Title: REArtGS: Reconstructing and Generating Articulated Objects via 3D Gaussian Splatting with Geometric and Motion Constraints
Venue: NeurIPS 2025
Accepted: yes
Keywords: Surface Reconstruction

3DGS

Embodied Intelligence
Abstract: Articulated objects, as prevalent entities in human life, their 3D representations play crucial roles across various applications. However, achieving both high-fidelity textured surface reconstruction and dynamic generation for articulated objects remains challenging for existing methods. In this paper, we present REArtGS, a novel framework that introduces additional geometric and motion constraints to 3D Gaussian primitives, enabling realistic surface reconstruction and generation for articulated objects. Specifically, given multi-view RGB images of arbitrary two states of articulated objects,  we first introduce an unbiased Signed Distance Field (SDF) guidance to regularize Gaussian opacity fields, enhancing geometry constraints and improving surface reconstruction quality. Then we establish deformable fields for 3D Gaussians constrained by the kinematic structures of articulated objects, achieving unsupervised generation of surface meshes in unseen states. Extensive experiments on both synthetic and real datasets demonstrate our approach achieves high-quality textured surface reconstruction for given states, and enables high-fidelity surface generation for unseen states. Project site: https://sites.google.com/view/reartgs/home.

REArtGS: Reconstructing and Generating Articulated Objects via 3D Gaussian Splatting with Geometric and Motion Constraints

Abstract

Articulated objects, as prevalent entities in human life, their 3D representations play crucial roles across various applications. However, achieving both high-fidelity textured surface reconstruction and dynamic generation for articulated objects remains challenging for existing methods. In this paper, we present REArtGS, a novel framework that introduces additional geometric and motion constraints to 3D Gaussian primitives, enabling realistic surface reconstruction and generation for articulated objects. Specifically, given multi-view RGB images of arbitrary two states of articulated objects, we first introduce an unbiased Signed Distance Field (SDF) guidance to regularize Gaussian opacity fields, enhancing geometry constraints and improving surface reconstruction quality. Then we establish deformable fields for 3D Gaussians constrained by the kinematic structures of articulated objects, achieving unsupervised generation of surface meshes in unseen states. Extensive experiments on both synthetic and real datasets demonstrate our approach achieves high-quality textured surface reconstruction for given states, and enables high-fidelity surface generation for unseen states. Project site: https://sites.google.com/view/reartgs/home .

1 Introduction

Articulated objects are ubiquitous in our daily lives. Modeling articulated objects, i.e. mesh reconstruction and generation, holds significant importance in many fields including virtual and augmented reality [16, 36], object manipulation [40, 31], robots [33, 17] and human-object interaction [37, 12]. Currently, the surface and shape reconstruction for articulated objects is a non-trivial task due to the challenges: firstly, articulated objects exhibit complicated and diverse geometric structures with a wide range of scales. Secondly, articulated objects possess varied kinematic structures, and the static surface meshes generated from vanilla 3D reconstruction methods fail to meet practical interaction requirements. Under this circumstance, PARIS [14] attempts to use multi-view images from two states for articulated object dynamically reconstruction with neural implicit radiance fields. Nevertheless, PARIS lacks geometric constraints, leading to shape-radiance ambiguity and additional errors for motion analysis.

In recent years, 3D Gaussian Splatting (3DGS) [10] achieves realistic and real-time novel view synthesis through explicit representation with 3D Gaussian primitives. Some subsequent works have expanded 3DGS to surface reconstruction [42, 8, 1] and dynamic reconstruction [38, 13, 4]. However, existing 3DGS-based surface reconstruction methods typically suffer from insufficient geometric constraints [42] or impose constraints by restricting the shapes of Gaussian primitives [8, 6], leading to noisy surface reconstruction results. On the other hand, current dynamic reconstruction methods via 3DGS tend to input motion time and spatial positions into neural networks to obtain the de-

Unseen state

Recon.

Generation

Inputs

Mesh

formed positions of Gaussian primitives [38, 4]. Consequently, these methods require continuous supervision throughout the entire motion, which limits their ability to generate surface meshes in unseen states. In general, introducing 3DGS as a ready-to-use technique into articulated object surface reconstruction and generation is still not feasible and remains a challenging task.

In this paper, to address the aforementioned issues and make full advantage of 3DGS, we propose REArtGS , a novel approach that R econstructs and g E nerates high-quality texture-rich mesh surfaces for Art iculated objects via 3D G aussian S platting, only taking multi-view RGB images from two arbitrary states. Specifically, unlike most 3DGS methods based on the opacity field, which may lead to noisy surface extraction due to the non-strict linearity of the opacity field, we first introduce the Signed Distance Function (SDF) field to facilitate geometry learning. Then we propose a geometry constraint which encourages the SDF values of the Gaussian primitives approach zero when their opacity values reach the maximum , achieving unbiased guidance of the SDF field over the opacity field. In this way, we can take advantage of the strong linearity of the SDF field to explicitly establish a connection between Gaussian opacity field and the scene surface, which significantly enhances geometric learning and reduces artifacts.

Subsequently, we employ the optimized Gaussian primitives as an accurate geometry initialization for dynamic surface generation. Due to the absence of intermediate states, we leverage the kinematic structures of articulated objects to model time-continuous Gaussian deformable fields in an unsupervised fashion, and constrain the deformable fields using learnable motion parameters. Concretely, we propose a heuristic method for unsupervised part segmentation and formulate the deformable fields of dynamic parts via the motion parameters. Our REArtGS is evaluated on PartNet-Mobility [34] and AKB-48 [15] object repositories, ranging from synthetic to real-world data. Extensive experiments demonstrate that our REArtGS outperforms existing state-of-the-art methods in articulated object surface reconstruction and generation tasks, as shown in Fig. 1.

