ReCon-GS: Continuum-Preserved Gaussian Streaming for Fast and Compact Reconstruction of Dynamic Scenes
Authors: Jiaye Fu
Qiankun Gao
Chengxiang Wen
Yanmin Wu
Siwei Ma
Jiaqi Zhang
Jian Zhang
Venue: NeurIPS 2025 poster ()
Keywords: 3D Gaussian Splatting
Streaming Free-viewpoint Video Reconstruction
Novel View Synthesis
Primary area: applications

Abstract: To address these challenges, we propose the Reconfigurable Continuum Gaussian Stream, dubbed ReCon-GS, a novel storage-aware framework that enables high-fidelity online dynamic scene reconstruction and real-time rendering. Specifically, we dynamically allocate multi-level Anchor Gaussians in a density-adaptive fashion to capture inter-frame geometric deformations, thereby decomposing scene motion into compact coarse-to-fine representations. Then, we design a dynamic hierarchy reconfiguration strategy that preserves localized motion expressiveness through on-demand anchor re-hierarchization,  while ensuring temporal consistency through intra-hierarchical deformation inheritance that confines transformation priors to their respective hierarchy levels. Furthermore, we introduce a storage-aware optimization mechanism that flexibly adjusts the density of Anchor Gaussians at different hierarchy levels, enabling a controllable trade-off between reconstruction fidelity and memory usage. Extensive experiments on three widely used datasets demonstrate that, compared to state‐of‐the‐art methods, ReCon-GS improves training efficiency by approximately 15% and achieves superior FVV synthesis quality with enhanced robustness and stability. Moreover, at equivalent rendering quality, ReCon-GS slashes memory requirements by over 50% compared to leading state‑of‑the‑art methods. Code is avaliable at: https://github.com/jyfu-vcl/ReCon-GS/.

ReCon-GS: Continuun-Preservee Gaussian Streaming for Fast and Compact Reconstruction of Dynamic Scenes
Jiaye Ful.2, Qiankun Gao', Chengxiang Wen', Yanmin Wu',
Siwei Jiaqi Zhang Jian Zhangl.3+ School of Electronic and Computer Engineering, Peking University
National Engineering Research Center of Visual Technology, Peking University
*Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Peking University
HiCoM
Suogge (
PeNti
SeDrv
32.0
OUESSA
SutD ISE
31.
ICV7924
Storage
Figure I: The proposed ReCon-GS framework for streamable dynamic scene reconstruction achieves superior rendering qualty with substantially reduced storage. The left figures show the high-quality rendering results ReCon-GS in a streaming fashion. The right figure is the performance comparison with previous SOTA 2 3
5 6), where the radius of circle corresponds to the rendering speed.
Abstract
Online free-viewpoint video (FVV) reconstruction is challenged by slow per-frame optimization, inconsistent motion estimation, and unsustainable storage demands. To address these challenges, we propose the Reconfigurable Continuum Gaussian Stream, dubbed ReCon-GS, novel storage-aware framework that enables high- fidelity online dynamic scene reconstruction and real-time rendering. Specifically, we dynamically allocate multi-level Anchor Gaussians density-adaptive fash- ion to capture inter-frame geometric deformations, thereby decomposing scene motion into compact coarse-to- caase-to-fine to-fine representations. Then, we design a dynamic hierarchy reconfiguration strategy that preserves localized motion expressiveness through on-demand anchor re-hierarchization. while ensuring temporal consis- tency through hara-hhirarchical deformation inheritance that confines transfor- mation priors to their respective hierarchy levels. Furthermore, we introduce a storage-aware optimization mechanism that flexibly adjusts the density of An- chor Gaussians at different hierarchy levels, enabling controllable trade-off between reconstruction fidelity and memory usage. Extensive experiments three widely used datasets demonstrate that, compared to state-of-the-art meth- ods, ReCon-GS improves training efficiency by approximately 15% and achieves superior FVV synthesis quality with enhanced robustness and stability. More- over, equivalent rendering quality, ReCon-GS slashes memory requirements by over 50% compared to leading statc-of-the-arr methods. Code is avaliable at: GS/
"corresponding to: shangian.ss@@puu.cuu.c jazhang@pku.cduu.co
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Free-viewpoint Video (FVV) reconstruction has emerged as a cornersoone of next next-generation immer- sive media systems, driven by the escalating demands of XR, AR, and VR applications. While this technology enables dynamic scene exploration from arbitrary viewpoints, its inherent spatiotemporal complexity poses persistent challenges in balancing real-time performance with reconstruction ac- curacy. Offline approaches [ g 91, though capable of high-quality reconstruction, fail to meet the low-latency requirements of real-time applications such streaming media and immersive live broadcasting. To address this limitation, recent methods (1O 6 adopt streaming reconstruction frameworks that progressively model inter-frame deformation for frame-by-frame reconstruction.
Despite these advancements, critical bottlenecks persist in achieving practical real-time FVV re- construction. First, current streaming 3D Gaussian splatting methods incur redundant storage due to deformation fields, primarily attributed to the fundamental incompatibility between the uniform spatial motion encoding strategy of Gaussian primitives and the inherent hierarchical characteristics of physical motion. This mismatch leads to inaccurate and inefficient deformation modeling for dominant macroscopic motions, triggering redundant deformation parameter storage and severe error accumulation issues. While some approaches [TT [2] mitigate error accumulation in streaming motion modeling by introducing auxiliary mechanisms (e.g., optical flow estimation networks), such solutions inevitably increase training costs and significantly compromise practical applicability. Fur- thermore, Industrial deployment necessitates dynamically balancing storage efficiency and fidelity to accommodate fluctuating resources. However, existing frameworks employ static strategies, thereby lacking the flexibility necessary for practical deployment.
To address the aforementioned challenges, we propose the Reconfigurable Continuum Gaussian Stream, referred to as ReCon-GS, novel streaming framework that jointly optimizes rendering fidelity and storage adaptability. Our method introduces Adaptive Hierarchical Motion Representation, an anchor- driven multi-scale motion encoding paradigm that dynamically allocates Anchor Gaussians through grid-based farthest point sampling to decompose scene dynamics into coarse-to-fine motion structures. This paradigm ensures alignment between Anchor Gaussian distributions and spatial geometric features. Deformation parameters are propagated to clustered General Gaussians via hierarchical mechanisms, enabling effective multi-scale rigid motion representation with minimal storage requirements. Then, ReCon-GS implements a Dynamic Hierarchy Reconfiguration strategy resolve the degradation of local motion representation caused by Anchor Gaussian drift during spatial deformation. Specifically, within each hierarchy level, we perform trajectory matching of Anchor Gaussians across frames to track their movement throughout the sequence, while constraining deformation field propagation to operate exclusively within these matched trajectories. Reorganized anchors inherit deformation priors only from their original predecessors at the same hierarchy level, thereby preserving both motion inheritance granularity and temporal motion coherence. Consequently, leveraging these architectural innovations, we reformulate 4D reconstruction as a storage-aware fidelity optimization problem, achieving paradigm-level advancements analogous to rate-distortion optimization theory. This enables dynamic balance between memory efficiency and rendering quality according to application demands without compromising real-time performance, as framework adaptively adjusts the density Anchor Gaussians at different hierarchy levels based on scene complexity.
Extensive experiments validate our framework's efficacy across diverse scenarios, demonstrating that ReCon-GS achieves compact yet high-fidelig reconstruction under varying motion patterns while outperforming state-of-the-ar streaming methods three well-known datasets. Our key contributions are summarized as follows:
We propose Adaptive Hierarchical Motion Representation, anchor-driven multi-scale that adaptively align Anchor Gaussians with scene geometry to
motion encoding paradigm decompose scene dynamics into coarse-to-fine structures, achieving efficient yet compact motion representation.
We design a Dynamic Hierarchy Reconfiguration strategy to address anchor drift-induced motion degradation, combining re- ee-herarccization with intra-level deformation inheritance to propagate priors exclusively within original hicrarchy layers, eliminating cross-layer interference while preserving motion fidelity.
Comprehensive experiments across multiple datasets validate ReCon-GS's superiority over ofte-oofthheart streaming methods. Our framework achieves superior performance with 15%

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faster training convergence while reducing storage requirements by over 50% at equivalent rendering quality. Even under constrained storage budgets, ReCon-GS maintains significant rendering quality improvements, demonstrating robust practical efficacy.
Related Work
2.1 3D
Gaussian Splatting for Static Scenes
With the rise of novel view synthesis (NVS) tasks, 3D Gaussian Splatting (3DGS) [33 has gradually replaced Neural Radiance Fields (NeRF)-based methods 04 D5
17 18 the mainstream approach for static scene reconstruction due to its high rendering quality and fast training speed. However, its massive storage requirements significantly hinder the practical application of 3DGS. Con- sequently, numerous studies have focused on achieving more compact 3DGS representations while maintaining high-quality rendering. Some methods [19 20 21 concentrate pruning Gaussian primitives, such as removing unimportant Gaussians through trainable mask mechanisms [2 or gra- dient thresholding approaches (2 Additionally, other approaches (24) aim to reduce the parameters
16
of 3D Gaussians to decrease per-primitivy storage. Meanwhile, 3DGS methods [9 [22] 24 25 26]
based on Vector Quantization (VQ) principles have also proven effective in reducing storage demands.
Alternative strategies 127 28 29] explore compact spatial structure representations to minimize 3DGS storage. Scaffold-GS 30 achieves efficient storage reduction by clustering Gaussians anchor points and implicitly representing Gaussian attributes for each cluster through MLPs. Subsequent works [3 32 33 further compress 3DGS by incorporating spatial structure priors into entropy encoders and other compression techniques.
2.2 3D Gaussian Splatting of Dynamic Scenes
3D dynamic scene reconstruction stands as one of the most challenging tasks in computer vision and graphics. With the risc of 3D Gaussian Splatting (3DGS), dynamic reconstruction approaches have diverged into two main paradigms. The first involves offline methods [7] I 8 34 35] that implicitly model scene dynamics through canonical Gaussian fields coupled with spatiotemporal deformation fields. While these methods achieve compact representations, they struggle with efficient motion parameterization. Recent ad advancements address this by embedding Gaussians into sparse motion representations using anchor points [36]
37 38 or control points 33 40 41,, enabling
motion propagation through sparse keyframes while preserving reconstruction quality. The second paradigm adopts streaming frameworks that first reconstruct a high-quality 3DGS representation for
[0]. However, the massive storage overhead of per-frame motion parameters poses significant challenges. Gao et al. [3] mitigate this through hicrarchical motion representation framework, decomposing deformations into coarse-to-fine fine transformations to reduce parameter dimensionality. Despite this, streaming frameworks inherently suffer from error accumulation due incremental updates. To address this, leverage pre-trained motion estimation models (e.g., optical flow networks) to guide 3D Gaussian deformations via 2D motion priors, enhancing both rendering quality and temporal consistency. These innovations highlight the critical balance between storage efficiency, computational tractability, and reconstruction fidelity in dynamic 3D modeling.
the initial frame and then model subsequent frames via deformation fields 42 6
some methods [3] 44 11 12 45
3 Preliminaries
3DGS models scenes using anisotropic 3D Gaussians defined by mean μ and a covariance matrix Σ RSS TR
where S diag ($z, $y) $z) E R represents axis-aligned scaling, and R SO (3) is parameterized by quaternion q. Each Gaussian's 's view-dependent color uses spherical harmonics SH and opacity a E 1].
The mathematical formulation of Gaussian is:
((x) = e (x- T X-'
(1)
For novel view synthesis, Gaussians are projected onto the imaging plane using view transformation matrix W. The projected 2D covariance E' is computed through the Jacobian J of the projective transformation:
E' = JWEW
(2)
3

