140
MVSplat Ours
28.60.a.7
0.860.n.04
0.140.a.om
A.2 Other Selection Strategies
In addition to our final choice of FPS-based selection strategy (Fig. 2 Step 1), we also experimented with other anchor views selection strategies. Below are detailed implementation descriptions for each strategy and comparison of all strategies' performance in Tab. B.
Overlap-based selection. We try overlap-based selection by computing a pairwise overlap matrix between camera views. The overlap is obtained by projecting dense pixel rays from one view into another and averaging the proportion of pixels that fall within the target image plane. The final overlap score between two views is the minimum of the bidirectional projections. We then apply a greedy vectorized algorithm that iteratively selects views with the lowest average overlap to the already chosen set, ensuring maximal scene coverage with minimal redundancy. This procedure produces both the anchor indices and the cluster assignments for the remaining views.
Pose-free settings. In the absence of camera poses, cluster learned per-view tokens instead of 3D positions. We use K-Means in the feature space of global view embeddings. Each token assigned its nearest cluster center, and the closest token to the mean of each cluster is selected as the anchor. This pose-free clustering provides a practical alternative for unposed datasets, as the anchor set is derived entirely from image content rather than camera geometry.
K-Means-based selection. K-Means-based selection groups view positions directly. Given the 3D camera centers all input views, we apply K-Means with a fixed number of groups and identify the anchor of each cluster the view closest to its centroid. Each remaining view is assigned
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Table Quantitative comparison of different selection strategies integrated with Depth- Splat [12]. All variants are evaluated with 36 test views on DL3DV [17].
Methods
PSNRT
SSIM
LPIPS
SppthSplat
19.23
0.666
0.286
SepthSplat Ours (overlap-based)
21.49
0.727
0. 0.199.00m
Sppth Ours (pose-free)
22.81 43.58
0.791 40.125
0. a.122
DepthSplat Ours (K-Means-based)
22. .3.11
0.789 12
0.175.o
DepthSplat Ours
23,88
0.815-0.10
00.10...136
its nearest centroid, producing compact and spatially coherent groups. This procedure is fully deterministic given the random seed and provides stable anchors that reflect the geometric distribution of input cameras.
A.3 Comparison with Confidence-basee Pruning
We compare ZPressor against confidence-based pruning after predicting Gaussians in MVSplat [8]. Confidence pruning removes input views based on their predicted reliability scores, with a fixed pruning ratio controlling the proportion of views retained. When maintaining the same number of Gaussians, the aggressive removal views leads to substantial performance drop due to insufficient multi-view support. At pruning ratio of 0.5, the method achieve the best accuracy, but the gain remains limited because the retained subset does not guarantee spatial coverage of the scene. In contrast, ZPressor consistently compresses all input views into compact latent anchors while maintaining balanced coverage, which yields superior accuracy without discarding information.
Table C: Comparison with confidence-based pruning (CP) in MVSplat [8] under 24 views on RE10K. For fair comparison, the prune ratio for CP is set to 0.75 to yield a same number of Gaussians as ZPressor, and we also manually adjust the prune ratio to 0.5 to achieve the best performance. All models are re-trained.
Methods
PSNR
SSIMT
LPIPS
MVSplat
25.00
0,871
0.137
MVSplat CP (prune_ratio 0.75)
21.15-385
0.816
0.190+a.05
MVSplat + CP (prune_ratio 0.5)
26.94.1.4
0,886+0.015
0.130 0.007
MVSplat Ours
27.49.2.4
0.895 .0.04
0.111
A.4 Ablation Study of IB-Loss
We conduct an ablation study to evaluate the role of the compression term derived from the Information Bottleneck formulation. When the compression term is removed (3 0), ZPressor still improves significantly over the baseline DepthSplat, confirming that the architectural design alone provides strong benefits. Introducing a small but non-zero cocfficient (3 10 10-5) 5) further encourages compact latent representations and yields the best balance between distortion and fidelity. This demonstrates that the IB- (B-inspired loss can serve as lightweight regularizer to assist compression module learning in achieving better compression results.
Table D: Ablation of the IB-inspired compression loss DepthSplat [12] with 36 views on DL3DV [17]. The compression term provides additional regularization that improves overall perfor- mance.
Methods
PSNR1
SSIM
LPIPS
DepthSplat
19.23
0.666
0.286
DepthSplat + Ours (3 =
23.43
0.806 -0110
0.165 0.121
DepthSplat + Ours (3 10 15
23,88 4.65
0.815
0.150 0.136
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B More Implementation Details
Network architectures. In Algorithm 1, we provide a detailed description of how ZPressor is integrated into existing feed-forward 3D Gaussian Splatting (3DGS) frameworks [7, 8, 12]. Initially, we select anchor views and their corresponding support views following Algorithm 2 and Eq. (5). The features associated with these views are then processed by attention-based network. This network composed of 6 structurally identical blocks, wherein each block encompasses cross-attention layer, self-attention layer, and MLP layer. The cross-atiention mechanism operates employing the anchor features as query, while the support features provide the key and value. Subsequent to this fusion, the resulting features are further refined by the self-attention and MLP layers.
Algorithm Overview of Feed-Forward 3DGS framework with ZPressor.
Input: K input views V (Vi) ;=1 camera poses P (P:) the number of anchor views N, the number of network blocks h. Output: Gaussian parameters y (11, E, a, c))- X' f
Dimage (V, P)
X anchor Ksupport f X, with Anchor view selection.
Assign support views to anchor cluster C &- Ksupport Initialize state Z t Xauchor
for i < to h do 2 Attention Q, KVK. V). where Q + 2 K.V
Xaupport
Z + Attention Q, K, V), where Q,K, V t Z
2 22)
end for
{(4,, E, a; c;)) t- pred (2ed(22,7) (3,P) P) return y + {1ug, Ei, 04, c:)}
Algorithm 2 Farthest Point Sampling for Anchor View Selection
Input:
Set of view camera positions T (Ti, T2, ..., Tk Number of anchor views N Output: Indices the selected anchor views = (Te,: Tay..., To,)
Initialize the set of anchor view indices S +- 0
Randomly select a random anchor view Ta, E T, where Ta, ~ Uniform Add Ta, to S: S < {Ta, for j < 2 to N do Initialize dictionary to store minimum distances D 0 for k +- 1 to K do if l: $ S then
Calculate the minimum distance dy + mings TT - Tell2 Store the distance: D[k] +- ds
end if end for
Find the view position Ta with the maximum minimum distance: Tay + arg max D[k] Add az to S: S - S }
end for return S
To ensure training stability, deviating from traditional Transformer architectures, we employ Pre- Layer Normalization [69] (Pre-LN), which enhances the robustness of the model. Furthermore, system-level advancements have been incorporated to accelerate computation. For example, we employ FlashAttention [19, 70], which uses highly optimized GPU kernels and leverages hardware topology to compute attention in a time- and memory-efficient manner.