In summary, our main contributions can be summarized as follows: (1) We propose REArtGS, a novel framework introducing 3DGS to conduct high-quality textured surface reconstruction and time-continuous generation for articulated objects, only using multi-view images from two arbitrary states. (2) Our REArtGS exploits an unbiased SDF guidance for 3D Gaussian primitives to enhance geometric constraints for improving reconstruction quality, and also establishes the deformable fields constrained by kinematic structures of articulated objects to generate unseen states in an unsupervised manner. (3) We incorporate REArtGS into various scenes ranging from synthetic to the real-world data across many different articulation categories. The extensive experimental results demonstrate that our approach significantly outperforms SOTAs in both mesh reconstruction and generation tasks.

2 Related Work

2.1 Articulated Object Shape Reconstruction

Object surface reconstruction is a well-established problem for understanding the full geometric shape of objects. Some works introduce to encode continuous functions that model the objects using Signed Distance [24, 22], radiance [21, 27, 30] and occupancy [20]. To achieve both geometry and motion analysis in a single forward, Ditto [9] and REACTO [29] propose to generate shapes at unseen states from pair observations. Specifically, PARIS [14] alleviates the 3D data requirement

limitation and succeeds in reconstructing shape surface with only RGB images. Most recently, ArticulatedGS [7] and ArtGS [18] introduce 3DGS to achieve both the reconstruction and motion estimation of articulated objects. However, these methods still suffer from insufficient geometry constraints, which may lead to noisy surface mesh outputs. Therefore, our REArtGS aims to address these issues, and proposes geometric and motion constrained 3DGS for this task.

2.2 Surface Reconstruction with 3DGS

3D Gaussain Splatting has become increasingly popular technique for surface reconstruction in recent years [10, 11, 41, 26]. GOF [42] establishes opacity fields of 3D Gaussians using raytracing-based rendering, and extracts the surface meshes by the opacity level set. 2DGS [8] and PGSR [1] seek to transform 3D Gaussians into 2D flat representation, obtaining accurate normal distribution. Although improving the surface reconstruction quality, these methods still lack more reasonable geometry constraints. Several researchers attempt to integrate SDF representation with 3D Gaussians [39, 2, 35, 3], but these approaches commonly use SDF to regularize normals and guide the pruning of 3D Gaussians, without substantial optimization of Gaussian opacity fields. Meanwhile, some works conduct dynamic reconstruction using 3DGS. Deformable 3DGS proposes a deformation field to reconstruct dynamic scenes with 3D Gaussian primitives. 4DGS [4] leverages a spatial-temporal structure encoder and a multi-head Gaussian deformation decoder to derive the deformed 3D Gaussians at a given time. However, these methods rely on complete supervision of the motion process, limiting their capacity for generating unseen states. The similar issues also occur in DGMesh [13] and REACTO [29]. We provide more analysis and comparison of the related works in Appendix B.

unbias

State

s

Reconstruction

Generation

3D Gaussians

MLP

t

*

Dense Points

|∆

x

|>

GLYPH(cmap:d835)

Initial Dynamic Part

Final Dynamic Part

Gradient flow

Operation flow

Dynamic Part

Evaluation

Surface Generation for Unseen States

End State

Reconstruction

Rendering

Opacity

GLYPH(cmap:d835)

t

(depth)

t

*

SDF

TSDF fusion

Prismatic

m,

d

Revolute

o

r

,

q

Deformable Network

Rendering

Regularized Opacity Fields

3 Method

The overall framework of our REArtGS is illustrated in Fig. 2. Taking multi-view RGB images from two arbitrary states s = 0 , s = 1 of articulated objects, we aim to achieve high-quality textured mesh reconstruction and generation at any unseen states between s ∈ [0 , 1] .

We first introduce SDF representation and propose an unbiased SDF regularization to enhance the geometry constraints of 3D Gaussian primitives. In this manner, we improve the reconstruction quality and yield dense point clouds at state s = 0 , providing an accurate geometric prior for subsequent dynamic reconstruction. Then we establish time-continuous deformable fields for 3D Gaussian primitives constrained by the kinematic structures of articulated objects. Given any state s , we can derive the deformed position x s of Gaussian primitives through the deformation fields in an unsupervised fashion. The details of our approach are elaborated below.

3.1 Reconstruction with Unbiased SDF Guidance

To better preserve the geometric features of Gaussian primitives, we use the ray-tracing-based 3DGS following GOF, which evaluates rendering contribution of Gaussian primitives directly without the 3D-to-2D projection step. However, the Gaussian primitives still suffer from insufficient geometric constraints. Concretely, the opacity fields lack an explicit link with scene surface, which may lead to noisy outputs in surface reconstruction of articulated objects.

To address the limitation, we first introduce SDF representation to guide the geometry learning of Gaussian primitives. We utilize a Multi-layer Perceptrons (MLP) with 8 hidden layers to learn the SDF values for spatial position inputs, and the scene surface can be represented by the zeroset:

Following Neus [32], we can derive the opacity ˆ σ for the center x i of Gaussian G i from learned SDF values:

t

*

t

*

GLYPH(cmap:d835)

GLYPH(cmap:d835)

(b) w/ unbiased SDF regularization

(a) w/o unbiased SDF regularization

GLYPH(cmap:d835)

GLYPH(cmap:d835)

Absolute value of SDF

GLYPH(cmap:d835)

GLYPH(cmap:d835)

Absolute value of SDF

：

where Φ denotes a Sigmoid function. f ( x i +1 ) is approximated as:

where ∆ x = RS r . R ∈ R 3 × 3 is the rotation matrix derived from Gaussian's quaternion, and S ∈ R 3 × 3 is the diagonal matrix of Gaussian's scaling. r is the normalized ray direction. Nevertheless, the SDF-opacity conversion is not directly applicable to the α blending rendering. This is because each 3D Gaussian primitive models a local density distribution, while Eq. 2 only maps the global positions to their opacity values and neglects the local properties of Gaussian primitives. Therefore, we define the opacity of Gaussian primitive combining the rendering contribution ε proposed in GOF as:

where o L , r L are the camera center and incident ray direction represented in the local coordinate system of Gaussian G i , which can be acquired by local scaling matrix and rotation matrix of G i . The maximum value of ε ( G i ) occurs at ray depth t ∗ , expressed as follows:

The rendering equation based on σ is represented as:

where N is the number of Gaussian primitives involved in α -blending and c i is the color modeled with spherical harmonics. To leverage SDF to constrain the geometric learning of Gaussian primitives, we first introduce a bell-shaped function Φ k to modulate the transformation from SDF to opacity and replace the original Sigmoid activation function Φ in Eq. 2, formulated as:

where k is a learnable parameter that adjusts the function shape and we initially set it to 0.1. Intuitively, the Gaussian primitive closer to the surface has a higher opacity value.