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(3) Framework ReCon-GS
Prozen
Anchor Gaussian
Rigid Cluster
Init. Training with AWGN
Training
General Gaussian
Explicit Composition
Image Rendering
View-based Densification
Init/ Re-Hierarchization
Densi
Deformed Gamesian
(b) Explicit Mation Composition
(c) Re-ffierarchization
EAp
zág
Deforming
Base Caussian
Deformed Causeian
Layer 1
Layer
Layer
Intra-bierarchical
Adg
Aq
Aps AAg
Matched Verte
Meun
Bessdos
Figure 2: Illustration of our ReCon-GS framework. (a) We begin by generating high-quality base 3D Gaussian Splatting (3DGS) representation for the first frame, then embed it into an adaptively hierarchical motion presentation framework via grid-based farthest-point sampling.
(b) Explicit Motion Composition updates the base 3DGS across successive frames. (c) and (d) Periodically, a Re- Hierarchization stage accommodates complex object motion while preserving temporal consistency through hierahhiraaccccall Deformation Inheritance. Finally, a view-based densification further refines the 3DGS for high-quality rendering.
Rendering proceeds by alpha-blending N depth-ordered Gaussians at each pixel, with the final color calculated as:
C= c,a, 1-1 iEN
ay))
(3)
J=1
where c; and a; correspond the color and blending weight of the 1th Gaussian, respectively.
The learning pipeline iteratively alternates between gradient-based parameter updates and topological adaptation. Parameter optimization supervised by the loss and D-SSIM term:
C (1 X)Li + ALD.SSM:
(4)
where À is typically set to 0.2.
4 Methodology
4.1
Overview
Our goal is to achieve high-quality dynamic scene reconstruction from multi-view videos under vary- ing storage constraints. To accomplish this, our framework first generates compact Base Gaussians GB through an additive noise-injected 3DGS training strategy 3 during initial frame reconstruction. Subsequently, efficiently parameterize per-frame Gaussian deformations, we propose an adaptive hierarchical motion representation framework that explicitly models scene dynamics through scale- aware motion decomposition. Specifically, each Gaussian's motion parameterized a composition of layer-wise rigid transformations across multiple geometric scales, with each deformation layer governed by a distinct set of Anchor Gaussians GA spatially distributed via grid-based farthest-point sampling, General Gaussians within each layer hicrarchically inherit blended motion parameters from

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their nearest anchor counterparts through cross-scale deformation coefficients, enabling progressive motion aggregation from coarse fine hierarchies.
To enhance the adaptability of our anchor-based motion representation for scenes with varying motion magnitudes, introduce dynamic hierarchy reconfiguration self-optimizing mechanism that redistributes Anchor Gaussians to match evolving scene geometry while maintaining temporal motion consistency through Intra-hierarchical Deformation Inheritance. Finally, further refine local details, ReCon-GS optimizes 3DGS representation through a view-adaptive densification process, enhancing scene rendering fidelity. The complete pipeline is illustrated in Figure (a).
4.2 Adaptively Hierarchical Motion Presentation
Inspired by the hierarchical decomposition of real-world object motion into multi-scale rigid-body components, we propose an adaptive hierarchical motion representation framework. Our framework begins with the Base Gaussians GB. which is the static 3D Gaussians reconstructed from the first frame of multi-view inputs. Building upon 9B. we establish an adaptive hierarchical motion representation through three core stages:
Anchor Gaussian Initialization. Given GB {g with position μu, E R we first compute the scene's spatial bounds as JAmin = min; 14 and Jmaa max; f4x GB is then divided into a uniform 3D grid with resolution M N1/3 anchor where Nencher is the number of anchor. For each cell Ci we select the Anchor Gaussians Ia GA with position μa closest to its geometric center:
ga arg min || C..,Mll,, where Ci.j, = Mmin + gEC
(i
2 (1 k+
(5)
M "
with A Mmar Mmin. This grid-based farthest point sampling ensures each anchor locally represents its cell while maintaining global uniformity. Compared to conventional farthest point sampling approaches, grid-based strategy achieves significant computational acceleration through structured sampling constraints, effectively reducing the time complexity while preserving sampling quality.
Hierarchical Rigid-Cluster Formation. With GΛ initialized, each General Gaussians gn. E GN assigned to its nearest anchor via L1 -diittaee minimization:
9- = arg min I|p, -Mall1-
ga E.E
forming primary rigid clusters (Ra). To address residual motions within rigid clusters, we recursively subdivide the hierarchy into two finer levels within the original spatial coordinate system. At each subsequent level, we increase the density of Anchor Gaussians by adapting the grid resolution M- = [[Nanckor 1/-1)1/37,, where I 2, denotes the hierarchy level.
Explicit Motion Composition. As shown in Figure (b). each hicrarchy level (1,2,3) associates clusters with rigid transformations T (A,r () where Ap R and Aq R² denote incremental translation and rotation of Anchor Gaussians GA. Thus, Anchor Gaussians GA act as rigid motion keypoints that govern the affine transformation of all General Gaussians Gy within their associated cluster. By propagating these parameters hierarchically. the motion of each Anchor dictates the collective deformation of its rigid sub-region, emulating real-world rigid-body dynamics. The total deformation of Gaussian is the cumulative sum across levels:
AH₁ Σ
Aq Σ A (
Upper level model global rigid motions shared across clusters, while the finest level resolves residual deformations unique to individual Gaussians. Crucially, we decouple geometric attributes (optimized via Apt, and Aqr)) from static appearance parameters (sphere harmonic coefficient SH and opacity a ). freezing the latter to avoid parameter explosion from separately modeling appearance deformations, which is critical for ReCon-GS's storage efficiency. Since pixel intensities rely on a-blended Gaussian contributions, precise geometric deformation alone ensures photorealistic rendering. By optimizing geometry while freezing appearance attributes, ReCon-GS achieves parameter efficiency without sacrificing visual quality.
5

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Motion Parameter Preservation. ReCon-GS can store motion parameters at each level more compactly compared previous streaming reconstruction method 3 This primarily due to farthest the use of a carefully designed farthes point sumpling algorithm for anchor Gaussian assignment, instead of uniform anchor sampling strategy. As a result, ReCon-GS can store motion parameters for Anchor Gaussians based solely on their relative positions, without the need for additional indices. This significantly reduces the memory required for storing motion parameters.
4.3 Dynamic Hierarchy Reconfiguration
Using Adaptive Hierarchical Motion Representation framework, we efficiently allocates explicit motion parameters according scene density and geometric structures. However, ReCon-GS also inherits a critical challenge common to streaming reconstruction methods [ persistent scene motion induces severe deformations in local rigid clusters, causing Anchor Gaussians to gradually lose their representativeness as rigid motion bases. This degradation propagates errors through the motion hierarchy, accumulating artifacts in rendered sequences. To address this, propose Dynamic Hierarchy Reconfiguration strategy-a self-correcting mechanism that periodically reinitializes the hierarchical structure while preserving temporal motion coherence through intra-hierarchical deformation inheritance.
Periodic Re-Hierarchization At each reconfiguration step = KT (k E N the framework regenerate Anchor Gaussians 9A using grid-based farthest point sampling on the current Gaussian distribution, ensuring updated anchors align with the latest scene geometry. Then, General Gaus- sian O is reassigned to new anchors via L.1 disttttaeee minimization. The process is described in
Figure 2 (c).
Intra- hierarchical Deformation Inheritance.As present in Figure 2 (d), after re-hierarchization, we implement intra- hirrahhhracccccal deformation inheritance to enforce Temporal motion consistency between G across hierarchy levels. For each hierarchy level E 3), each new Anchor Gaussian inherits deformation parameters from its three nearest legacy Anchors to ensure temporal continuity. Specifically. The translational deformation is governed by:
3
Ap'a (
1
Apas:
3
where Ap", (1) denotes the translational deformation parameter of new Anchor Gaussian at level 1, (1) and App. represents the translational deformation parameter of the i-th matched Anchor Gaussian. Rotational deformation follows an eigenvector- based formulation:
Aq (1)
Umax (m)
M
Σ Aqu !!!
Umax (M)l
where Ag', (4) is the rotational deformation parameter of new Anchor Gaussian, and Aqu represents the rotational deformation parameter of the i-th matched Anchor Gaussian. Uman (M) denotes the cigenvector corresponding to the largest cigenvalue of M.
4.4 Storage-aware Optimization
Thanks to the streaming architecture of ReCon GS, the storage overhead of Gaussian primitives constitutes only minor fraction of the total storage consumption, with the majority allocated to per-frame deformation field parameters. By adopting an anchor-driven adaptive hierarchical motion representation, ReCon-GS operates as a storage-aware framework-the hierarchical anchor
resolution (!-( ) dynamically adapts to varying scene complexity and storage constraints. Moreover, ReCon-GS adopts a decoupled two-stage optimization pipeline to balance motion fidelity and computational efficiency. Phase 1, we exclusively train the hierarchical deformation fields while freezing the geometric and appearance attributes of the Base Gaussians GR. Once the deformation fields converge, Phase 2 activates the view-based densification address persistent discrepancies between learned motion and scene dynamics.
anchor