More training details. We use the first model version of DepthSplat [12] from its October 2024 release. Experimental results obtained with this specific version may exhibit slight variations when compared the current version. 7Prrssoor was incorporated subsequent to the monocular feature extraction performed by CNN.
17

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Inputs (~500 views) Figure A: Limitations. Visual results from extremely dense input views show slightly poor presenta- tion effect.
DepthSplat + ZPressor
Adhering to its original configuration, experiments were conducted at resolution of 256 x 448. The model was initially trained on the RE10K [21] for 100,000 steps and subsequently fine-tuned on the DL.3D [17) for an additional 100,000 steps. We employed the Adam W optimizer [71] with learning rate of 2 x 10-1 The total training duration was approximately two days, and the integration of ZPressor did not significantly alter the original training time of DepthSplat.
Similarly, for MVSplat [8] and pixelSplat [7], ZPressor was integrated after the monocular feature extraction stage. MVSplat utilizes CNN for feature extraction, whereas pixelSplat employs DINO [24, 25]; this architectural choice in pixelSplat contributes to a marginally higher VRAM consumption compared to the other two baselines. We maintained the model parameter settings as published in their respective original works, training models on the RE10K [21] at a resolution of 256 x 256. The learning rate both of which were trained for 100,000 steps. Notably, due to memory constraints, we trained the pixelSplat model incorporating ZPressor using 4 anchor views, in contrast the 6 anchor views configured for DepthSplat and MVSplat. The training times for MVSplat and pixelSplat, when augmented with ZPressor, remained comparable their original durations.
to 2 10-1 for MVSplat and 1.5 10-1 for pixelSplat, where
We will open-source the complete codebase for ZPressor, our "Pressor-integratee versions of DepthSplat, MVSplat, and pixelSplat, and all associated model checkpoints.
C Limitation and Societal Impacts
Limitation analysis. As discussed Sec. 5, ZPressor exhibits limitations when processing scenarios with an extremely high density of input views. Specifically, its efficacy in compressing the information from such dense views through a limited set of anchor views is diminished. To illustrate this, we conducted experiment DepthSplat [12] integrated with ZPressor, using approximately 500 images as input. As depicted in Fig. A, the quality of the rendered novel views was perceptibly affected, which can be attributed represent the scene under these dense input conditions.
insufficient number of Gaussian primitives to adequately
Potential and negative societal impacts. ZPressor can significantly reduce the training costs associated with feed-for ford-fo 3DGS networks. It enables the processing of a larger number of input views within the same VRAM budget and training duration, delivering fighfiideiity rendering results and thereby decreasing energy consumption during the model training process. While the capability to render higher-quality novel views from more densely sampled perspectives positions ZPressor as valuable tool for augmented reality applications, it is important to acknowledge that the fidelity of the rendering can be compromised by the emergence of artifacts, particularly when processing input views of extremely high density. Consequently, in safety-critical applications, such as the training of
18

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Inputs (36 views)
DepthSplat [12]
DepthSplat ZPressor
Ground Truth
Figure B: More qualitative comparisons on DL3DV [17] with DepthSplat [12] under 36 input views. Models with ZPressor performs the best in all cases.
autonomous driving models, the deployment of ZPressor would necessitate the implementation of additional precautionary measures to mitigate potential risks arising from such limitations.
D More Visual Comparisons
This section provides additional qualitative comparison results. We present further visualizations for DepthSplat [12] on the DL3DV [17] and MVSplat [8] on the RE10K [21] in Fig. B and Fig. F, with ZPressor.
Furthermore, to illustrate how ZPressor performs with dense input views, we showcase comparative results. For DepthSplat [12], comparisons between the original framework and DepthSplat augmented with ZPressor are presented for scenarios with 24, 16, and 12 input views in Fig. C, Fig. D, and Fig. F. Similarly, for MVSplat [8], visual comparisons between the original framework and MVSplat integrated with ZPressor are displayed for inputs of 24, 16, and 8 views in Fig. G, Fig. H, and Fig. I. The corresponding quantitative results for these multi-view experiments can be found in Tab. 2.
19

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Inputs (24 views)
DepthSplat [12]
DepthSplat '.rressor
Ground Truth
Figure C: More qualitative comparisons on DL3DV [17] with DepthSplat [12] under 24 input views.
Inputs (16 views)
DepthSplat [12]
DepthSplat ZPressor
Ground Truth
Figure D: More qualitative comparisons on DL3DV [17] with DepthSplat [12] under input views.
Inputs (12 views)
DepthSplat [12)
DepthSplat + 2.ressoo
Ground Truth
Figure E: More qualitative comparisons on DL3DV [17] with DepthSplat [12] under 12 input views.
20

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Inputs (36 views)
MV [8]
MV + ZPressor
Ground Truth
Figure F: More qualitative comparisons on RE10K [21] with MVSplat [8] under 36 input views. Models with ZPressor performs the best in all cases.
21

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Inputs (24 views)
MV [8]
MV ZPressor
Ground Truth
Figure G: More qualitative comparisons on REIOK [21] with MVSplat [8] under 24 input views.
22

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Inputs (16 views)
MVSplat
MV ZPressor
Ground Truth
Figure H: More qualitative comparisons on RE1OK [21] with MVSplat [8] under 16 input views.
Inputs (8 views)
MVSplat
MV ZPPressor
Ground Truth
Figure I: More qualitative comparisons on RE10K [21] with MVSplat [8] under 8 input views.