However, since ˆ σ is determined solely by the Gaussian's center, a non-alignment still exists between the maximum points of ˆ σ and ε , which can be formulated as:

To mitigate this bias, we propose an unbiased regularization for the SDF value at depth t ∗ as following:

where o , r are the camera center and incident ray direction. Through the unbiased regularization, we encourage that when the rendering contribution of a Gaussian primitive reaches its maximum value, the corresponding spatial position is close to the scene surface, as shown in Fig. 3. In this manner, the SDF representation is able to regularize the opacity fields without bias and facilitate more reasonable distribution of Gaussian primitives over scene surface. We provide more detailed elaboration and proof in the Appendix C.1.

3.2 Mesh Generation with Motion Constraints

Due to the explicit representation of Gaussian primitives, we can acquire accurate dense point clouds from reconstruction, serving as initial geometry for mesh generation. Then we establish timecontinuous deformable fields for 3D Gaussians to infer the deformed position x s at state s . However, it is challenging to optimize the deformable fields for Gaussian primitives only with the supervision of two states. Accordingly, we exploit the kinematic structures of articulated objects to constrain the Gaussian deformation field. We first assume that the articulated motion only includes rotation and translation following PARIS. Then we employ a heuristic method to efficiently predict the joint type through movement trend of points, which is elaborated in the Appendix C.3. For rotation joints, our learning objective is to determine their rotation axis and rotation angle. Specifically, we take the pivot point o r ∈ R 3 of the rotation axis and a normalized quaternion q ∈ R 4 as learnable parameters, where the quaternion can be decoupled into the rotation axis a ∈ R 3 and rotation angle θ . For prismatic joints, our learning objective is to determine their translation directions d and translation distances m . Similarly, we take the unit vector d ∈ R 3 representing the translation direction and the translation distance m as learnable parameters.

Subsequently, we formulate the deformable fields through a canonical state. Given the rotation motion SO (3) ∈ R 3 × 3 , we define its canonical state as s ∗ = 0 . 5 and the rotation angle of state s can be expresses as:

where s ∈ [0 , 1] and θ ∈ [ -π/ 2 , π/ 2] . The angle-bounded parameterization is able to prevent the singularity in exponential coordinates when ∥ θ ∥ > π . Using Rodrigues' rotation formula [25], the deformed position x s can be derived as following:

where K is a skew-symmetric matrix formed by the rotation rotation axis a and I ∈ R 3 × 3 is a unit matrix. Meanwhile, we align the local quaternions of 3D Gaussian primitives after rotation through quaternion multiplication.

For the prismatic motion m · d ∈ R 3 , we take advantage of the vector space structure of Euclidean geometry by setting s ∗ = 0 . The deformed position x s can be naturally acquired by linear interpolation, formulated as:

unlike rotation motion, this parameterization avoids singularities due to the flat Riemannian structure of R 3 .

However, the deformable fields should be applied exclusively to movable Gaussian primitives. Consequently, the Gaussian primitives need to be segmented into dynamic and static parts. To address this challenge, we propose an unsupervised method for dynamic part segmentation. We first only input the multi-view images at the end state during warm-up training iterations, and then perform an initialization segmentation of the dynamic part. The segmentation criterion for the initial dynamic

USB

Laptop

Washer

PARIS

GOF

Fridge

Input state

GT

Ours

Ours

(textured)

Foldchair

ArtGS

imation

components is represented as: | ∆ x | > β , where | ∆ x | is the L1 distance of spatial position variation during the warm-up training, and β is its average value. Furthermore, the spatial transformation of the dynamic part should align with the learnable motion parameters. Hence the dynamic part among the Gaussian primitives is re-evaluated at certain intervals of iterations, where the criterion for revolute or prismatic joints is represented as:

where ˆ θ , ˆ m are the rotation angle around axis a and the translation distance respectively, φ is the tolerance threshold which decreases as the number of iterations K increases.

Moreover, our method can be conveniently transferred to multi-part articulated objects, through sequentially learning the segmentation masks and motions of each individual part. Please refer to our Appendix C.4 for more details.

3.3 Optimization and Textured Mesh Extraction

Since the shapes of Gaussian primitives are not limited, the normal distributions of Gaussian primitives are difficult to estimate accurately. However, Gaussian's normals should be parallel to the gradients of SDF in an ideal case. We consequently introduce additional regularization to regularize the normals of Gaussian primitives by SDF representation, expressed as follows:

where n i represents the normal of Gaussian G i , which is calculated following GOF:

where R is the rotation matrix encoded by Gaussian's quaternion and S is the scaling matrix. We also employ the Eikonal regularization term [5] to encourage SDF gradient direction perpendicular to the surface, represented as:

The overall training objective L is formulated as follows:

where λ is the weight of regularization and L d is the depth distortion loss following [8]. L c is defined as:

where L D-SSIM is proposed in [10] and L 1 is the L1 norm of the pixel loss.

Once the optimization converges, we adopt the TSDF fusion algorithm[23] to extract the textured mesh from Gaussian primitives. We utilize the α -blending rendering pipeline to obtain depth, opacity and RGB renderings for training views. Then we integrate these images into a voxel block grid (VBG) and extract a triangle mesh from the VBG. The textures of surface meshes can be efficiently obtained leveraging the spherical harmonics and opacity of Gaussian primitives. Please refer to our Appendix C.3 for a more detailed illustration.