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Table Quantitative comparison on N3DV dataset. The storage metric includes the size without and with the initial frame, separated by "/". The training time metric includes the first frame training. The method with is reproduced by us through official code in the same experimental environment.
Category
Method
PSNR (dB)t SSIM1 LPIPSL Storage (MB) Train (sec) Render (FPS)
4DGS1
31.36
0.950
0.131
0.3
7.8
30
STG
32.05
0.948
0.67
140
Offlinc
SaRO-GS
5
32.15
1.0
40
Swift4D
46
32.23
0.4
5.0
125
SplineGS
47
32.60
11
76
Dynamic 3DGS
42
30.67
-/9.2
560
StreamRF
48
30.68
0.930
17.7/31.5
12
3DGStream
0
31.35
0.948
0.130
7.6/7.8
8.1
245
Online
4DGC
2
31.58
0.943
-/0.5
50
168
QUEEN-I
32.19
0.946
0.136
-/0.75
7.9
248
HiCoM
3
32.08
0.953
0.130
0,48/0.69
6.6
255
ReCon-GS (ours)
32.66
0.957
0.123
0.40/0.44
6.4
250
Table Quantitative comparison on Meet Room and PanopticSports datasets. The storage metric includes the size without and with the initial frame, separated by "/" The training time metric includes the first frame training. The method with f reproduced by us through official code in the same experimental environment. The method marked with adopts the Discussion scene from the
Meet Room dataset as the training set, with the remaining scenes serving as the test set.
Meet Room
PanopticSporrts
Method
PSNR (dB)t
LPIPS
Storage (MB)
Train (sec)
Render (FPS)
PSNR
LPIPS
Storage (MB)
Train Render (FPS)
(dB)t
(sec)
3DGStream
6
29.30
0.188
4.0/4.1
4.77
260
23.02
0.187
7.9/8.1
5.87
369
IGS-1"
30.13
-/1.26
2.67
252
HiCoM 3
29.57
0.182
0.30/0.39
3.91
236
29.17
0.142
1.33/2.11
8.60
358
ReCon-GS (ours)
30.84
0.163
0.28/0.30
3.86
256
29.33
0.136
0.64/0.8
7.14
410
Experiments
5.1 Dataset
The Nerual 3D Video Dataset (N3DV) [5]] comprises 6 indoor dynamic scenes. Each scene contains 18 to 21 free-viewpoint videos, with each video spanning 300 frames captured at 30 FPS and resolution of 2704 2028.
Meeting Room Dataset [8 comprises 3 dynamic scenes across diverse real-world scenarios. Each scene contains 13 free-viewpoint videos, with each video spanning 300 frames captured at 30 FPS and a resolution of 1280 x 720.
PanopticSports Dataset (2), derived from sports sequences in the Panoptic Studio dataset (52) includes sports-oriented scenarios. Each scenario consists of free-viewpoint videos, each containing 150 frames recorded at 30 FPS and resolution of 640 x 360.
Technicolor Dataset [53] consists of video recordings captured by a x camera rigs. Each video has spatial resolution of 2048 x 1088 and frame rate of 30 FPS. Following the Ex4DGS [34), evaluation performed on five distinct scenes (Birthday, Fabien, Painter, Theater, and Trains) using their original full resolution.
5.2 Implementation Details
Our framework initializes first-frame reconstruction using 3DGS 13 with the maximum degree of spherical harmonics (SH) to and a noise-injected coefficient Anoise 0.01, training on three datascts for 10,000 to 15,000 steps. During the construction of hierarchical motion representation,
7

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Table Quantitative comparison on Technicolor dataset. The storage metric includes the size with
the initial frame. The training time metric includes the first frame training.
Category
Method
PSNR (dB)t
SSIM
Storage (MB)
Render (FPS)t
NeRF-based
HyperReel
49
31,80
0.906
1.20
STG 9
33.60
1.10
87
Offline
Ex4DGS
33.62
0.916
2.81
E-D3DGS 50
33.24
0.907
1.54
Online
ReCon-GS (ours)
33.83
0.932
0.82
207
Slope: 1
Slope: -..6 10"-4"
w/h W/yy/
PSNR (dB)
PSNR
29.0
29.5
28.5
29.0
Slope: -3.3 x 10
28.0
Oure
Slope: -,.7 10
28.5
Gurs IiCaM
27.3
100
150
200
250
300
28.0
50
100
150
200
250
300
Frame Index
Frame Index
(a) Coffee Martini
(b) Flame Salmon
Figure The PSNR Trend Comparison between Ours and HiCoM across different scenarios.
set the finest-level Anchor Gaussians 1/24 of the total Gaussian primitive count, with each subsequent layer containing one-third the anchors of its predecessor.
For per-frame optimization, all datasets undergo initial 100 steps for deformation training. Sub- sequently, N3DV and Meet Room datasets receive an additional 100 refinement steps for Gaussian densification and pruning, while PanopticSports employs 400 steps address its higher motion complexity. All experiments are conducted on an NVIDÍA RTX 4090 GPU, with results averaged over independent runs ensure statistical reliability. Extended implementation details provided in the Appendix A
5.3 Experiment Results
Quantitive comparisons. We conducted comprehensively evaluation of our method against existing state-of-ther oftthh-attttttte (SOTA) online and offline approaches across three datasets using multiple quantitative metrics, including PSNR, SSIM, LPIPS, storage consumption, training time, and rendering speed. To ensure statistical validity, storage and rendering speed measurements were caleulated averaged values across all video frames. As demonstrated achieves SOTA performance in rendering quality and training efficiency compared to online baselines. Specifically, our method outperforms the recent SOTA streaming methods in rendering quality by than 0.5dB the N3DV 551, MeetRoom 48 and Technicolor [53] datasets. This improvement underscores the effectiveness of our approach in achieving superior visual fidelity in dynamic scene reconstructions. In the case of the PanopticSports [42] dataset, our method achieves the best rendering quality while inducing substantial reduction in storage requirements, decreasing storage by approximately 60% compared previous streaming methods. In comparison to offline methods, ReCon-GS requires slightly more storage, primarily due to the inherent storage advantages of offline methods in implicit motion representation.
Table D. Table 2 and Table B] our method
Furthermore, as evidenced in Figure ReCon-GS effectively alleviates temporal performance degradation observed in recent SOTA method. Through the Dynamic Hierarchy Reconfiguration Strategy, ReCon-GS successfully circumvents the degradation motion representation capability of Anchor Gaussians. More quantitive evaluations provided in the Appendix C.I
Qualitative comparisons. While our ReCon-Gs framework primarily focuses on optimizing the efficiency of dynamic scene reconstruction, Table and Table substantiate the superiority of our

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119th
249th
60th
207th
3DGet
HiCoM
Ours
GT
Figure 4: Qualitative results on the coffee martini scene in the N3DV Dataset. To ensure a fair comparison, we retrained their official code with the same initial sparse points.
PSN dil
20,98
PSNi 2150dl
Stanage
Strrae: 0.30N13
Shongg
PSNR 3388
GT
Level =2
Level =3
Level =4
Figure 5: Qualitative results of our ReCon-GS under different hierarchical levels
method in rendering quality. As illustrated in Figure compared to HiCoM 3 and 3DGStream 6 our approach achieves more complete and temporally consistent reconstruction of dynamic scene details across multiple datasets. Moreover, the proposed method demonstrates more enhanced dynamic reconstruction capabilities when handling prolonged temporal intervals. We have also provided an additional subjective quality evaluations in Appendix C.3
5.4 Ablation Study
As evidenced in Table 4 we conducted ablation studies on key strategies of ReCon-GS, including Adaptively Hierarchical Motion Presentation paradigm, Dynamic Hierarchy Reconfiguration strategy and view-based densification process. The experimental results demonstrate that the Adaptively Hierarchical Motion Presentation paradigm, despite introducing some memory overhead, significantly enhances the performance of ReCon-GS, proving the paradigm's 's effective motion representation