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Reviews:
[{"review_id": "igsDUW4icZ", "reviewer": "Reviewer_9obw", "summary": "This paper tackles the scalability problem of feed-forward 3DGS, which is limited by the encoder's capacity, leading to performance degradation or high memory use as the number of input views grows. The authors introduce ZPressor, a lightweight, architecture-agnostic module that efficiently compresses multi-view inputs into a compact latent state. This compression retains essential scene details while removing redundancies.\n\nZPressor splits the views into anchor and support sets. Anchor views serve as compressed states, and support views’ information is compressed into these anchors using cross-attention. Farthest point sampling is used to select anchor views, maximizing coverage with fewer views. Remaining views are assigned to the nearest anchor based on camera distance, and their features are fused into the anchors via cross-attention blocks.", "questions": "Pose-Free Settings:\nDemonstrating the effectiveness of ZPressor in such configurations could strengthen the case for its versatility and robustness.\n\nClarification of Figure 6:\nA deeper analysis of this phenomenon would help clarify the trade-off involved.\n\nAdaptive Selection of Anchor Views:\nIf you could demonstrate how this adaptation might work, it could further enhance the practical utility of ZPressor.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# Strengths\n\n## Effectiveness:\nZPressor enables feed-forward 3DGS models to scale to over 100 input views at 480P resolution on an 80GB GPU. The results demonstrate that integrating ZPressor consistently improves the performance of baseline models, especially with a moderate number of input views (e.g., 12 views). It helps maintain reasonable accuracy and computational cost even with dense inputs (e.g., 36 views), where original models would typically degrade or run out of memory.\n\n## Versatility:\nZPressor is architecture-agnostic, meaning it can be easily integrated into various feed-forward 3DGS models. The module improves performance under moderate input views and enhances robustness with dense view settings. It was successfully integrated into several state-of-the-art models (like PixelSplat, MVSplat, and DepthSplat) and tested on large-scale benchmarks (DL3DV-10K and RealEstate10K), consistently showing improvements.\n\n## Insightful Experiments:\nThe paper provides valuable insight, particularly in Tables 1 and 2, where the performance of the state-of-the-art models drop as input views become denser. This highlights the importance of the proposed method in improving scalability.\n\n# Weaknesses\n\n## Lack of Pose-Free Settings:\nThe paper does not explore pose-free settings such as NoPoSplat, FLARE, or AnySplat, which could provide additional context to the proposed method’s versatility.\n\n## Unexplained Performance Drop with Too Many Anchor Views:\nFigure 6, which analyzes the bottleneck constraint, introduces an interesting idea. However, it doesn’t fully explain why performance drops when there are too many anchor views. While it's clear that too few anchor views lead to performance degradation due to over-compression, the impact of having too many anchor views is less clear. A more detailed explanation of this phenomenon would be beneficial.\n\n##  Adaptive Selection of Anchor Views:\nThe method could benefit from a more flexible approach, allowing for adaptive selection of the number of anchor views based on input conditions. This could help optimize the performance further in different scenarios.", "was_revised": true, "final_justification": "The rebuttal addressed my main concerns. I have also read the reviews and rebuttals from the other reviewers. I would like to maintain my original opinion: This paper tackles the scalability issue of feed-forward 3DGS, which often leads to performance degradation and high memory consumption as the number of input views increases — a phenomenon consistently observed in many experiments. Tackling this challenge is valuable, and this paper represents a promising step in that direction. However, I also agree with the other reviewers that while the paper claims to be guided by IB theory, the current presentation lacks mathematical rigor. That said, the proposed approach is empirically consistent with the theory. I hope the authors can further clarify and strengthen their theoretical justification, using more precise language and derivations, in the camera-ready version.", "rebuttal": "## **Response to Reviewer 9obw**\n\nWe thank Reviewer 9obw for the positive assessment and constructive feedback. We are glad that the reviewer found our paper effective, versatile, and insightful. Below, we address the main discussion points raised by the reviewer:\n\n---\n\n### **Q1. Lack of pose-free settings**\n\nWe agree that the pose-free setting is an important issue, but NoPoSplat, which based on the MASt3R [1] architecture and pre-trained weights, was only trained on two views, making it an unfair comparison to use as a baseline; FLARE does not have an official open-source implementation for NVS training; AnySplat was released on arXiv (May 29, 2025) after the NeurIPS submission deadline, so we chose the best options available to us in this submission.\n\nFollowing the reviewer's suggestion, we explored a **pose-free anchor view selection** method. We clustered the features of the input views with K-Means to group all views and then determined the anchor views. We conducted this experiment on DepthSplat:\n\n| **Views** | **Methods**                   | **PSNR**  | **SSIM**  | **LPIPS** |\n| --------- | ----------------------------- | --------- | --------- | --------- |\n| 36        | DepthSplat                    | 19.23     | 0.666     | 0.286     |\n| 36        | DepthSplat+ZPressor(w/o pose) | 22.81     | 0.791     | 0.174     |\n| 36        | DepthSplat+ZPressor(w/ pose)  | **23.88** | **0.815** | **0.150** |\n\nThe experimental results demonstrate that ZPressor significantly outperforms the baseline, even with pose-free anchor selection, substantially improving the scalability of feed-forward 3DGS.\n\n---\n\n### **Q2. Clarification of figure 6: performance drop with too many anchor views**\n\nWe hypothesize that when the scene's information content is relatively low (e.g., small camera baseline, similar views, as proxied by a small Context Gap like 50), a smaller number of anchor views (e.g., 7) is already sufficient to capture the essential scene information. Adding more anchor views in such a scenario might **introduce redundancy or ambiguity**, as these additional anchors might not observe genuinely new regions but rather re-observe already covered areas from slightly different perspectives. This could **lead to a less compact and potentially noisier latent representation**, hence the performance drop. In contrast, for scenes with higher information content (larger Context gap like 100), more anchor views are beneficial as they help cover more diverse perspectives and capture richer scene details. We will clarify this in the updated version.\n\n---\n\n### **Q3. Adaptive selection of anchor views**\n\nWe agree that adaptive anchor selection is a promising direction. While our current farthest point sampling (FPS) method ensures a diverse and representative set of anchor views, we have explored an alternative following the reviewer’s constructive suggestion:\n\nWe implemented an **adaptive anchor view selection method** based on the overlap of every input views. Based on a given overlap threshold, our greedy algorithm stops when the overlap of all candidate views with the selected anchor views is greater than the given threshold. This results in different numbers of anchor views for different coverage areas, which to a certain extent ensures that the amount of scene information remains within the ideal range and realizes adaptive anchor view selection.