Metrics

Method

Stapler

USB

Scissor

Fridge

Foldchair

Washer

Blade

Laptop

Oven

Storage

Mean

CD(ws)

↓

A-SDF [22]

14.19

7.14

10.61

13.71

40.85

12.50

3.31

2.11

21.37

22.57

14.84

Ditto [9]

2.38

2.09

1.70

2.16

6.80

7.29

42.04

0.31

2.51

3.91

7.19

PARIS [14]

0.96

1.80

0.30

2.68

0.42

18.31

0.46

0.25

6.07

8.12

3.94

GOF [42]

1.51

8.09

0.36

1.51

0.53

17.35

0.89

0.84

19.81

10.04

6.09

ArtGS

∗

[18]

2.77

1.36

0.75

2.01

0.41

20.59

0.63

0.99

9.01

9.00

4.75

REArtGS (Ours)

3.47

0.75

0.29

1.50

0.40

12.20

0.72

0.53

8.89

8.27

3.79

CD(rs)

↓

A-SDF [24]

2.140

2.478

1.805

1.801

2.080

1.590

5.630

2.418

2.751

4.077

2.677

Ditto [9]

2.874

3.049

2.926

1.550

0.925

1.428

5.792

1.184

1.296

2.837

2.386

PARIS [14]

2.077

3.812

2.807

0.370

1.209

3.869

2.855

1.025

2.873

2.903

2.380

GOF [42]

2.510

1.736

2.304

2.230

2.787

5.725

2.111

0.961

3.080

2.812

2.626

ArtGS

∗

[18]

2.949

1.704

2.438

0.725

0.615

2.874

2.031

1.182

1.148

1.157

1.682

REArtGS (Ours)

2.186

1.433

2.291

0.475

0.018

1.204

2.596

0.038

0.784

1.330

1.236

F1

↑

A-SDF [24]

0.041

0.035

0.094

0.019

0.053

0.046

0.224

0.001

0.010

0.015

0.054

Ditto [9]

0.197

0.181

0.275

0.114

0.352

0.059

0.107

0.366

0.052

0.030

0.173

PARIS [14]

0.240

0.151

0.343

0.091

0.429

0.024

0.396

0.533

0.031

0.033

0.227

GOF [42]

0.217

0.189

0.614

0.173

0.480

0.087

0.386

0.303

0.049

0.036

0.253

ArtGS

∗

[18]

0.251

0.209

0.438

0.118

0.447

0.053

0.408

0.294

0.056

0.042

0.232

REArtGS (Ours)

0.256

0.307

0.598

0.165

0.502

0.069

0.488

0.419

0.065

0.066

0.294

EMD

↓

A-SDF [24]

0.755

1.113

0.952

0.945

1.020

0.982

1.763

1.039

1.174

1.258

1.100

Ditto [9]

1.724

1.308

1.212

0.619

0.935

0.841

1.996

0.417

0.852

0.970

1.087

PARIS [14]

1.197

1.381

0.637

0.426

0.778

1.660

1.823

0.706

1.200

1.046

1.085

GOF [42]

1.643

1.815

1.075

1.055

1.181

1.692

1.237

0.394

1.241

0.959

1.229

ArtGS

∗

[18]

1.921

0.677

0.934

0.637

0.506

1.154

1.131

0.502

1.006

0.787

0.926

REArtGS (Ours)

1.060

0.847

1.078

0.485

0.097

0.777

1.112

0.111

0.627

0.755

0.695

Ours

PARIS

D-3DGS

Ours

(textured)

Ours

(textured)

Ours

PARIS

D-3DGS

4 Experiments

4.1 Experimental Setting

Datasets . To evaluate the reconstruction quality of our method, we conduct surface reconstruction experiments on synthetic dataset PartNet-Mobility [34] and real-world dataset AKB-48 [15], an extremely rich repository of real-world articulated objects which exhibit a wide variety of geometries and textures. Please see the Appendix D.1 for more details.

Metrics . Chamfer Distance (CD), F1-score and Earth Mover's Distance (EMD) [43] are usually adopted as evaluation metrics for surface quality. However, these methods typically focus on the whole surface, endowing the unseen areas (such as the bottom of the object) with considerable weights during evaluation. This is unreasonable since we tend to be more concerned with the upper hemisphere regions of articulated objects. Therefore, we sample point clouds from the fragments between the rays cast by test cameras and the surface mesh to calculate EMD and CD, defined as CD (rs). We also evaluate the F1-score and CD for the whole surface, defined as CD (ws), to comprehensively evaluate the surface quality. Note that the CD values are multiplied by 1000 and the distance threshold of F1-score is set to 0.4. The calculation of these metrics can be referred to in our Appendix D.4.

All the experiments are conducted on a single RTX 4090 GPU. We also provide detailed implementation of our approach and baselines in the Appendix D.2.

4.2 Mesh Reconstruction Performance

We report the quantitative results of mesh reconstruction in Table. 1 and qualitative results are shown in Fig. 4. Note that we implement ArtGS without depth supervision for a fair comparison. As can be observed, our REArtGS outperforms state-of-the-art approaches in the mean across all metrics with 3.79 , 1.236 , 0.294 and 0.695 respectively. Compared to A-SDF [22] and Ditto [9] that exploits 3D inputs for articulated object surface reconstruction, our method still achieves higher reconstruction quality in the majority of categories. In terms of PARIS, GOF and ArtGS, our approach exhibits significantly smoother and clearer surfaces as shown in Fig. 4, benefiting from our enhanced geometric constraints.

4.3 Mesh Generation Performance

We report the quantitative results of mesh generation in Table. 2 and present the qualitative results Fig. 5. Please refer to our Appendix for more qualitative results. Our REArtGS achieves the best mean results across all metrics with 4.10 , 1.063 , 0.298 and 0.670 . Compared to PARIS, our method exhibits smoother surface generation results, as shown in Fig. 5. It is mainly because PARIS lacks a reasonable geometry initiation and directly utilizes the composite rendering, leading to the spatial overlap between the neural radiation fields of the start and end states. It can be also observed that

our method is superior to ArtGS on most categories, as shown in Fig. 10 in the Appendix. This is primarily attributed to our high-quality geometry initialization from the reconstruction stage, as well as the motion-constrained deformable fields to yield dynamic generation.

Besides, we provide the part segmentation and joint parameter estimation results in the Appendix. D.5 and D.6 respectively.