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Table Ablation on key components of our ReCon-GS framework.
Method
|PSNR (dB)t SSIM f Storage (MB), Train (sec).,
Render (FPS)
w/o Hicrarchical Motion Representation 31.43
0,9525
0.32
6.37
260
w/o Dynamic Hierarchy Reconfiguration
32.00
0.9521
0,45
6.45
248
w/o View-based Densification
32.15
0,9559
0.40
6.52
287
Ours (full)
32.66
0.9571
0.44
6.44
250
Table 5: Ablation on hierarchy depth of our ReCon-GS framework.
Hicrarchy Depth
N3DV
Meet Room
PSNR (dB)
Storage (MB) 4
Train (sec)
PSNR (dB)
Storage (MB) +
Train (sec)
32.422
0.40
6,44
30.20
0.28
3.87
3
32.662
0.44
6.49
30.84
0.30
3.86
4
32.658
0.46
6.54
31.01
0.32
4.01
capabilities. Furthermore, the implementation of Dynamic Hierarchy Reconfiguration strategy further improves ReCon-GS's rendering quality, indicating enhanced spatiotemporal localization accuracy of Gaussian primitives and ensured temporal motion consistency. While the view-based densification process increases per-frame training time, it substantially improves the representation of high- freuuerr scene details. This suggests that merely deforming base Gaussian primitives is insufficient for comprehensive dynamic scene modeling.
Furthermore, 5 ablation experiments on hierarchy depth, motivated by our investigation into motion representation efficiency through ReCon-GS. As shown in Figure experimental findings reveal that a 3-level hierarchy optimally balances high-fidelity scene reconstruction with storage efficiency. This configuration enables ReCon-GS to achieve superior fidelity-storage trade-offs compared to other hierarchical configurations. See Appendix for more ablation studies.
6 Conclusion
This paper introduces ReCon-GS, novel storage-aware framework for high-fidelity multi-view video reconstruction. ReCon-GS achieves density-aware multi-layer decoupled compact motion modeling of Gaussian primitives through an Adaptive Hierarchical Motion Representation frame- work. Furthermore, we propose Dual-level Deformation Strategy combining inter-frame temporal deformation and intra-frame spatial deformation, accurately capture spatio-temporal positions of Gaussian primitives while avoiding reconstruction failures in high-dynamic motions and emerging objects. Notably, ReCon-GS innovatively transforms dynamic single-objective optimization into a storage-ave aware fidelity optimization framework. This paradigm shift allows dynamic balancing between memory efficiency and rendering quality according appli- cation requirements, significantly enhancing the practical applicability of 3D dynamic reconstruction method. Extensive experiments demonstrate that ReCon-GS achieves state-of-the art performance in both rendering quality and training efficiency while maintaining exceptional storage effectiveness. Compared with existing SOTA methods, ReCon-GS attains over 50% memory reduction while preserving same rendering fidelity.
reconstruction from conventional
Linitations. While maintaining alignment with prior methodologies in focusing multi-view gen- eral scene reconstruction tasks, our approach is not specifically optimized for monocular datasets or specialized reconstruction scenarios. Similar to existing streaming based architectures, face chal- lenges dynamic allocation/updaatig of Gaussians to handle real-time object emergence/diisolltionn abrupt scene changes require instant 3DGS structural adaptations.
Acknowledge
This work was supported part by the Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology (No. 2024B 2212100000) and Beijing Natural Science Foundation under grant no. L242014, in part by the PCL-CMCC Foundation for science and innovation grant 2024ZY 1024ZY1C0040, C0040, which are gratefully acknowledged.
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Guidelines:
19

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The answer NA means that the paper does not involve crowdsoureing nor research with
human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent)
may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Question:
Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Answer: [NA]
Justification: The paper does not invovle LLMs as any important, original, or non non-standard components.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to LLM for what should or should not be described.
20

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Appendix
A More Implementation Details
We implement ReCon-GS upon the open-source codebase of 3D Gaussian Splatting (3DGS) [3 During initial frame training, we the spherical harmonic coefficients (SH) degree I to ensure the storage compactness. For all datasets, we terminate the densification process of 3DGS at 5,000 iterations, which significantly alleviates the overfitting issue in streaming 3DGS during initial frame training. Additionally, we adopt the opacity preservation strategy from 4DGS to effectively eliminate oversized Gaussian primitives. The hyperparameter Anoise follows the first-frame configuration from HiCoM B3, while other 3DGS parameters maintain their default settings in 3DGS. Specifically, we train the first frame for 10,000 iterations the Meet Room dataset, and extend this 15,000 iterations for both N3DV and PanopticSports datasets.
For subsequent frame optimization, our approach differs from HiCoM's implementation which uses 250 iterations for deformation field training on Panoptic Sports versus 100 iterations on N3DV and Meet Room datasets. We uniformly apply 100 iterations for deformation field training across all datasets due to our superior ability in capturing the motion of Gaussian primitives. Phase 2, the refinement of deformed Gaussian primitives, considering the more pronounced surface deformation in PanopticSports dataset compared to other datasets, we implement 400 iterations for intensive refinement on PanopticSports dataset versus 100 iterations for N3DV and MeetRoom datasets. All 3DGS- relatted hyperparameters remain consistent with those used in initial frame training to ensure methodological coherence.
B More Ablation Studies
As ReCon-GS represents a novel storage-aware framework designed for high-fidelity real-time rendering in dynamic scene reconstruction, the quantity of Anchor Gaussians per layer in our adaptive hierarchical motion representation structure fundamentally governs the critical trade-off between storage efficiency and rendering quality. Concurrently, the iteration counts allocated to each training stage in subsequent frame training directly determine the training time of our optimization process.
Table 6 investigates the impacts of different iteration configurations across training phases on ReCon- GS performance. The results indicate that prolonging the deformation field training process in Phase can effectively improve rendering quality, however, this training configuration significantly increases computational time. The view-based densification process Phase demonstrates optimal performance and best storage quality at 100 steps. As evidenced by Table 4 and Table view-based densification effectively address persistent discrepancies between learned motion and scene dynamics, though their impact on overall rendering quality remains less substantial compared to the training of adaptively hierarchical deformation.
also performed an ablation study on the performance ReCon-GS, varying the training duration of the initial frame (Frame 0). The detailed results is shown in Table 7 At 2,500 iterations, the suboptimal quality and sparse representation of the initial reconstruction lead to the steepest performance degradation slope. Increasing the training to 5,000 iterations substantially mitigates this quality degradation. Extending the training to 10,000 iterations yields a converged initial frame, which further minimizes quality decay while achieving state-of-the- -hte--rr-t performance. Importantly, with the converged initial frame, ReCon-GS requires only a minimal number of incremental Gaussians per frame to ensure high-quality reconstruction. This result strongly validates the superior robustness of
Table 6:
Ablation on training steps of our ReCon-GS framework.
Phase
Phase
PSNR (dB)f
SSIM t
Storage (MB)
Train (sec)..
Rendering (FPS) 1
50
32.40
0.9560
0.44
5.20
248
100
150
32.76
0.9568
0.44
7.66
249
50
32.56
0.9564
0.46
5.22
246
100
150
32.64
0.9568
0.49
7.70
249
100
100
32.66
0.9571
0.44
6.44
250
21

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Table 7: Ablation on the Training Steps of Frame The Init. 3DG refers to the initial 3D Gaussians constructed from the Frame 0. The Incr. 3DG denotes the incremental Gaussians added in Phase for all following frames.
PSNR
Frame 0 PSNR
Slope
Storge (MB) +
Init. 3DG Storage Incr. 3DG Storage (MB) +
Training Steps
(dB) f
(dB) f
(MB) +
2500
31.03
31.59
0.0077
0,46
8.96
42.17
5000
32.24
32.51
0.0029
0.48
11.14
11.57
10000
32.63
32.75
0.0016
0.46
11.12
4.11
Table First-frame reconstruction results across various scenario configurations. 3DGStream uses standard 3DGS training, Our Method and HiCoM use standard 3DGS training that incorporates with positional noise injection.
Coffee Martini
Flame Steak
Sear Steak
Method
PSNR GS Num Storage Train (MB)) (Sec)) (dB)f
PSNR Num Storage Train
PSNR GS Num Storage Train
(dB)f
(k)
(k)
(MB)4 (Sec) (dB)
(k)
(MB) (Sec)
3DGStream
27.62
416
7.60
340
33.85
208
7.60
304
29.25
214
7.60
317
HiCoM
29.73
348
83.00
368
34.27
187
44.55
334
33.96
191
45.60
317
Ours
29.67
353
16.17
354
31.20
189
8.65
324
33.90
191
8.76
338
ReCon-GS against error accumulation and underscores the critical impact of initial frame quality in streaming frameworks.
C More Detailed Results
C.1
Quantitative Results
C.1.1 Initial 3DGS Comparison
For streaming 3DGS reconstruction frameworks, the initial frame training plays pivotal role in determining the reconstruction quality of subsequent frames. Table presents comparative metrics of average initial frame training quality and corresponding storage consumption across three benchmark datasets. While both ReCon-GS and HiCoM implement positional noise injection during initial Gaussian optimization framework demonstrates superior rendering fidelity. This configuration, combined with our first- frame spherical harmonic (SH) coefficient restriction to degree 1, enables ReCon GS to achieve more compact Gaussian representations compared to HiCoM's SH degree implementation, significantly enhancing storage efficiency without compromising visual quality.
strategic commonality that differentiates them from 3DGStream our
C.1.2 Per-Scene Results
T Table 10 and Table I1 present comprehensive comparative results between ReCon-GS and recent state-of state-of-the-ar methods across three benchmark datasets under specific scene.
C.1.3 More Results
6 demonssrates the superior rate-distortion performance of our method compared to existing SOTA approaches across multiple scenarios. Table T2 presents the detailed results under different
hierarchical anchor densities. By modulating hierarchical anchor density (Iv-() ing the high-efficiency characteristics of the Adaptively Hierarchical Motion Presentation paradigm, ReCon-GS maintains robust rendering quality under varying storage constraints.
anchor )2= and leverag-
C.2 Anchor Gaussian Visualization
We visualize the distribution of Anchor Gaussians at different levels for ReCon-GS and HiCoM, with the results shown Figure 7 As shown in the Figure 7 compared the baseline method HiCoM. ReCon-GS assigns more Anchor Gaussians motion-rich and texture-rich regions (e.g., people, VR glasses, flowers). This is primarily due to the density-based Anchor Gaussian assignment mechanism used by ReCon-GS, As result, ReCon-GS is able to perform more refined rigid motion representation in areas with rich textures, thus improving the overall rendering quality.
22