\n\nThe experimental results of adaptive anchor view selection are as follows:\n\n| **Views** | **Methods**                             | **PSNR**  | **SSIM**  | **LPIPS** |\n| --------- | --------------------------------------- | --------- | --------- | --------- |\n| 36        | DepthSplat                              | 19.23     | 0.666     | 0.286     |\n| 36        | DepthSplat+ZPressor(Adaptive Selection) | 22.71     | 0.782     | 0.182     |\n| 36        | DepthSplat+ZPressor(FPS-based)          | **23.88** | **0.815** | **0.150** |\n\nThe overlap-based adaptive anchor view selection, while showing some improvement, doesn't consistently beat the FPS-based strategy. We think this is because the overlap calculation requires highly accurate camera poses, which our COLMAP-estimated poses may not provide. Therefore, using the default camera location is simpler and more effective. The view angle information doesn't change our core motivation, and we'll explore overlap-based settings with more precise pose data in the future.\n\nWe also counted the number of scenes in DL3DV-Benchmark(140 scenes) corresponding to different numbers of anchor views in this experiment when context gap is 50:\n\n| Number of Anchor Views | Number of Corresponding Scenes |\n| ---------------------- | ------------------------------ |\n| 6                      | 87                             |\n| 7                      | 23                             |\n| 8                      | 11                             |\n| 9                      | 19                             |\n\nThe results show that the algorithm does indeed select different numbers of anchor views for different scenarios.\n\n---\n\n### Reference\n\n[1] Leroy, Vincent et al. “Grounding Image Matching in 3D with MASt3R.” ECCV 2024."}, {"review_id": "yCB6gYLFvp", "reviewer": "Reviewer_nH8F", "summary": "Gaussian Splatting reconstruction has recently become a popular approach for feed-forawrd reconstruction. However, many approaches reconstruct the scene by predicting Gaussian Splats from every image, which becomes memory intensive when the number of input frames is high. This paper addresses this issue with an idea inspired by the information bottleneck principle: effectively compressing a long sequence of frames into a latent representation that operates on a smaller set of keyframes.", "questions": "Eq 6: I think the function operating on $Q, K, V$ is attention and not Cross-attn. What makes this cross attention is the source of $K$ and $V$.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "## Strengths\n- Compression for Gaussian Splatting representations is an important topic that is currently undexplored. \n- The paper is well written and good to follow. The notation is clear and precise. \n- The qualitative and quantitative results show that the method is effective at compression and thus can eliminate some floater artefacts. This, in turn, results in better reconstruction scores. \n\n## Weaknesses\nThere is a disconnect between the theoretical motivation through the information bottleneck and the actual implementation. Line 202 reads “we optimize the feed-forward 3DGS with ZPressor by the variational rendering loss, e.g., MSE and LPIPS.”. In my understanding, MSE is not a variational loss. It can be part of a variational loss if used as a reconstruction loss in a VAE-like formulation, but this would require a KL term. Similarly, while the framework is motivated via Eq. (3), but since there is no explicit term measuring the compression score, the hyperparameter $\\beta$ is unused, and no balance between compression and reconstruction is learned. It is, in a sense, pre-defined by the architecture choice.\nThe IB connection does not really help in designing, training, or improving the model .\n\nThe choice of anchor frames is purely based on the camera position. Typically, in SLAM-like applications, the view angle is taken into account when identifying new keyframes. This could be done in many ways, for example by computing the overlap in view frustrums. This clustering is chosen ad-hoc, and likely needs to be manually tuned. Can the IB formulation be used to learn the anchor frame selection strategy?\n\nMV Splat associates a confidence with every Gaussian. A simple baseline would use the confidence to prune Gaussians for compression. Would that reduce the floater artefacts present in the original MVSplat reconstructions?", "was_revised": true, "final_justification": "After reading the rebuttal, the other reviews, and additional responses, most of my concerns are sufficiently addressed to recommend acceptance.", "rebuttal": "## **Response to Reviewer nH8F**\n\nWe are grateful to Reviewer nH8F for the thorough evaluation of our work and for providing valuable suggestions. We have carefully considered all comments and provide our responses below:\n\n---\n\n### **W1. Clarification of the Information Bottleneck principle and implementation**\n\nWe appreciate the reviewer’s comment and suggestion. Our initial intention is that variational loss is **based on** MSE or LPIPS. Specifically, the loss implementation includes a first term based on MSE or LPIPS, and a second term based on a KL divergence multiplied by a coefficient β. These correspond to the reconstruction and compression terms, respectively.\n\nThis formulation follows the Information Bottleneck (IB) principle, which aims to learn a compact latent representation that retains task-relevant information while discarding irrelevant details. The reconstruction term encourages preservation of useful information for the output task, while the KL divergence penalizes excessive mutual information between the input and latent variables, thus enforcing compression. By adjusting the coefficient β, we control the trade-off between fidelity and compactness, balancing the model’s expressiveness and generalization.\n\nWe will clarify this unintentional misleading in the updated version.\n\n---\n\n### **W2. Anchor frame selection strategy (View Overlap)**\n\nWe appreciate the reviewer's valuable suggestions. We agree that incorporating view geometry (like view frustum overlap) could lead to more optimal anchor selection. Our current approach, farthest point sampling (FPS) based on camera positions, is a simple yet effective heuristic to ensure spatial diversity among anchor views. We report a comparison of experimental results using an anchor view selection method based on the overlap in view frustums:\n\n| **Views** | **Methods**                        | **PSNR**  | **SSIM**  | **LPIPS** |\n| --------- | ---------------------------------- | --------- | --------- | --------- |\n| 36        | DepthSplat                         | 19.23     | 0.666     | 0.286     |\n| 36        | DepthSplat+ZPressor(Overlap-based) | 21.49     | 0.727     | 0.194     |\n| 36        | DepthSplat+ZPressor(FPS-based)     | **23.88** | **0.815** | **0.150** |\n\nThe results indicate that while the overlap-based anchor view selection strategy does yield some improvements, it does not consistently outperform the FPS-based strategy. We hypothesize this is due to the added constraints imposed by incorporating rotation information, which demands more precise camera poses. Given that our dataset only provides COLMAP-estimated poses, which may not be sufficiently accurate for such fine-grained angle considerations, opting for the default camera location proves to be a simpler and more effective approach. It is worth noting that the inclusion or exclusion of view angle information does not affect our core motivation and contribution. We will further investigate the overlap-based setting with more accurate pose data in future work.