4.4 Ablation Studies

We conduct an ablation of the unbiased SDF guidance on PartNet-Mobility dataset to validate its effectiveness. The ablation setting and results are reported in Table. 3. The quantitative results prove that both SDF guidance and the unbiased regularization of SDF indeed improve surface reconstruction quality. This is primarily because the SDF representation establishes explicit geometric association between the opacity fields and scene surface through Φ k , and the unbiased SDF regularization further optimizes the distribution of Gaussian opacity fields.

We also conduct an ablation study of the motion constraints on PartNet-Mobility dataset and present the quantitative results in Table. 4. For the baseline without mo-

tion constraints, we employ three MLPs to learn the variations of spatial positions, scales, and quaternions respectively for Gaussian primitives, which is similar to D-3DGS. The significantly superior results demonstrate the effectiveness of the motion constraints.

Reconstruction

Generation

Inputs

9 999919

9 e9 9

4.5 Generalization to the Real World

We conduct reconstruction and generation experiments in the real world to investigate the generalization capacity of our REArtGS. We report the quantitative results comparing with PARIS and ArtGS in Table. 5, and present the qualitative results in Fig. 6. Our approach significantly outperforms PARIS and ArtGS in mean performance across all metrics. This demonstrates that our method exhibits strong generalization capability for real-world objects and enables high-quality surface reconstruction and generation as shown in Fig. 6.

5 Conclusion and Future Work

We propose REArtGS, a novel framework that achieves high-quality textured mesh reconstruction and dynamic generation of articulated objects using only RGB views of two arbitrary states. We propose an unbiased SDF guidance to regularize Gaussian opacity fields and model the Gaussian deformation fields constrained by kinematic structures for generating meshes of unseen states. Experiments show the superior performance of our method on both synthetic and real-world data.

The limitations of REArtGS lie on the requirement of camera pose prior and challenges of objects with transparent materials. Future works will introduce relative pose estimation to alleviate the dependence on camera poses and employ physically-based networks to model the transparent materials.

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Appendix

A Overview

In the appendix, we first provide a detailed comparison highlighting our advantages over related works in Sec. B. Then we elaborately extend our method in Sec. C from the following four aspects: unbiased SDF guidance theory, textured mesh extraction, joint type prediction, and multi-joint mesh generation. In addition, more experimental details and results are presented in Sec.D. Finally, we provide our codes and video in Sec. E.

B Related Work, Extended

Recently several methods also attempt to integrate SDF representaion with 3D Gaussian primitives [39, 2, 35, 3, 19] and have improved surface reconstruction quality. However, most approaches are limited to normal regularization and pruning strategy of Gaussian primitives through SDF representation [39, 2, 35, 3], and the opacity of Gaussian primitives holds a more essential role in the α -blending. In contrast, we leverage the unbiased SDF guidance to regularize Gaussian opacity fields, achieving substantial geometric constraints and enhancing geometric optimization. 3DGSR [19] directly obtains the opacity of Gaussian primitives through SDF-opacity conversion proposed in Neus [32] and also employs a bell-shaped activation function to control the distribution of SDF-opacity. Nevertheless, since the SDF network simply utilizes Gaussian's center as input, 3DGSR ignores the local features of Gaussian primitives. In comparison, our approach combines the local rendering contributions with SDF-opacity conversation results as Gaussian's opacity values, yielding more reasonable distribution of Gaussian opacity fields.

Most recently, ArtGS [18] also proposes a mesh reconstruction method for articulated objects. Although demonstrating its effectiveness and robustness for mesh reconstruction, it relies on depth inputs, limiting its performance when the 3D supervision is unavailable. Moreover, it still lacks sufficient geometry constraints, leading to noisy results occasionally. In contrast, our REArtGS only requires 2D RGB images as inputs and introduces additional geometry constraints, improving the mesh reconstruction quality.

C Methodology, Extended

C.1 Unbiased SDF Guidance for Gaussian Opacity Fields, Extended

As defined in the main draft, the opacity of Gaussian primitives combines with the rendering contribution ε , formulated as:

where x L = o L + t r L . x L , o L , r L represent the spatial position, camera center, and ray direction of Gaussian's local coordinate system respectively. ε attain its maximum value when t = t ∗ as mentioned in the main draft. ˆ σ i can be derived from Φ k and the SDF-opacity conversation, where Φ k is also defined in the main draft, formulated as:

Although the composite function ˆ σ ( f ( x )) attains its maximum when f ( x ) = 0 , a non-alignment still exists between the maximum points, formulated as:

This mismatch leads to a spatial deviation between ˆ σ and ε proposed in the main draft:

t

*

t

GLYPH(cmap:d835)

f

(

GLYPH(cmap:d835)

)

(a)

(b)

As illustrated in the main draft (Fig. 3), the deviation t bias results in a disordered distribution of the Gaussian opacity fields. We also present their distributions without regularization for intuitive illustration in Fig. 7.

To tackle the deviation, we propose an unbiased SDF regularization L unbias defined in the main draft:

Wepresent the theorem and proof that L unbias is capable of eliminating the deviation t bias in Theorem. 1 and Proof. 1 below.

Theorem 1 Let f : R 3 → R be a multilayer perceptron (MLP) with smooth activation functions, and let the spatial position be defined as x ( t ) = o + t r with camera center o ∈ R 3 and ray direction r ∈ R 3 . Let argmax t ( ε ) = t ∗ . Consider the activation function Φ k : R → R defined by:

If lim t → t ∗ f ( x ( t )) = 0 , then the composite function Φ k ◦ f ◦ x ( t ) attains its maximum at t = t ∗ , i.e., ∥ ∥ ∥ ∥ argmax t ( ˆ σ ) -argmax t ( ε ) ∥ ∥ ∥ ∥ = 0 .

Proof 1 Define z ( t ) := f ( x ( t )) . Compute the first derivative:

Setting d Φ k dz = 0 gives the unique critical point at z = 0 .