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34.75
33.5
24.50
24.25
PSNR (4B)
25.50
32.0
40ea
4Dcc
Sato 03 spostream
33.25
4p48
4DOc
QUECK
Storage (MB)
Storage (MB)
(a) Cook Spinach
(b) Sear Steak
$1.0
21.5
30.5
$1.0
30.5
$0.0
PSNR (dB)
30.0
28.5
PSNR
28.0
0.0
28.0.
6.5 alo
Storage (MB)
Storage (MB)
(c) Discussion
(d) Trimming
Figure 6: Rate-distortion performance across diverse scenarios. As evidenced by comparative evaluations, our framework not only achieves superior compression efficiency compared to state-of- the-art approaches [ 6 3 5 2], but also demonstrates unique adaptability in storage-aware fidelity optimization a critical capability for practical deployment scenarios where resource constraints and reconstruction quality must be jointly optimized.
HiCoM
Ours
1st Layer
2nd Layer
Layer
Reference Image
Figure 7: The Visualization of Anchor Gaussian Distribution. 1st Layer is the finest layer, and 3rd Layer is the coarsest layer. The yellow points represent the General Gaussian, while the red, purple, and blue points represent the Anchor Gaussians for their respective layers.
23

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Table Per-scene quantitative results on the N3DV dataset. The method with is reproduced by us through official code in the same experimental environment.
Coffee Martini
Cook Spinach
Cut Roasted Beef
Category Method
PSNR
SSIM Storage|
PSNR1
SSIM Storage|
PSNR1
SSIM Storage
4DGS' 1
30.10
0.935
0.30
31.15
0.958
0.30
32.60
0.951
0.30
Offline
SaRO-GS 3
28.96
1.0
33.19
1.0
33.91
1.0
3DGStream
27.75
0.917
7.85
32.22
0.957
7.79
32.67
0.957
7.76
4DGC 2
27.98
0.58
32.81
0.44
33.03
0.47
Online
QUEEN-I
28.38
0.915
1.17
33.40
0.956
0.59
34.01
0.959
0.57
HiCoM
28.67
0.925
0.82
33.73
0.962
0.63
32.73
0.963
0.60
Ours
30.14
0.938
0.64
33.54
0.961
0.35
33.92
0.966
Flame Salmon
Flame Steak
Sear Steak
PSNR SSIM Storage]
PSNR1
SSIM Storage|
PSNR1
SSIMI Storage]
4DGS
11
30.21
0.934
0.30
33.49
0.961
0.30
30.64
0.961
0.30
Offline
SaRO-Gi GS
3
29.14
1.0
33.83
1.0
33.89
1.0
3DGStream
28.61
0.924
7.83
33.47
0.966
7.79
33.39
0.965
7.76
4DGC [2]
28.49
0.51
33.58
0.44
33.60
0,50
Online
QUEEN-I 4
29.25
0.923
1,00
34.17
0.962
0.59
33.93
0.962
0.56
HiCoM
29.70
0.932
0.81
33.92
0.969
0.60
33.71
0.968
0.60
Ours
30.43
0.938
0.61
34.00
0.969
0.33
33.91
0.966
Table 10: Per-scene quantitative results on the Meet Room dataset. The method with f is reproduced by us through official code in the same experimental environment.
Discussion
Trimming
Vrheadset
Category Method
PSNR1 SSIM Storage PSNRT SSIM Storage| PSNR1
SSIM Storage
3DGStream
6
30.06
0.945
4.10
28.59
0.939
4.10
29.25
0.941
4.10
Online
HiCoM
29.39
0.941
0.48
29.67
0.945
0.37
29.65
0.947
0.30
Ours
30.67
0.955
0.35
31.20
0.956
0.28
30.64
0.953
0.30
Table 11: Per-scene quantitative results on the PanopticSports dataset. The method with is reproduced by us through official code in the same experimental environment.
Basketball
Boxes
Football
Category Method
PSNR SSIM Storage], PSNR SSIM Storage., PSNR SSIM Storage.
Offline
4DGST
26.99
0.912
0.34
27.76
0.922
0.32
27.20
0.922
0.36
DDGSmic3DGS
42
28.22
0.910
0.74
29.46
0.910
0.74
28.49
0.910
0.74
Online
3DGStream
6
20.08
0.815
8.10
23.76
0.861
8.10
22,80
0.856
8.10
HiCoM
28.49
0.915
2.06
29.03
0.922
2.05
29.64
0.926
2.01
Ours
28.78
0.921
28.93
0.926
0.80
30.01
0.931
0.77
Juggle
Softball
Tennis
PSNRT SSIMT Storage|, PSNRT SSIMI Storage|, PSNR* SSIM Storage]
Offline
4DGS
1
26.72
0.923
0.32
26.88
0.923
0.31
27.64
0.925
0.31
Dynamic3DGS
42
29.48
0.920
0.74
28.43
0.910
0.74
28.11
0.910
0.74
3DGStream
6
26.25
0.891
8.10
22.22
0.858
8.10
22.99
9.862
8.10
Online
HiCoM
3
29.28
0.932
2.02
29.61
0.928
1.97
28.97
0.925
2.05
Ours
29.71
0.938
0.79
29.61
0.933
0.77
28.97
0.929
0.79

---

Table 12: Quantitative Results under different hierarchical anchor density (N
anchor J1=1"
Hierarchical Anchor Density
1/16
1/24
1/32
1/48
1/64
1/96
Storage (MB/Frame)
0.63
0.44
0.37
0.28
0.19
0.14
PSNR (dB)
32.72
32.66
32.50
32.39
32.31
32.11
C.3
Qualitative Results
Figure g provides expanded quantitative comparisons between ReCon-GS and HiCoM on the N3DV dataset, highlighting critical performance differentials in dynamic scene reconstruction.
Figure presents extended visual comparisons on the MeetRoom dataset, contrasting geometric fidelity and temporal consistency across competing frameworks.
Figure 10 details supplementary visual evaluations on PanopticSports dataset, emphasizing our method's s enhanced capability in handling complex motion patterns under real-world capture conditions.
25