\n\nTo further explore whether the Information Bottleneck (IB) principle could guide the anchor selection process, as suggested by the Reviewer 9obw, we designed an Adaptive Anchor View Selection strategy that adheres to this principle. Based on a given overlap threshold, the greedy algorithm stops when the overlap between all candidate views and the selected anchor views exceeds the threshold. This results in varying numbers of anchor views for different coverage regions, thus linking the information content of different scenes with the overlap between views, and ultimately enabling the selection of an appropriate bottleneck. The experimental results are as follows:\n\n| **Views** | **Methods**                        | **PSNR**  | **SSIM**  | **LPIPS** |\n| --------- | ---------------------------------- | --------- | --------- | --------- |\n| 36        | DepthSplat+ZPressor(Overlap-based) | 21.49     | 0.727     | 0.194     |\n| 36        | DepthSplat+ZPressor(Adaptive)      | **22.71** | **0.782** | **0.182** |\n\n---\n\n### **W3. MVSplat confidence-based pruning baseline**\n\nWe appreciate the reviewer's insightful comment and suggestion regarding the use of confidence-based pruning for addressing \"floater\" artifacts. It's important to clarify that **floaters are a known characteristic** of feed-forward 3D Gaussian Splatting (3DGS) in dense view settings, as also observed in works like DepthSplat (refer to Figure B.3. in DepthSplat for similar observations). Our experiments are consistent with this behavior.\n\nWhile naively pruning Gaussians based on opacity (confidence) might seem intuitive, it is **generally sub-optimal and does not reliably mitigate floater artifacts**. This approach can inadvertently remove important Gaussians representing scene content, leading to \"empty regions\" or holes in the reconstruction, rather than exclusively targeting floaters.\n\nTo illustrate this, we conducted experiments using confidence-based pruning as a baseline for compression (models are re-trained):\n\n| **Views** | **Methods**                                   | **PSNR**  | **SSIM**  | **LPIPS** |\n| --------- | --------------------------------------------- | --------- | --------- | --------- |\n| 24        | MVSplat                                       | 25.00     | 0.871     | 0.137     |\n| 24        | MVSplat+Confidence Pruning (prune_ratio=0.75) | 21.15     | 0.816     | 0.190     |\n| 24        | MVSplat+Confidence Pruning (prune_ratio=0.5)  | 26.94     | 0.886     | 0.130     |\n| 24        | MVSplat+ZPressor                              | **27.49** | **0.895** | **0.111** |\n\n*Note: For a fair comparison, the prune ratio for Confidence Pruning was set to 0.75 to yield a same number of Gaussians as ZPressor, and we also manually adjusted the prune ratio to 0.5 to improve performance.*\n\n1. Numerically, confidence pruning offers some marginal improvement over the MVSplat baseline. However, its effectiveness is significantly lower than that of ZPressor. Visualizing the results reveals that **confidence pruning often introduces large empty regions**, particularly at object boundaries or in areas like the sky, which typically exhibit low confidence. This leads to significantly degraded visual quality. Furthermore, selecting an optimal pruning ratio for confidence-based methods is challenging.\n2. This highlights the fundamental difference between the two approaches. Floater artifacts in feed-forward 3DGS often arise from inconsistent information across dense input views, causing the network to predict erroneous Gaussians in empty space. While pruning might remove some of these floaters, it does not address this root cause of redundancy at the prediction stage.\n3. ZPressor, in contrast, focuses on compressing the multi-view features *before* 3D Gaussian prediction. By effectively reducing redundancy and focusing on salient information early in the pipeline, ZPressor directly tackles the \"information overload\" problem. This leads to a more robust and scalable model.\n\nWe agree that exploring more sophisticated Gaussian pruning strategies post-prediction is a valuable direction for future research. We will include this ablation study and corresponding visualization results in the updated version.\n\n---\n\n### **Q1. Eq. 6: Cross-attention and Attention**\n\nWe appreciate this clarification. We will correct Equation 6 and the accompanying text to reflect this precise terminology in the updated version."}, {"review_id": "BSYQICc4M2", "reviewer": "Reviewer_cRJd", "summary": "The author tackles the view scalability problem of the modern feedforward based 3DGS method. They find that with increasing number of views,  methods like MVSplat, pixelSplat, DepthSplat struggle with reconstruction quality. The author attributes such degradation to the \"limited capacity of the encoder\". Hence, motivated by information bottleneck theory, they propose a plug-and-play model ZPressor that dramatically reduce the input features from image encoder and generate enhanced visual quality.", "questions": "- confusion definition of \"variational rendering loss\" at the end of section  3.\n- what do you mean by \"information overload\" in section 4.2? intuitively, those information from different views should be redundant, what does overload mean here and why it leads to performance degradation then?\n- since the current baseline is too weak, plz **demonstrate your module on a feedforward 3DGS methods that natively support input views more than 6**. Recent works, for example anysplat, FLARE, InstantSplat, GPSGaussian++, and any other method are acceptable. Or you can simply choose to fine-tune MVSplat/PixelSplat/DepthSplat with multi views as the baseline. Don't have to stick with single 80GB GPU constraints.", "limitations": "It relies on known cam pose for selecting anchor views. Yet today's sota work could also works on poes-free inputs (noposplat, anysplat, PF3plat). How your method could help in this scenario ?", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Good part:\n- the paper is well written and easy to follow with abundant visual results.\n- the identified problem is a true pain for sparse view feedforward 3DGS prediction and leveraging cross attention for information fusion is generally a plausible solution\n- the module as a feature digester does reduce the hardware overhead of previous methods and achieves remarkable enhancement.\n\nWeaknesses:\n- the key claim of the paper is that \"limited capacity of encoder\" as the main drawback of previous methods. Yet for all three methods they have modified are SINGLE VIEW image encoder which means that no information has been exchanged between views during the encoder stage. **That's to say, the key reason of bad performance of previous methods is not the encoder**\n- the key reason of poor performance of those methods are very simple actually: they are all trained on very sparse views typically 2-4, with depthSplat at most 6 views. Yet the evaluated views of ZPressor is 8~36, which falls far outside the distribution of training data of those methods. **That's to say: the baselines are simply too weak, and are not expected to handle the dense view scenario**.\n- The author use cross attention to fuse information from support views to anchor views, yet the ablation study of tab 3 shows very limited enhancement from anchor-fusion to support fusion. Considering the proposed method is trained with 6+6 views, **such enhancement could simply be attributed to the model's getting familiar with more views, instead of learning extra information from support views.**\n- It is not supposed to claim a method as `plug-and-play` if you integrate your module and still need to train the integrated new model from scratch.\n- IB theory is a little bit out-of-place here.", "was_revised": true, "final_justification": "Author has provided further experiment results to demonstrate their claim. My concerns are resolved.", "rebuttal": "## **Response to Reviewer cRJd**\n\nWe are thankful to Reviewer cRJd for their insightful comments. Our detailed responses to the points are as follows:\n\n---\n\n### **W1. \"Limited capacity of encoder\" definition**\n\nWe agree that our phrasing of \"limited capacity of the encoder\" might be misleading. Our intention was to refer to the entire network from image input to 3D Gaussian prediction (as mentioned in L27), which acts as a **“scene encoder”** by extracting scene-dependent features and predicting 3D Gaussians in a single forward pass.\n\nWe will clarify this point in the updated version of the paper by rephrasing \"encoder\" to \"the whole network from image to 3DGS\" to avoid confusion.\n\n---\n\n### **W2. Baselines are \"too weak\" for dense views**\n\nThanks for reviewer's insightful comments regarding the performance of our baselines and the suggestion to evaluate ZPressor on feed-forward 3DGS methods that natively support more input views. We appreciate the opportunity to clarify these points.