Evaluate the second derivative at z = 0 :

For z > 0 , d Φ k dz < 0 ; for z < 0 , d Φ k dz > 0 . This proves that the maximum value of Φ k is attained when z ( t ) = 0 . Given that lim t → t ∗ f ( x ( t )) = 0 and argmax t ( ε ) = t ∗ , it can be proved that

C.2 Textured Mesh Extraction, Extended

In this paper, we employ TSDF fusion [23] to extract textured meshes from the regularized Gaussian opacity fields. We first obtain multi-view RGB renderings through α -blending, where the opacity σ and view-dependent spherical harmonics (SH) coefficients jointly modulate the color accumulation. These per-pixel blended colors are then projected into the voxel block grid (VBG), ensuring that high-opacity regions dominate the color field, while low-opacity areas are suppressed. Meanwhile, we acquire the multi-view depth renderings via α -blending and conduct filtration with a minimum opacity threshold 0.5 as well as a maximum depth threshold 5.0. Then the filtered depth renderings are similarly integrated into the VBG. After multi-view fusion, a triangle mesh is extracted via Marching Cubes and vertex colors are directly sampled from the VBG's color fields. This process effectively bakes the fused texture into the extracted mesh.

In the early experiments, we also attempt to use SDF combined with Marching Tetrahedra to extract surface meshes, but this leads to a decline in surface quality. It is mainly because the SDF-opacity conversion only uses the center positions of Gaussian primitives for approximate calculation during training. Therefore, the SDF network only learns the spatial information at Gaussian's center, making it challenging to handle dense sampling points in mesh extraction. In contrast, the α -blending in TSDF fusion incorporates the local information of the Gaussian primitives via Gaussian opacity fields, yielding higher-quality mesh results.

C.3 Joint Type Prediction

Given an articulated object with unknown kinematic structure, we first obtain two coarse point clouds P 0 and P 1 using the multi-view images from two states s 0 and s 1 respectively, based on the proposed pipeline in Sec. 3.1. Note that it only takes few minutes since the training process only undergoes 3000 iterations. Then we use the Farthest Point Sampling (FPS) to produce the sparse point clouds P ∗ 0 and P ∗ 1 for P 0 and P 1 . For each point p i ∈ P ∗ 0 , we identify a point q i ∈ P ∗ 1 closest to it. Subsequently, we filter out the dynamic points D with significant displacement through the distance between p and q , formulated as:

where τ is the dynamic threshold.

After that, we determine the motion type of the dynamic point set D through the variance σ D of the displacement direction angle.

where v i = p i -q i and v mean = 1 | D | ∑ v i . Then we use a threshold σ rot to predict the motion type, that is, the joint type of the articulated object. Concretely, we determine the dynamic part as prismatic joint if σ D ≤ σ rot and as rotation joint in unsatisfactory cases.

C.4 Mesh Generation of Multi-Part Articulated Object

Mesh generation of multi-part articulated objects can be treated as a straightforward extension of the two-part task for our approach. We can generate meshes for multi-part objects in unseen states through a sequentially learning strategy. Specifically, given an articulated object with k dynamic parts ω d k , we start with fixing parts { ω d 2 , ..., ω d k } except for the dynamic part ω d 1 , and the object can be decomposed the into dynamic part ω d 1 and static part ω s (all the remaining points). After learning the motion and segmentation mask for ω d 1 , we sequentially perceive dynamic part ω d 2 , while the static part ω s can be updated as: ω s = ˆ ω d 2 -ω d 1 , where ˆ ω d 2 denotes the remaining points except ω d 2 . By parity of reasoning, we achieve learning all motions and segmentation masks for the k parts, and the mesh generation results for any unseen state can be obtained using the proposed pipeline in Sec. 3.2.

D Experiments, Extended

D.1 Dataset

The PartNet-Mobility dataset [34] is a synthetic dataset comprising articulated objects across 46 categories, characterized by a diverse scale and geometry. Following PARIS, we selected the same 10 categories for our experiments. Each category comprises 64 to 100 RGB views at two arbitrary states, which are sampled randomly from their upper hemispheres. AKB-48 [15] is a real-world dataset containing an extensive collection of articulated objects scanned in real indoor scenes. We select 6 categories with both two-part and multi-part objects and sample 100 RGB views at two arbitrary states for each object.

D.2 Implementation

All the experiments are conducted on a single RTX 4090 GPU. Our approach constitutes a two-stage procedure to perform surface reconstruction and generation respectively. For the reconstruction stage, we train the 3D Gaussian primitives with SDF network for 3,1000 iterations, which includes an warm-up phase consisting of 1,000 iterations for SDF initial learning. Then we employ the unbiased SDF regularization in 1,500 to 1,6000 iterations, use the SDF-normal regularization and Eikonal regularization during the entire training process. We perform the densification and pruning for 3D Gaussian primitives from 1,500 iterations to 16,000 iterations. The training time of reconstruction stage is about 60 minutes.

For the generation stage, we leverage the 3D Gaussian primitives optimized from the reconstruction stage as initialization. We also set a warm-up phase with 3,000 iterations to train 3D Gaussian primitives only using RGB images from the end state. Then we start to evaluate dynamic parts of the articulated object and apply the deformation fields to them. The total number of training iterations during the generation stage is 30,000. The training time of the generation stage is about 10 minutes and the total training time for surface generation from scratch is approximately 70 minutes.

D.3 Baselines

For the surface reconstruction experiments of articulated objects, we define the start states of articulated objects as reconstruction targets, and compare the reconstruction results with existing state-of-the-art methods: A-SDF [22], Ditto [28], PARIS [14], GOF [42] and ArtGS [18]. Note that we implement ArtGS without depth supervision for a fair comparison. In particular, A-SDF uses sampled point clouds as inputs and Ditto requires both point clouds and united robotics description format (URDF) files of articulated objects. Following PARIS, we train A-SDF on the 10 categories of the PartNet-Mobility dataset and employ the pretrained weights of Ditto to conduct the comparison.

As for the surface generation experiments, we compare the surface generation results with existing state-of-the-art methods: A-SDF, PARIS, Deformable-3DGS (D-3DGS) [38] and ArtGS [18]. Since the ground truth point clouds of unseen states are unavailable, we take the end states of the articulated objects as generation targets and generate surface meshes from the canonical states. Besides, all the methods employ point clouds (A-SDF) or RGB images (PARIS, D-3DGS, ArtGS, ours) from both start and end states as training inputs.