---

HiCoM
Ours
GT
Figure 8: Qualitative results N3DV Dataset.
26

---

HiCoM
Ours
GT
Figure 9: Qualitative results on Meet Room Dataset.
27

---

HiCoM
Ours
GT
Figure 10: Qualitative results on PanopticSports Dataset.
28


Reviews:
[{"review_id": "0Cf0KsslAO", "reviewer": "Reviewer_43Ni", "summary": "The author proposed a hierarchy-reconfigurable variant of HiCoM. To accomplish such capability of reconfiguration, they repropose a new set of anchor point Gaussians per fixed frame span. To further make sure a smooth transition after hierarchy reconfiguration, they derive the new anchor's deformation field with an inheritance from the previous hierarchy setup.", "questions": "- not quite sure the meaning of \"Continuum\" in the title\n- since you always have a stage 1 for training the deformation field after a new frame comes, why bother when a reconfiguration occurs? or does such \"inheritance\" based deformation field initialization effort help the stage 1 training of the deformation field? Honestly it seems to be marginally helpful. \n- Densification is per-frame or per-reconfiguration?\n- try eval on Technicolor and longer sequence.\n- there actually should be some extra overhead for repetitive anchor reconfiguration: record the index of those anchor points at different timestamp requires extra storage. So how your method achieves a more compact representation than HiCoM?", "limitations": "as mentioned above, it is still a deformation-based method, simply a better way (easier-to-train actually) to represent the gaussian deformation across frames. Just like any other deformation-based dynamic modeling, it cannot efficiently encode highly dynamic scenes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Good:\n- it works great with best performance compared with other sota works. Also baseline works are appropriately selected.\n- I do like Figure 6 in appendix which demonstrate the effectiveness of the reconfiguration and would encourage the author to put it in the front sections. Also it will be more convincing to eval the proposed method on longer dataset than those 300s datasets.\n\nWeakness:\n- grid based anchor point sampling is BORING.\n- **the research innovation of the work is very limited.** The main difference compared with previous HiCom is merely this reconfiguration setup, which is implemented in a naive method: repropose a new set of anchors. The inheritance seems to be unnecessary. \n- still it is a deformation-based method. Just like any other deformation-based dynamic modeling, it cannot efficiently encode highly dynamic scenes. \n\nothers:\n- overall its a well-written paper with abundant visualization. yet readers with little background for HiCom might get lost.", "was_revised": true, "final_justification": "I have kept the original rating of weak accept for this paper. The method is straightforward enough for me and makes sense with no doubt. The reason for this weak decision is that the method is a subtle update of HiCom, and personally for me it is not a significant innovation. Yet it is still a solid paper with reasonable method and convincing evaluation. I will recommend this paper for acceptance.", "rebuttal": "We sincerely appreciate your recognition of ReCon-GS's performance. We will now systematically address the questions you have raised.\n\n**1. The meaning of \"Continuum\" in the title.**\n\nThe term \"Continuum\" highlight ReCon-GS's characteristics. Through our Adaptive Hierarchical Motion Representation and Dynamic Reconfiguration Strategy, the framework effectively prevents progressive geometric distortion in streaming reconstruction – a capability conclusively demonstrated in Figure 7. This preservation of geometric consistency across frames fundamentally realizes a true continuum of scene representation, justifying the terminology.\n\n**2. How essential is Intra-Hierarchical Deformation Inheritance to achieving performance gains during Re-Hierarchization, considering deformation fields undergo per-frame retraining in Stage 1?**\n\nGiven that inter-frame motion typically exhibits inertia, ReCon-GS initializes the motion state of the current frame’s Anchor Gaussians using that of the previous frame’s Anchor Gaussians. However, sustained displacements reduce Anchor Gaussians' capacity to represent quasi-rigid motion. This necessitates reassigning anchors through Re-Hierarchization. After reassignment, directly inheriting motion from old to new Anchor Gaussians would compromise temporal motion consistency. To address this, as detailed in Section 4.3, we proposes Intra-Hierarchical Deformation Inheritance. This mechanism enables new anchors to precisely inherit local motion inertia by uniformly acquiring deformation traits from three legacy anchors, which enables local motion inertia inheritance for new anchors while preserving temporal consistency.\n\nTo thoroughly validate the efficacy of this mechanism, we conducted an ablation study. As shown in *Table #1*, removing Intra-hierarchical Deformation Inheritance leads to a performance deterioration, accompanied by a pronounced increase in the rate of performance decline. This result confirms the critical role of this mechanism in maintaining motion prediction accuracy and temporal consistency. To enhance readers' understanding of ReCon-GS's validity, we will add this ablation experiment in the revised version based on your feedback.\n\n**Table #1: Ablation on Intra-hierarchical Deformation Inheritance.**\n|  | PSNR (dB)(↑) | Slope (↓) |\n|----------------------------------------|--------------------|-------------------------|\n| w/o Intra-hierarchical Deforamtion Inheritance | 32.45  |  0.0014   |\n| Ours (full)   | 32.66   | 0.0005  |\n\n**3. Densification is per-frame or per-reconfiguration?**\n\nDensification operates on a per-frame scheme. Although this incurs a modest training cost, the frame-by-frame densification promptly compensates for limitations in ReCon-GS's quasi-rigid motion representation regarding local detail reconstruction.\n\n\n**4. Quantitative Result Comparison on other Datasets.**\n\nBased on your suggestion, we conducted supplementary quantitative evaluation on the Technicolor dataset[1].\n\nAs demonstrated in *Table #2*, our method achieves **state-of-the-art (SOTA)** performance across rendering quality, storage efficiency, and rendering speed metrics. Notably, compared to streaming approaches, ReCon-GS delivers approximately **0.5 dB higher rendering quality** while reducing storage by over **40%** and significantly accelerating rendering speed. Against offline methods, ReCon-GS attains SOTA rendering quality while achieving more than 25% storage reduction.\n\n**Table #2: Quantitative Comparison on Technicolor Light Field dataset.**\n| Category   | Method         | PSNR (dB)(↑) | SSIM (↑) | Storage (MB/Frame)(↓) | FPS (↑) |\n|------------|----------------|--------------|----------|-----------------------|---------|\n| NeRF-based | HyperReel[2]   | 31.80        | 0.906    | 1.20                  | 4       |\n| Offline    | STG[3]         | 33.60        | -        | 1.10                  | 87      |\n| Offline    | Ex4DGS[4]      | 33.62        | 0.916    | 2.81                  | 72      |\n| Online     | E-D3DGS[5]     | 33.24        | 0.907    | 1.54                  | 79      |\n| **Online** | **ReCon-GS**   | **33.83**    | **0.932**| **0.81**              | **207** |\n\n\n\n**5. How our method achieves more compact deformation data representation than HiCoM?**\n\nUnlike HiCoM, our method doesn't require indexing every deformation field parameter for preservation. ReCon-GS effectively achieve this through a geometrically ordered storage scheme: Our anchor Gaussians, sampled via grid-based Farthest Point Sampling (FPS), undergo motion data serialization relative to the world coordinate, where parameters are stored sequentially from the farthest anchor point from the origin inward along a predetermined spatial axis. This coordinate-based ordering inherently eliminates redundant index storage while ensuring consistent data retrieval, making ReCon-GS significantly more compact than HiCoM.\n\nThank you once again for your time and effort in reviewing our manuscript. We look forward to any further comments you may have.\n\n**Reference**:\n\n[1]Neus Sabater, Guillaume Boisson, Benoit Vandame, et al. Dataset and pipeline for multi-view light-field video. In Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops (CVPR workshops), 2017.\n\n[2]Benjamin Attal, Jia-Bin Huang, Christian Richardt, Michael Zollhoefer, Johannes Kopf, Matthew O’Toole, and Changil Kim. HyperReel: High-fidelity 6-DoF video with ray-conditioned sampling. In Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR), 2023.\n\n[3]Zhan Li, Zhang Chen, Zhong Li, and Yi Xu. \"Spacetime gaussian feature splatting for real-time dynamic view synthesis.\" In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.\n\n[4]Junoh Lee, ChangYeon Won, Hyunjun Jung, Inhwan Bae, and Hae-Gon Jeon. \"Fully explicit dynamic gaussian splatting.\" In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), 2024.\n\n[5]Jeongmin Bae, Seoha Kim, Youngsik Yun, Hahyun Lee, Gun Bang, and Youngjung Uh. \"Per-gaussian embedding-based deformation for deformable 3d gaussian splatting.\" In Proceedings of the European Conference on Computer Vision (ECCV), 2024."}, {"review_id": "s4gzGSYPJS", "reviewer": "Reviewer_zioK", "summary": "This paper proposes a multi-level anchor Gaussian design for real-time free-viewpoint video reconstruction. To support the hierarchical structure, the method also decomposes rigid transformations in a hierarchical manner. Additionally, the Gaussian hierarchy is reconfigured during training to better capture motion. The proposed approach achieves improved performance while offering faster training and reduced storage requirements.", "questions": "Please kindly refer to the \"Weaknesses\".", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "### Strengths:\n- The writing is clear and easy to follow. The methodology figure is well-designed and effectively conveys the main idea, allowing readers to grasp it quickly.\n- The method uses less training cost to get better performance.\n\n### Weaknesses:\n- The hierarchical idea in this paper is similar with ScaffoldGS [https://arxiv.org/pdf/2312.00109] as well as the level-of-detail concept in OctreeGS [https://arxiv.org/pdf/2403.17898], with the addition of rigid transformations. As it currently stands, the contribution appears to be more of a technical extension. Further clarification is needed to better highlight the novelty of the proposed approach.\n- Only Table 1 reports LPIPS metrics. It would be beneficial to include perceptual metrics in other experiments as well to enable a more comprehensive comparison.\n- This is a minor question: does the reported training time include the first-frame reconstruction? According to the paper, this step requires 10,000 to 15,000 iterations, so it seems unlikely to be completed within 10 seconds. Additionally, how many Gaussian primitives are generated in total?", "was_revised": true, "final_justification": "The authors provide a clear explanation of their method and effectively clarify the novelty of their work. I am raising my score.", "rebuttal": "We appreciate your recognition of the strengths of our work, particularly the achievement of state-of-the-art (SOTA) performance using a straightforward loss function during training, as well as your positive feedback on the quality of our writing. We will systematically address each of your concerns and questions.\n\n**1. The proposed method shares similarity with Scaffold-GS and Octree-GS.**\n\nReCon-GS demonstrates fundamental distinctions from Scaffold-GS[1] and Octree-GS[2]. First, Scaffold-GS and Octree-GS target static 3D reconstruction, while ReCon-GS addresses dynamic 4D reconstruction, resulting in divergent frameworks and optimization objectives. Moreover, Scaffold-GS embeds Gaussian attributes via uniformly sampled voxel-grid anchors for compact scene representation. Octree-GS achieves multi-resolution compression through Level-Of-Detail (LOD) hierarchies. Both prioritize spatial compactness. In contrast, ReCon-GS employs adaptively sampled Anchor Gaussians that capture local motion characteristics. Its hierarchical structure compactly encodes inter-frame motion rather than spatial features. Consequently, ReCon-GS’s Adaptive Hierarchical Motion Representation fundamentally differs from these static approaches.\n\n**2. Include perceptual metrics in other experiments.