\n\n1. We did not use the original settings or pre-trained weights of MVSplat, PixelSplat, and DepthSplat. Instead, we **re-trained** these baseline models from scratch under multi-view settings, adhering to the training strategies described in their respective papers while adjusting for multi-view inputs where applicable. This consistent training approach for both the baselines and our ZPressor-integrated models is reflected in Tables 1 and 2. This ensures that the observed improvements are due to ZPressor's bottleneck-aware compression and not merely a result of providing more input views to models in training stage.\n2. The existing baselines (pixelSplat/MVSplat/DepthSplat) are the **best options** we have in this submission. Reviewer suggested evaluating on more recent feed-forward 3DGS methods like AnySplat, FLARE, InstantSplat, and GPSGaussian++. However, there are some practical limitations: (1) AnySplat was released on a arXiv (29 May 2025) *after* NeurIPS submission deadline. (2) FLARE’s official implementation does not provide readily available NVS training code. (3) InstantSplat requires approximately 1000 iterative optimizations, which is fundamentally different from the single-forward-pass nature of the feed-forward 3DGS models we target. (4) GPSGaussian+ primarily focuses on free-viewpoint video synthesis of human characters.\n\nCovering every conceivable baseline would extend far beyond the scope of our paper and incur prohibitive resource costs. Our module's core functionality—compressing multi-view features—is not confined to any specific model, and its demonstrated benefits across the chosen baselines adequately showcase its broad applicability and contribution.\n\n---\n\n### **W3. Limited enhancement from \"anchor-fusion to support-fusion\" in Tab. 3**\n\nThe key to understanding this lies in the nature of the information contributed by the support views. The support views don't introduce entirely new macro-level information, but rather **enhancing the richness and consistency of details**, its impact on broad numerical metrics like PSNR might be less dramatic.\n\n1. When we consider a baseline of 6 anchor views, these views, selected through Farthest Point Sampling, already provide substantial spatial coverage of the scene. Therefore, they capture the primary structural and appearance information. The \"support views,\" while adding to the overall number of input views, are by definition spatially closer to existing anchor views.\n2. Their primary contribution is not to drastically improve evaluation results but rather to provide finer-grained, complementary details or to reinforce information in areas already partially observed by the anchor views. This additional information is often concentrated on subtle aspects of the scene.\n\n---\n\n### **W4. Not \"plug-and-play\" if training from scratch**\n\nWe'll clarify this nuance in the updated version and will no longer refer to our module as \"plug-and-play\" as this was an unintentional misleading.\n\nOur initial intention was for our model to be **architecture-agnostic**. While fine-tuning or training from scratch on the combined architecture is typically required to fully leverage ZPressor's benefits, the architectural integration itself is straightforward.\n\n---\n\n### **W5. IB theory \"out-of-place\"**\n\nWe believe the IB principle provides a robust theoretical foundation and a powerful guiding framework for our work. Inspired by the Information Bottleneck principle, we analyzed the information flow within the Feed-Forward 3DGS model. This led us to propose learning a compact and informative feature representation that discards redundancy while retaining sufficient original information. This aligns perfectly with the IB objective: finding the minimal sufficient statistic of the prediction network's input to predict its output.\n\n---\n\n### **Q1: Confusion definition of \"variational rendering loss\"**\n\nThe sentence in Line 202 \"we optimize the feed-forward 3DGS with ZPressor by the variational rendering loss, e.g., MSE and LPIPS\" may be misleading. Our variational loss consists of two main components: a reconstruction term (e.g., MSE and LPIPS) and a compression term (calculated as a KL divergence weighted by β). So, what we mean to say is that the variational loss **is based on** the rendering loss.\n\n---\n\n### **Q2. \"Information overload\" in Section 4.2**\n\nFor many feed-forward 3DGS models, as input views become dense, the network is overwhelmed by the massive, redundant information from highly overlapping regions, leading to an information overload. This information overload arises because the network **lacks the capacity to effectively process the massive amount of redundant information** across dense views, leading to degraded performance and memory issues. This can particularly impact generalization during inference with more views.\n\nZPressor tackles this challenge by acting as a \"bottleneck-aware\" module within the scene encoding process. It explicitly manages this multi-view information flow by compressing view features, thereby reducing redundancy and distilling essential information to overcome the \"information overload.”\n\n---\n\n### **Q3. Demonstrate module on methods supporting more input views**\n\nPlease refer to our response in W2 for more clarification.\n\n---\n\n### **Limitation. Reliance on known camera poses**\n\nWe appreciate the reviewer for highlighting this important aspect. For the works that can handle pose-free inputs, NoPoSplat is based on the MASt3R architecture and pre-trained weights, was only trained on two views, making it an unfair comparison to use as a baseline; AnySplat was released on a arXiv (29 May 2025) after NeurIPS submission deadline; PF3plat explicitly predict the poses, which is essentially no different from models with pose input. For these reasons, we did not apply ZPressor to these models at the beginning.\n\nWe also explored a pose-free ZPressor compression method. This approach leverages K-Means clustering on the features of input views to identify distinct view groups. Subsequently, an anchor view is determined for each group, enabling a pose-independent anchor view selection process.\n\nWe conducted preliminary validation on pose-free setting using DepthSplat as our baseline:\n\n| **Views** | **Methods**                   | **PSNR**  | **SSIM**  | **LPIPS** |\n| --------- | ----------------------------- | --------- | --------- | --------- |\n| 36        | DepthSplat                    | 19.23     | 0.666     | 0.286     |\n| 36        | DepthSplat+ZPressor(w/o pose) | 22.81     | 0.791     | 0.174     |\n| 36        | DepthSplat+ZPressor(w/ pose)  | **23.88** | **0.815** | **0.150** |\n\nThe experimental findings clearly indicate ZPressor's substantial performance lead over the baseline, even when foregoing camera poses, which boosts the scalability of feed-forward models."}, {"review_id": "OlpdIjjq4Z", "reviewer": "Reviewer_8gSJ", "summary": "This paper introduces ZPressor, a lightweight, architecture-agnostic module designed to enhance the scalability of feed-forward 3D Gaussian Splatting (3DGS) models, which are prominent for real-time novel view synthesis. Traditional 3DGS approaches struggle to efficiently process a large number of input views due to their limited encoder capacity, resulting in degraded performance and high memory usage as the number of views increases. The authors analyze these limitations through the lens of the Information Bottleneck (IB) principle, which emphasizes the preservation of task-relevant information while discarding redundancy. Inspired by this, ZPressor employs a strategy where input views are divided into anchor views and support views. Cross-attention mechanisms fuse the support view information into anchor views, compressing the dense multi-view data into a compact latent representation Z that retains essential scene details. By integrating ZPressor into existing feed-forward 3DGS models, they demonstrate significant improvements: models can handle over 100 dense input views at high resolution (480P) on standard GPUs without performance drops or excessive memory consumption. Their experimental validation on large-scale benchmarks (like DL3DV-10K and RealEstate-10K) shows consistent enhancements in quality and efficiency, making 3D scene reconstruction more practical for real-world applications.", "questions": "- In the experiments, how do you split input views and target novel views?