D.4 Metrics, Extended

We employ CD (ws), CD (rs), F1-score, EMD as the metrics of surface quality. We present the detailed calculation below.

The CD (ws) is defined as the mean of the shortest distance between two point clouds, which can be formulated as following:

where N 1 , N 2 represent the point number of output and ground truth respectively. The CD (rs) is calculated by the sampled point clouds from the fragments between the rays cast by test cameras and the surface mesh, shown as following:

where N s 1 , N s 2 denote the point number of sampled output and sampled ground truth respectively. Similarly, we adopt the same point cloud sampling method to calculate EMD score [43], which can be formulated as:

where ϕ is a bijection to identify the optimal correspondence between two point clouds.

End State

Ours

GT

Ours

GT

F1-score is the harmonic mean between the precision P ( d ) and recall R ( d ) with distance threshold d = 0 . 4 in this paper, formulated as:

D.5 Part Segmentation

We present the quantitative results of part reconstruction quality on PartNet-Mobility dataset in Table. 6, and provide the qualitative results of part segmentation in Fig. 8. Our method achieves the best mean results on both dynamic and static parts. It can be also observed that our REArtGS

successfully acquires accurate part decompositions, as shown in Fig. 8. This demonstrates that our approach is capable of perceiving motion, and enables accurate part segmentation using the simple yet effective heuristic method.

D.6 Joint Parameter Estimation

We also present the joint parameter estimation quantitative results in Table. 7 and qualitative results in Fig. 9 to evaluate motion learning. Following PARIS [14], we compute the orientation error (Ang Err) between the predicted axis direction ( a , d in our method) and the ground truth, and evaluate the position error (Pos Err) for revolute joints through the position difference of the pivot point ( o r in our method). It can be observed that our method achieves the best results, which demonstrates our superiority in motion learning and further demonstrates the high fidelity of our dynamic generation.

Fridge

Scissor

Stapler

Storage

USB

D.7 Mesh Generation, Extended

To present our generation results more comprehensively, we provide the qualitative results in Fig. 10 corresponding to Table. 2 in the main draft. We generate surface meshes of the end state from the canonical state using the Gaussian deformation fields. It can be observed that our generated results still maintain smooth and high-quality surfaces, which proves the effectiveness of our method.

D.8 Computational Efficiency Analysis for Mesh Generation

We present the detailed training time in Sec. D.2, and also provide the qualitative inference time for mesh generation in Table. 8. It can be observed that our method achieves the highest computational

ArtGS

Blade

Scissor

Oven

Stapler

Storage

PARIS

D-3DGS

Ours

Ours

(textured)

GT

L

77777

efficiency. This is mainly because of our efficient design for the learnable motion parameters, which obviates inference of deep networks.

D.9 Ablation Study, Extended

To evaluate the effectiveness of the canonical state selecting, we compare the surface generation quality under three settings of canonical state 0.0, 0.5, and 1.0 on PartNet-Mobility dataset. The quantitative results can be found in Table. 9. For prismatic joints, the surface reconstruction quality attains the highest when the canonical state is 0.0. It is mainly because the linear interpolation can be performed more stably taking advantage of the vector space structure of Euclidean geometry.

As for revolute joints, setting canonical state 0.5 achieves the best surface generation quality. This can be mainly attributed to the fact that canonical state 0.5 leads to the angle-bounded parameterization θ ∈ [ -π/ 2 , π/ 2] , which can prevent the singularity in exponential coordinates when ∥ θ ∥ > π .

E Reproducibility and Video

We provide our codes in https://github.com/wd-ustc-cs/REArtGS and present a video to show more visualization results in https://sites.google.com/view/reartgs/home .