**\n\nYour concern is indeed well-founded, as LPIPS provides a more comprehensive assessment of ReCon-GS's capabilities. As presented in *Table #1*, LPIPS(VGG) metrics comparing ReCon-GS against HiCoM[3] and 3DGStream[4] across both datasets are now included. We observe that ReCon-GS achieves state-of-the-art (SoTA) performance on this perceptual metric among streaming frameworks. Considering your suggestion, we will include the LPIPS metric for the MeetRoom and PanopticSports datasets in our revised manuscript.\n\n**Table #1: The Perceptual Quality Comparison on Meet Room and PanopticSports datasets (Averaged Over 3 Training Results)**\n| Method         | LPIPS at Meet Room (↓) | LPIPS at PanopticSports (↓) |\n|----------------|------------------------|-----------------------------|\n| 3DGStream      | 0.188                  | 0.187                       |\n| HiCoM          | 0.182                  | 0.142                       |\n| **ReCon-GS**   | **0.163**              | **0.136**                   |\n\n**3. Does the reported training time include the first-frame reconstruction?** \n\nWe sincerely apologize for not explicitly specifying the methodology in the table caption. The time metrics reported in our tables include the first-frame training duration. Our first-frame training time typically ranges between 300 and 400 seconds, resulting in a negligible impact on the overall reconstruction process. To more clearly demonstrate our method's advantage in low training cost, *Table #2* presents comparisons on the N3DV dataset, showing that ReCon-GS requires significantly less time than both HiCoM and 3DGStream for both first-frame training and average time per frames. \n\n**Table #2: Training Time Comparison on N3DV (Averaged Over 3 Training Results)**\n| Method         | First Frame Tr. Time (sec)(↓) | Avg. Tr. Time (sec)(↓) | Avg. Tr. Time (w/o First Frame)(sec)(↓) |\n|----------------|----------------------------|---------------------|--------------------------------------|\n| 3DGStream      | 341.82                     | 7.58                | 6.46                                 |\n| HiCoM          | 336.51                     | 6.63                | 5.53                                 |\n| **ReCon-GS**   | **328.96**                 | **6.48**            | **5.36**                             |\n\n**4. How many Gaussian primitives are generated in total?** \n\n*Tables #3* and *Table 6* in the manuscript demonstrate that ReCon-GS generates approximately 244k Gaussians in the first frame, which is comparable to HiCoM. However, due to ReCon-GS's implementation of spherical harmonics (SH) at degree=1, its actual first-frame storage occupies only 20% of HiCoM's requirement. During subsequent frame reconstruction, ReCon-GS produces approximately 6.4k incremental Gaussians, merely **40%** of HiCoM's corresponding count. This evidences ReCon-GS's superior motion representation capability, enabling high-quality scene reconstruction through densification while requiring significantly fewer Gaussians.\n\n**Table #3: Statistics of 3DGS Quantities at Different Stages (Averaged Over 3 Training Results)**\n| Method         | inital 3DG(↓)| incremental 3DG(↓)| Total 3DG(↓)|\n|----------------|--------------|-------------------|-------------|\n| HiCoM          | 243.3k       | 15.8k             | 402.1k      |\n| **ReCon-GS**   | 244.0k       | **6.4k**          | **308.8k**  |\n\nThank you once again for your valuable feedback and insightful comments. We hope that our responses have addressed your concerns and clarified the points raised in your review.\n\n**Reference**:\n\n[1]Tao Lu, Mulin Yu, Linning Xu, Yuanbo Xiangli, Limin Wang, Dahua Lin, and Bo Dai. \"Scaffold-gs: Structured 3d gaussians for view-adaptive rendering.\" In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024.\n\n[2]Kerui Ren, Lihan Jiang, Tao Lu, Mulin Yu, Linning Xu, Zhangkai Ni, and Bo Dai. \"Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians.\" IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.\n\n[3]Qiankun Gao, Jiarui Meng, Chengxiang Wen, Jie Chen, and Jian Zhang. \"Hicom: Hierarchical coherent motion for dynamic streamable scenes with 3d gaussian splatting.\" In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), 2024.\n\n[4]Jiakai Sun, Han Jiao, Guangyuan Li, Zhanjie Zhang, Lei Zhao, and Wei Xing. 3dgstream: On-the-fly training of 3d gaussians for efficient streaming of photo-realistic free-viewpoint videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024."}, {"review_id": "K89HldvQvw", "reviewer": "Reviewer_pBHc", "summary": "This paper presents ReCon-GS, a novel framework for real-time free-viewpoint video (FVV) reconstruction that balances high rendering fidelity with memory efficiency. It introduces an Adaptive Hierarchical Motion Representation using Anchor Gaussians to model scene dynamics in a coarse-to-fine manner. A Dynamic Hierarchy Reconfiguration mechanism addresses anchor drift while preserving motion coherence via intra-level deformation inheritance. Additionally, a storage-aware optimization strategy enables flexible trade-offs between quality and memory. Experiments show that ReCon-GS outperforms prior streaming methods with faster training and lower memory usage.", "questions": "Most of my questions have been raised in the Weaknesses section, and also is there a explicit motion relation between anchor gaussians across different level?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n1. The proposed multi-level anchor-driven deformation model captures scene dynamics in a coarse-to-fine manner, providing an efficient and compact motion representation.\n2. The reconfiguration strategy helps mitigate anchor drift and improves temporal coherence.\n3. The ablation study and per-scene result is detailed.\n\nWeakness:\n1. While the method shows improvement in streaming settings, it underperforms compared to offline methods. In some cases, it requires higher storage, longer training times, and achieves lower reconstruction quality.\n2. Some details are not clear to me: how often the dynamic hierarchy reconfiguration is triggered during training and whether the framework is sensitive to this parameter, A clearer formulation explain how Anchor Gaussians quantitatively govern or parameterize general Gaussian deformation. In Table 3: It is surprising that removing the Hierarchical Motion Representation leads to lower storage, which I think is proposed to improve efficiency and compactness.\n3. The method relies on freezing appearance attributes initialized from the first frame during the first training phase. This introduces sensitivity to initial conditions when the first frame poorly captures the scene. Do you have examples show that.\nI will consider raise rating if my above weakness point are answered.", "was_revised": true, "final_justification": "The authors have addressed my concerns regarding the effectiveness of the Hierarchical Motion Representation, so I have raised my score accordingly.", "rebuttal": "We sincerely appreciate your recognition of ReCon-GS, particularly regarding how its adaptive hierarchical motion representation framework and Dynamic Reconfiguration Strategy effectively achieve efficient and compact motion representation while resolving error accumulation caused by anchor Gaussian drift. Moving forward, we will systematically address each of your concerns and questions.\n\n**1. Regarding storage requirements, rendering quality, and training time compared to offline methods.** \n\nWe acknowledge the critical importance of these factors in determining ReCon-GS's practical applicability. It should be noted that through its Storage-aware fidelity optimization objective, ReCon-GS enables storage regulation by adjusting the density of anchor Gaussians in its Adaptive Hierarchical Motion Representation framework while maintaining efficient motion modeling. As evidenced in *Table #1*, ReCon-GS achieves tunable quality-storage trade-offs ranging from 0.14MB/Frame to 0.63MB/Frame across varying anchor Gaussian densities. Crucially, at a 1/64 Anchor Guassian density, ReCon-GS maintains an average storage cost of **0.19MB/Frame** – not only lower than offline methods (4DGS[1], STG[2], Swift4D[3]) but simultaneously achieving **state-of-the-art (SOTA) rendering quality** among all approaches. For training time, online frameworks inherently face disadvantages compared to offline paradigms due to computational constraints, representing an area for our future improvement. However, in real-time 4D reconstruction scenarios, ReCon-GS's online framework consequently offers greater practical utility than offline alternatives.\n\n**Table #1: Quantitative Comparison Across Varying Anchor Gaussian Density Levels. Storage values represent per-frame averages inclusive of the initial frame.**\n|Method      | Anchor Guassian Density  | Storage (MB/Frame)(↓) | PSNR (dB)(↑) |\n|------------|--------------------------|-----------------------|--------------|\n|4DGS        | - | 0.30 | 31.36 |\n|STG         | - | 0.67 | 32.05 |\n|Swift4D     | - | 0.40 | 32.23        |                        \n|ReCon-GS    | 1/16        | 0.63    | **32.72**|\n|ReCon-GS    | 1/24 (Ours) | 0.44    | 32.66    |\n|ReCon-GS    | 1/32        | 0.37    | 32.50    |\n|ReCon-GS    | 1/48        | 0.28    | 32.39    |\n|ReCon-GS    | 1/64        | 0.19    | 32.31    |\n|ReCon-GS    | 1/96        | **0.14**| 32.11    |\n\n**2. How often the dynamic hierarchy reconfiguration is triggered during training?** \n\nReCon-GS employs a fixed 4-frame interval for reconfiguration. This design stems from the observation that reconstruction quality inherently fluctuates in streaming frameworks. Consequently, quality-based dynamic adaptation proves ineffective for this issue since quality variations within 3-5 frame intervals do not reliably indicate diminished motion representation capacity of anchor Gaussians. The 4-frame reconfiguration interval represents the optimal configuration determined through extensive experimentation.\n\n**3. How Anchor Gaussians quantitatively govern or parameterize general Gaussian deformation?**\n\nWe adopt a simple yet effective paradigm where each General Gaussian directly inherits motion parameters from its associated Anchor Gaussian. Following Dynamic Reconfiguration Strategy, which redistributes Anchor Gaussians, General Gaussians are reassigned to new anchors via L1-distance-based association. After reassignment, General Gaussians subsequently inherit motion transformations from their newly assigned Anchor Gaussians during deformation field training (Stage 1).\n\n**4. Removing the Hierarchical Motion Representation leads to lower storage.**\n\nIn *Table #2*, Our storage analysis reveals that removing hierarchical motion representation significantly reduces motion field storage requirements. However, this comes at the cost of diminished motion representation capacity, necessitating increased incremental 3DG generation to compensate for detail loss. Crucially, by implementing spherical harmonics at degree=1 for 3D Gaussians, the per-Gaussian storage footprint remains minimal, preventing substantial memory overhead despite increased Gaussian counts. \n\n**Table #2: The ablation study on storage.**\n|  | Total 3DG number(↓)| Storage for 3DG (MB)(↓) | Storage for Deformation(MB)(↓) | Total Storage(MB)(↓) |\n|----------------------------------------|--------------------|-------------------------|--------------------------------|----------------------| \n| w/o Hierarchical Motion Representation | 320.6k  | 14.68 | 81.05 | 95.73                |\n| Ours (full)   | 308.8k   | 14.13    | 116.12        | 128.25  |\n\n**5. The sensitivity to initial conditions when the first frame poorly captures the scene.**\n\nYour suggestion is profoundly insightful. Streaming frameworks indeed face catastrophic error accumulation when encountering poor quality of initial frame reconstruction. To demonstrate the superiority of ReCon-GS's motion representation framework, we conducted experiments under varying initial frame training iterations. \n\nTable #3 reports ReCon-GS performance under varying initial frame training iterations. At 2,500 iterations, suboptimal initial reconstruction quality and sparse scene representation result in the steepest performance degradation slope. When trained for 5,000 iterations, quality degradation is substantially mitigated. Extending to 10,000 iterations yields converged initial frame training, further optimizing quality decay  while achieving state-of-the-art (SOTA) performance. Responding to your insight, we will integrate this analysis in the future revised version to demonstrate the critical impact of initial frame quality on streaming frameworks while further validating ReCon-GS's superior robustness against error accumulation.