\n- Do you select the same target novel views for different numbers of input views?\n- For anchor view selection, did you try other strategies? e.g., k-means clustering.", "limitations": "Limitations are mentioned in weaknesses.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "**Strengths:**\n- This paper is well-written, motivations and significance of the task are clearly described in the paper.\n- The use of the Information Bottleneck principle provides a solid theoretical foundation for addressing the limitations of existing 3DGS models, offering insights into why they struggle with dense views and how to mitigate these issues.\n- ZPressor is designed to be architecture-agnostic, allowing it to be seamlessly integrated into various existing feed-forward 3DGS models, which broadens its applicability.\n- Extensive experiments on large-scale benchmarks like DL3DV-10K and RealEstate-10K demonstrate consistent performance improvements, showing that the approach is effective both qualitatively and quantitatively.\n\n**Weaknesses:**\n- The authors acknowledge that ZPressor may be less effective when dealing with extremely dense views (e.g., thousands of views), where even the compressed representations may become computationally challenging to handle.\n- Incorporating cross-attention-based compression modules introduces extra complexity into the pipeline, which may affect training stability, implementation, or inference speed, although it is designed to be lightweight.", "was_revised": true, "final_justification": "After reading the comments from other reviewers and the rebuttal from the authors, I would like to keep my score for acceptance.", "rebuttal": "## **Response to Reviewer 8gSJ**\n\nWe sincerely thank Reviewer 8gSJ for the positive evaluation and for recognizing the strengths of our paper, including its clear motivation, theoretical foundation, versatility, and the extensive experimental validation. We are particularly pleased that reviewer found the use of the Information Bottleneck principle insightful.\n\n---\n\n### **W1. Less effective when dealing with extremely dense views**\n\nWe acknowledge that ZPressor's effectiveness is limited in extremely dense views (e.g., thousands of views), as detailed in L322-L327. Our future work may combine ZPressor with further 3D Gaussian merging or memory-efficient rendering techniques to tackle such scenarios.\n\n---\n\n### **W2. Complexity from Cross-Attention**\n\n**Inference efficiency and parameters comparison:** Below is a comparative analysis of DepthSplat before and after the integration of ZPressor, focusing on trainable parameters, inference speed, and memory consumption when processing 36 input views:\n\n| **Methods**         | **Parameters** | **Inference Time (ms)** | **Memory (GB)** |\n| ------------------- | -------------- | ----------------------- | --------------- |\n| DepthSplat          | **114M**       | 692.3                   | 17.84           |\n| DepthSplat+ZPressor | 116M           | **204.7**               | **3.59**        |\n\nAs demonstrated by our results, ZPressor introduces a mere 1.7% increase in parameters while achieving significant reductions of 70% in inference time and 80% in memory consumption. This clearly underscores ZPressor's lightweight nature and its substantial contribution to enhancing inference efficiency.\n\n**Implementation:** ZPressor is designed with an architecture-agnostic approach, allowing for seamless integration and expansion of the scalability of existing feed-forward models.\n\n**Training stability:** Our experiments confirm that ZPressor does not compromise model stability during training. In fact, by assisting the model in eliminating feature redundancy, ZPressor contributes to a more stable training process.\n\n---\n\n### **Q1. How to split input views and target novel views**\n\nFollowing the standard protocol in existing feed-forward 3DGS works (e.g., DepthSplat/MVSplat/pixelSplat), for each scene in the datasets (DL3DV-10K and RealEstate10K), we select the context views by picking frames with a fixed \"context gap\" (e.g., skipping a certain number of frames) around the target view or a central reference frame, ensuring a diverse set of inputs.\n\nThe target novel views are chosen at different, distinct camera positions (frames) that were not part of the input context views. These target views are usually selected to be spatially separated from the context views to rigorously test the model's generalization capabilities for novel view synthesis.\n\nFor evaluation, we render eight fixed target novel views, as stated in Section 4.2 of the paper.\n\n---\n\n### **Q2. Whether to select the same target novel views for different numbers of input views**\n\n**Yes**, for a fair comparison across different numbers of input views (e.g., 8, 12, 16, 24, 36 views), we use the same set of eight target novel views for evaluation. This ensures that the performance gains or drops observed are solely attributable to the changes in the input view configuration and the effectiveness of ZPressor, rather than variations in the evaluation criteria.\n\n---\n\n### **Q3. Other anchor frame selection strategy**\n\nWe experimented with **K-Means clustering** on camera positions, and the results are presented in the table below.\n\nFurthermore, we explored **feature-based anchor view selection strategies**. By clustering view features using K-Means to obtain view groups, and then calculating the anchor view, ZPressor can support pose-independent anchor selection. The experimental results for this approach are also included in the table below.\n\n| **Views** | **Methods**                           | **PSNR**  | **SSIM**  | **LPIPS** |\n| --------- | ------------------------------------- | --------- | --------- | --------- |\n| 36        | DepthSplat                            | 19.23     | 0.666     | 0.286     |\n| 36        | DepthSplat+ZPressor(w/o pose+K-Means) | 22.81     | 0.791     | 0.174     |\n| 36        | DepthSplat+ZPressor(w/ pose+K-Means)  | 22.84     | 0.789     | 0.175     |\n| 36        | DepthSplat+ZPressor                   | **23.88** | **0.815** | **0.150** |"}, {"review_id": "judVaxVGGg", "reviewer": "Reviewer_qeXk", "summary": "The paper revisits feed-forward 3DGS prediction from an information bottleneck perspective and introduces ZPressor, a plug-and-play module that can improve the model performance and reduce inference/training memory. The method includes group-based compression with selected anchor views. The authors verified the proposed ZPressor on multiple base models and datasets.", "questions": "In general, I believe this is an interesting paper, which improving the feed-forward 3DGS reconstruction by revisiting the information bottleneck theory. However, based on the current performance patterns, biased baseline choice, and the unclear claims, I will give a boarderline reject. I encourage the authors to include more analysis regarding my questions listed in the weakness section. And I would be happy to increase my score if it is reasonable to me.", "limitations": "Please see the sections above.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The paper revisits feed-forward 3DGS reconstruction from an information bottleneck perspective, which recalls me of the discussion why do we need inductive bias for 3D. And ZPressor is a kind of inductive bias of compression. This makes a lot of sense to me, as the essence of 3D reconstruction is just compression (of the repetitive and redundant information).\n- Based the results shown in the paper, the proposed ZPressor is effective and efficient, which improves performance and reduces computation at the same time.\n- The authors verified the effectiveness of ZPressor on multiple base model and datasets.\n\n\nWeaknesses:\n- However, at the same time, I do have some concerns about the claims and performance patterns of the baseline models of the paper. And I would like to get more feedback from the authors. Please see details below.\n- First, the performance in Table 1 and Table 2 is weird to me. The results show that **with more input views, the performance drop**. The authors have also mentioned it multiple times in the writing, e.g., Line 4 and Line 31. However, this is counter-intuitive. Personally, I have never observed any performance pattern like this in my own experiments. And this is not the performance pattern of every paper that I read. For example, in Long-LRM [1], MegaSynth [2], the performance improved with more input views. I encourage the authors to explain a bit more on this.\n- Second, my conjecture is that, the weird performance pattern is because of a biased base model choice. Concretely, the base models, pixelSplat, DepthSplat and MVSplat, are based on pairwise computation, e.g., cost volume calculation; while the two paper I mentioned above adopts global attentions on all views, which has less inductive bias in model architecture design. It would be beneficial to explore whether this assumption is true by adapting ZPressor on models with global attention.\n- Third, in Table 2 and 3, after adding the ZPressor module, the performance still drop with more input images. Thus, this experimental result cannot support the claim of the author on solving the ``degraded performance'' problem.\n- Forth, the authors mentioned *limited capacity of the encoder* multiple times. However, I am not able to understand this claim. What exact ``capacity'' the authors were referring to here?\n- Finally, the visualization examples in the supplementary videos is in very low quality to me. In general, it is weird that the performance is so bad and the renderings are so blurry.\n\n[1] Chen, Ziwen et al. “Long-LRM: Long-sequence Large Reconstruction Model for Wide-coverage Gaussian Splats.” ArXiv 2024. \n[2] Jiang, Hanwen et al. “MegaSynth: Scaling Up 3D Scene Reconstruction with Synthesized Data.” CVPR 2025.", "was_revised": true, "final_justification": "Thanks for the authors for the rebuttal! Previously I misunderstood the problem setting and thought it leads to worse performance by training on more images, and the authors clarified that it was using more input images during test time.\nThen I think this makes a lot more sense and I will increase my score. At the same time, I would still recommend training models with global attentions to further verify the claim.", "rebuttal": "## **Response to Reviewer qeXk**\n\nWe sincerely thank Reviewer qeXk for the thoughtful review and valuable feedback. We have carefully considered all points raised and provide our responses in detail below:\n\n---\n\n### **W1. Performance drop with more input views in Tables 1 and 2**\n\n1. The numerical values presented in the paper for models such as Long-LRM and MegaSynth mentioned by the reviewer are **based on fine-tuning (e.g., Long-LRM Section 4.2)/re-training (e.g., MegaSynth Section 6.5) for each test input**. However, during training, we used 12 input views as input, and during testing, we directly applied inputs with more than 12 input views to our models **for generalization**. While the model performance may slightly decrease, it is still significantly better than the substantial decline observed in the baseline models.\n2. Furthermore, these baseline models exhibit redundancy under multi-view input conditions, which inherently leads to performance degradation when the number of views exceeds the model's inherent capabilities. Consistent with the results in Table 1 of PixelGaussian [1], we also observed test degradation in feed-forward models when increasing the number of input views during the testing phase.\n\n---\n\n### **W2. Need for models with global attention**\n\nWe thank the reviewer for this insightful comment. We agree that our selected based models (pixelSplat, DepthSplat, MVSplat) rely on pairwise computations, which carry a different inductive bias compared to the global attention mechanisms in recent works like Long-LRM and MegaSynth. We are keen to test ZPressor on global attention models. However, we were unable to do so as Long-LRM lacks an official implementation and we failed to reproduce results from the community version. Similarly, MegaSynth has not yet released its model implementation. \n\nWe agree that global attention is a promising direction for dense-view reconstruction, which motivates the design of Long-LRM and MegaSynth. Nevertheless, our primary contribution is to enhance existing sparse-view reconstruction methods for dense-view scenarios (L63), and it was not our intention to imply that our module would yield similar improvements for models already architected for dense-view reconstruction. We will clarify this in the updated version and explore global attention-based architectures in future work.\n\n---\n\n### **W3. Performance still drops with ZPressor for more views**\n\nAs addressed in W1, we didn't finetune for every input view, so a reasonable performance drop is observed.\n\nWe would like to clarify that we do not claim to \"solve\" the \"degraded performance\" problem. Instead, our work focuses on **mitigating** this issue. Our approach does not offer a perfect solution for generalization (beyond fine-tuning) to extremely dense views in feed-forward scene reconstruction. We are indeed the pioneering work in dealing with this important problem. And we will clarify this point in the updated version of our paper.\n\n---\n\n### **W4. \"Limited capacity of the encoder\" unclear**\n\nThe **capacity** of an image-to-3DGS network refers to its ability to effectively process redundant information across dense views without leading to degraded performance or memory issues.\n\nThe phrasing \"limited encoder capacity\" in our discussion may be misleading. Our intention was to refer to the network from image input to 3D Gaussian prediction.To prevent misinterpretation, we will address this ambiguity in an updated version of our paper by rephrasing \"encoder\" as \"the whole network from image to 3DGS\".\n\n---\n\n### **W5. Low quality of visualization examples**\n\n1. Due to GPU resource constraints, we adopted the configurations of DepthSplat, MVSplat, and pixelSplat. This resulted in 480P visualization, which can make the rendering appear blurry. Figure 3 and the supplementary videos in our paper used the same rendering method, so perhaps scaling the video playback window to an appropriate size could achieve a suitable playback effect.\n2. The baseline models perform poorly with 36-view inputs, but that adding ZPressor significantly improves their performance.\n3. We are finetuning the ZPressor-integrated models on 960P datasets and will include the results in a updated version.\n\n---\n\n### Reference\n\n[1] Xin Fei et al. “PixelGaussian: Generalizable 3D Gaussian Reconstruction from Arbitrary Views.” Arxiv 2024."}]

Author rebuttal:

Meta-review: This paper introduces ZPressor, an architecture-agnostic module designed to improve the scalability of feed-forward 3D Gaussian Splatting (3DGS) models in dense-view settings. By employing a cross-attention–based compression mechanism inspired by the Information Bottleneck (IB) principle, ZPressor enables models to process more input views without excessive memory usage or performance degradation.

Reviewers agree that ZPressor addresses a relevant and challenging problem, demonstrates strong empirical effectiveness, and represents a meaningful contribution to dense-view 3DGS. Concerns about the rigor of the IB theory and certain experimental analyses are noted, but do not undermine the core technical contribution. Rebuttals clarified misunderstandings and provided additional context, increasing reviewers’ confidence in the paper.

Therefore, the AC recommends acceptance and suggests the authors: 1. Clarify the role and positioning of IB theory in the final version.
2. Enhance visualizations and explanations of technical claims, such as encoder capacity and dense-view behavior.
Decision: Accept (poster)