\n\n**Table 3: The ablation study on Training Steps of Frame 0.**\n| Method   | Init. Training_Step | PSNR (dB)(↑) | Frame 0 PSNR (dB)(↑)  | Slope (↓) | Storage (MB/Frame)(↓) | Init. 3DG Storage (MB)(↓) | Incr. 3DG Storage (MB)(↓) | Storage for Deformation Field (MB)(↓) |\n|----------|---------------------|--------------|-----------------------|-----------|-----------------------|---------------------------|---------------------------|-----------------------------|\n| ReCon-GS | 2500 | 31.03 | 31.59 | 0.0077 | 0.46 |   8.96 | 42.17 | 78.96|\n| ReCon-GS | 5000 | 32.24 | 32.51 | 0.0029 | 0.48 | 11.14 | 11.57 | 94.80 |\n| ReCon-GS | 10000  | 32.63 | 32.75 | 0.0016  | 0.46  | 11.12 | 4.11 | 92.76 |\n\n**6. Is there a explicit motion relation between anchor gaussians across different level?**\n\nNo, We deliberately avoid imposing hierarchical coarse-to-fine constraints on Anchor Gaussians because non-tree-structured organization enhances motion representation efficacy. This design accounts for subtle quasi-rigid motions occurring at rigid body junctions, which would be inadequately represented under tree-structured partitioning. Enforcing tree-structured partitioning would consequently compromise motion encoding efficiency.\n\nWe are grateful for your constructive feedback, which has been instrumental in enhancing the quality of our manuscript. Thank you once again for your time and consideration.\n\n**Reference**:\n\n[1]Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, and Xinggang Wang. 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.\n\n[2]Zhan Li, Zhang Chen, Zhong Li, and Yi Xu. \"Spacetime gaussian feature splatting for real-time dynamic view synthesis.\" In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.\n\n[3]Jiahao Wu, Rui Peng, Zhiyan Wang, Lu Xiao, Luyang Tang, Jinbo Yan, Kaiqiang Xiong, and Ronggang Wang. Swift4D: Adaptive divide-and-conquer Gaussian Splatting for compact and efficient reconstruction of dynamic scene. In International Conference on Learning Representations (ICLR), 2025.\n\n[4]Qiankun Gao, Jiarui Meng, Chengxiang Wen, Jie Chen, and Jian Zhang. \"Hicom: Hierarchical coherent motion for dynamic streamable scenes with 3d gaussian splatting.\" In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), 2024."}, {"review_id": "XVhaA76x80", "reviewer": "Reviewer_Yahd", "summary": "This paper proposes ReCon-GS, a method that dynamically assigns multi-level anchor Gaussians to capture inter-frame geometric deformation. This approach reduces storage requirements while maintaining high reconstruction quality. Experiments on multiple datasets shows this method achieves better reconstruction accuracy and storage efficiency than current state-of-the-art online methods.", "questions": "- Could the authors visualize the spatial extent and assignment of anchors at each level? This would provide intuitive evidence that the multi-level scheme effectively captures motion in a coarse-to-fine manner.\n\n - How does the proposed method compare with recent offline baselines such as STG[1] and SplineGS[2]? If ReCon-GS not outperforms these, please consider adding these to Tables 1 and 7. If the proposed online method outperforms these offline methods, a more detailed explanation of why online is better would strengthen the paper. Notably, the results reported for STG and 4DGS on N3DV seem weaker than those in the SplineGS paper, clarification would be helpful.\n\n - Some tables lack proper units for storage and training time, which may hinder reproducibility and clarity.\n\n - Would replacing the MLP with an explicit trajectory representation (e.g., as in SplineGS) further improve performance or interpretability?\n\n[1] Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis. CVPR 2024\n\n[2] SplineGS: Learning Smooth Trajectories in Gaussian Splatting for Dynamic Scene Reconstruction. ICLR 2025", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n - The idea of coarse-to-fine multi-level anchors is well-motivated and aligns with real-world motion patterns, leading to reduced storage overhead.\n\n - The experiments are thorough, and the proposed method outperforms existing state-of-the-art online approaches.\n\nWeaknesses\n\n - The final multi-level anchor modeling lacks visualization, which would help justify its effectiveness and spatial structure.\n\n - The comparisons in the offline setting omit some recent baselines.", "was_revised": true, "final_justification": "Although NeurIPS restricts the inclusion of visualizations for the hierarchical Gaussian, I trust the authors can elaborate on this aspect thoroughly in the revised appendix. I will maintain my original score.", "rebuttal": "We sincerely appreciate your recognition of our Adaptive Hierarchical Motion Representation framework design and particularly value your acknowledgment of the comprehensive experimental validation demonstrating superior performance over existing streaming frameworks. We will now systematically address each of your questions and concerns.\n\n**1. The Visualization of hierarchical Anchor Gaussian & The effectiveness of Anchor Guassian.** \n\nWhile NeurIPS rebuttal guidelines prevent us from providing visualizations of hierarchical anchor Gaussian, we will include  this visualization in the supplemental appendix of our revised version to demonstrate the efficacy of Anchor Gaussians. Moreover, we have included comprehensive quantitative evidence to prove the effectiveness of Anchor Gaussians in *Table #1*. The data demonstrates that compared to HiCoM[1] (the current open-source SOTA in streaming frameworks), ReCon-GS generates a comparable number of initial Gaussians in the first frame but reduces subsequent incremental Gaussian generation by approximately **60%**. This significant reduction indicates that ReCon-GS achieves high-quality scene reconstruction while requiring only minimal Gaussian increments (approximately 300 per frame). These metrics strongly validate the efficacy of our Adaptive Hierarchical Motion Representation in enabling efficient motion modeling.\n\n**Table #1: Statistics of 3DGS Quantities at Different Stages. (Averaged Over 3 Training Results)**\n| Method         | inital 3DG(↓)| incremental 3DG(↓)| Total 3DG(↓)|\n|----------------|--------------|-------------------|-------------|\n| HiCoM          | 243.3k | 15.8k | 402.1k |\n| **ReCon-GS**   | 244.0k | **6.4k** | **308.8k** |\n\n**2. More Quantitative Results Compared with Recent SOTA Offline Methods.**\n\nBased on your suggestions, we have compared the performance of ReCon-GS with STG[2] and SplineGS[3]. After reviewing their papers, we have also noticed the inconsistency in the benchmarks in the performance comparison between STG and SplineGS: the performance of STG reported by SplineGS (32.57dB) is higher than that in the STG paper (32.05dB). Since SplineGS is not currently open-sourced, we will directly use the performance reported in the paper to ensure fairness. \n\n*Table #2* demonstrates ReCon-GS's **state-of-the-art (SOTA)** performance in rendering quality, storage efficiency, training time, and FPS even when compared to recent offline methods. This superiority stems primarily from the Adaptive Hierarchical Motion Representation's efficient motion modeling capability. Offline methods use global MLPs to represent motion across space and time. This creates compact motion encoding, but storage limits hurt their generalization ability. Swift4D (ICLR25)[4] addresses this problem through static-dynamic 3D gaussian decoupling, allocating more storage to dynamic Gaussian motion representation. However, camera-related issues may introduce noticeable artifacts in static regions[5], compromising static scene reconstruction. Moreover, as training datasets trend toward higher frame rates, streaming frameworks have shifted optimization objectives from temporally global to temporally local deformation field refinement, emerging as a more effective paradigm. Recently, the fundamental challenge in streaming frameworks remains temporal error accumulation. ReCon-GS overcomes this via its novel Dynamic Reconfiguration Strategy, which preserves motion representation fidelity through periodic anchor Gaussian redistribution. This constitutes the underlying reason for ReCon-GS's performance superiority over current SOTA methods.\n\n\n**Table #2: Compare with recent offline baselines.**\n| Category | Method          | PSNR (dB)(↑)| Storage (MB/Frame)(↓) | Training Time (min)(↓) | FPS(↑) |\n|----------|-----------------|-------------|-----------------------|------------------------|--------|\n| Offline  | STG (official)  | 32.05 | 0.67 | - | 140 |\n| Offline  | STG (SplineGS)  | 32.57 | - | 252 | 140 |\n| Offline  | SplineGS        | 32.60 | - | 55  | 76  |\n| Online   | **ReCon-GS(ours)** | **32.66** | **0.44** | **32** | **250** |\n\n**3. Some tables lack proper units for storage and training time.**\n\nWe appreciate your suggestion. Providing more detailed units will make our manuscript clearer for readers, and we will add unit information in the tables in the subsequent revised version.\n\n**4. After replacing the explicit motion representation with MLP, can the performance be further improved?**\n\nThis is an excellent advice. Indeed, many current online and offline methods use MLP for implicit representation of inter-frame motion. However, for the online framework, implicit representation will bring additional issues: \n * **The storage of MLP parameters.** \n Without changing the number of anchor Gaussians, the introduction of MLP will consume more storage compared to the explicit motion representation of ReCon-GS. \n * **Extremely limited quality improvement.** \nWhen ReCon-GS needs to sparsify anchor Gaussians due to storage constraints, the MLP-based motion representation method may lead to a huge decline in rendering quality.\n\nThank you again for your valuable feedback. We hope our responses meet your expectations. If there are any further questions or concerns, please do not hesitate to let us know.\n\n**Reference**:\n\n[1]Qiankun Gao, Jiarui Meng, Chengxiang Wen, Jie Chen, and Jian Zhang. \"Hicom: Hierarchical coherent motion for dynamic streamable scenes with 3d gaussian splatting.\" In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), 2024.\n\n[2]Zhan Li, Zhang Chen, Zhong Li, and Yi Xu. \"Spacetime gaussian feature splatting for real-time dynamic view synthesis.\" In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.\n\n[3]Jihwan Yoon, Sangbeom Han, Jaeseok Oh, and Minsik Lee. \"SplineGS: Learning Smooth Trajectories in Gaussian Splatting for Dynamic Scene Reconstruction.\" In The Thirteenth International Conference on Learning Representations (ICLR), 2025.\n\n[4]Jiahao Wu, Rui Peng, Zhiyan Wang, Lu Xiao, Luyang Tang, Jinbo Yan, Kaiqiang Xiong, and Ronggang Wang. Swift4D: Adaptive divide-and-conquer Gaussian Splatting for compact and efficient reconstruction of dynamic scene. In International Conference on Learning Representations (ICLR), 2025.\n\n[5]Youngsik Yun, Jeongmin Bae, Hyunseung Son, Seoha Kim, Hahyun Lee, Gun Bang, and Youngjung Uh. \"Compensating Spatiotemporally Inconsistent Observations for Online Dynamic 3D Gaussian Splatting.\" In Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers (SIGGRAPH), 2025."}]

Author rebuttal:

Meta-review: This paper proposes an adpative storage-efficient method for 4D Gaussian reconstruction of dynamic scenes in a streaming fashion. Its core idea is to employ a hierarchical motion representation to compactly encode inter-frame Gaussian differences and a reconfiguration strategy for them to reduce their drift over time. The paper shows quantitatively superior results over the state of the art. Four reviewers provided final ratings of 4 x borderline accept. The strength of the work was noted in having strong quantitative performance and a logical and simple method. During the author-reviewer discussion phase, most of the reviewer's major concerns were sufficiently addressed and they all raised their scores towards a favorable accept rating. The AC has carefully checked the paper and its reviews, concurs with the reviewers' consensus and recommends acceptance. Congratulations! The authors should incorporate the changes that they have promised into the final camera-ready version of their manuscript.
Decision: Accept (poster)