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper purposed reartgs with can reconstruct the articulated object with given two states, and generate the internal state between the given training state. The method first extends GOF, a volume rendering based GS method, with the SDF representation to enhance the reconstruction quality. Then a deformable field is applied to the GS to predict the deformed GS for rendering.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:​​\n\n1. Motivation is compelling. Formulating reconstruction for articulated objects (dynamic reconstruction) remains an open challenge for 3DGS. \n2. The performance surpasses that of the baselines.\n\n​​Weaknesses:​​\n1. The motivation to apply an 8 layer mlp for SDF prediction is unclear. The author can provide more details about it. For example, the sdf can be a property on each GS while there is no need an implicit function to predict it as f(x) = sdf.\n2. The definition of $x_{i+1}$ is missing. It might be the index of the sorted GS for each pixel.\n2. Section 3.2 (\"Mesh Generation with Motion Constraints\") lacks clarity. Despite its title, the section does not discuss mesh generation. It describes using point clouds (based on the center of GS) to compute deformable fields, while the definition of the deformation network is missing, it might be (q,d,s)=g(x).\n3. The initialization of the gs is missing. In line 189, the proposed method uses s=0.5 as the init states, and the images from s=1 used for warning up. To this end, the initialization position of the points will be important. Based on the description in line 111, the reconstruction is  built on s=0. It is confusing.\n3. Since the proposed method extends GOF with SDF representation, reconstruction results on standard datasets like Tanks and Temples, DTU, and DeepDeform should be reported.\n\nQUESTIONS:\n1. The author should provide more detail mentioned in the weaknesses. How to define the defomable network, and more details about generation mesh with different states.\n2. More results on reconstruction datasets should be provided. Since the proposed method extends GOF with SDF, the author should also clarify why introducing SDF is important for this task, otherwise the comparison with the previous reconstruction methods is needed.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper tackles an interesting task: generation articulatable objects from multiview observations collected in two distinct states of the object articulation. \nIt improves upon prior work PARIS and ArtGS. The former uses a NeRF representation, and the latter uses Gsplats but requires RGB-D images whereas the proposed methos REArtGS needs only RGB images.\nThe method differs from prior work by providing a much more accurate mesh reconstruction in the initial state by adopting ray-traced GS formulation (Gaussian Opacity Field) and connecting the that opacity with a jointly learned SDF. \nSecondly, during the articulation learning step, it also incorporated heuristic, but effective, rules to correctly determine the moving parts and their articulation type. \nThe paper showed impressive improvements compared to prior work in both synthetic Part-Net data and real-world captured data.\n\nSTRENGTHS AND WEAKNESSES:\n# Strength\n- The paper is clearly written. Moreover, the authors ensure the reproducibility of their implementation by stating that core modules and generation inference codes are provided in the supplementary materials.\n- The paper presents convincing visual and quantitative improvements upon prior art. Qualitatively, REArtGS demonstrates \"significantly smoother and clearer surfaces\" for reconstruction\n\n# Weaknesses\n- I find the reporting of the tables and figures curiously unmatched. Why do the figures report D-3DGS while the tables don't even include this method? The close contender ArtGS is not shown only one figure in the Appendix.\n- Since the paper mentioned multi-part and outlined a solution in the appendix, I would like to see a couple of examples. Otherwise, the paper feels rather incomplete.\n- Motivation of using 3DGS (see my questions below). \n- The training time seems rather long - 95 minutes in total. There is no reference to the training time of prior work.\n\n# Minor weaknesses\n- For texture extraction, I'm curious if the texture considers only SH0 and discard the rest of the SH degrees? If so, it might be better to mention if the reconstruction is done with only sh0.\n\nQUESTIONS:\nI'm curious what is the fundamental difference between a NeRF-based approach like PARIS and 3DGS-based approach, such as REArtGS? \nThe paper focused on introducing geometry regularization specifically for 3DGS. But what benefit do we get by using 3DGS? If I used NeuS instead of NeRF in PARIS (thus no need these additional techniques introduced in this paper) and if I applied similar deformation optimization, would I get similar result? \nIs there any gains in terms of training speed, texture quality etc?\n\nSecondly, I couldn't find how many images are required for optimizations in the two stages.\n\nLastly, how does the method compare with other work in terms of training time?\n\nLIMITATIONS:\nI think a key limitation, not restricted to this paper alone, is potential failure when the observations of the initial state is incomplete. \nIn this case, one may think of combining the second observed state and jointly optimize the reconstruction and deformation.\nIt might be worthy mentioning this in the paper.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims to solve the problems of (1) 3D textured surface reconstruction of articulated objects from multiview RGB images (2) generating dynamic unseen motion states from a given start state to an end state both of which have multiview RGB images. The paper proposes a novel approach using unbiased Signed Distance Field (SDF) guidance for regularization of Gaussian opacity fields for solving the problem in (1). Reconstruction and Motion Generation results are demonstrated on a synthetic dataset and a real world dataset.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n+ The paper attempts to solve a challenging intermediate state generation problem given multiview images of a only start and end state of an articulated object, and achieves geometry and texture generation of reasonably good quality on real world datasets and on multi-part articulated objects.\n+ The paper is well-written and easy to follow. \n+ The geometry reconstruction results on USB and Fridge in Fig.4 and the generation results in Fig. 5 clearly denote better geometric quality.\n\nWeaknesses :\n\n1. Originality : This paper reuses concepts and ideas from NeuS (Eq. 2), GOF (Eq. 3-5) and 3DGSR (bell-shaped function similar to Eq. 6).  SDF guidance for 3D Gaussian primitives has been done in 3DGSR. \n\n2. Results : \n- Fig 4 – The reconstruction quality of Washer and Laptop does not seem to be very good. \n- Missing Qualitative comparison with state-of-the-art methods on real-world dataset AKB-48. \n- Qualitative comparison with Ditto missing in Fig 4. \n- Qualitative ablation results needed to understand the significance of the proposed geometric constraints. \n- Quantitative / qualitative comparison of 3D reconstruction not done with 3DGSR, which also uses SDF along with 3DGS for unified surface and appearance modeling, and uses a similar bell-shaped function. \n \n\n3. Writing Ambiguity : Lines 94-95 : Although improving the surface reconstruction quality, these methods still lack “more reasonable geometry constraints”. \n\n \n\n4. Undefined notations: \n\n- $x_{i+1}$ in Eq. 2 not defined. \n\n - $x_L$ and $t$ in Eq. 3 are not defined.\n\nQUESTIONS:\n1. What is $x_{i+1}$ in Eq. 2 ? In NeuS i refes to the index of the sampled point along the ray, in this case the how is the (i+1)th gaussian defined with respect to the ith Gaussian? \n\n2. How many input views are used for each of the two states (both source and target) ?  \n\n3. Lines 94-95 : Although improving the surface reconstruction quality, these methods still lack “more reasonable geometry constraints”. Exactly what is unrealistic or unreasonable about the geometric constraints in GOF, 2DGS and PGSR that this paper has improved?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

comments: SUMMARY:
The authors propose a solution to surface reconstruction and generation for articulated objects by coupling geometric and motion constraints to 3D Gaussian primitives.
Given multi-view RGB images of arbitrary two states of articulated objects, the method utilizes an unbiased Signed Distance Field (SDF) guidance to regularize Gaussian opacity fields. 
It then establishes deformable fields for 3D Gaussians, constrained by the kinematic structures of articulated objects, thereby achieving unsupervised generation of surface meshes in unseen states.

STRENGTHS AND WEAKNESSES:
**Strength**
- The proposed method utilizes unbiased SDF guidance for 3D Gaussian primitives to enhance geometric constraints, thereby improving the reconstruction quality.
- It learns deformable fields and generates unseen states by constraining the kinematic structures of articulated objects.
- The method is evaluated on different synthetic and real datasets.

**Weaknesses**
- The method requires access to posed images as input.

QUESTIONS:
What is the effect of the diversity of the two views on surface reconstruction quality? Does the method require the two surfaces to be very distinct, and would the surface reconstruction suffer if the images are close?

Can the method take more than two input images? Let's say $s_0=0$, $s_1=0.1$ and $s_2=1.0$, would that help the reconstruction quality or would it have any effect?

LIMITATIONS:
- The method requires accurate camera poses.
- Rendering of objects with transparent materials and non-Lambertian surfaces are challenging.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper propose REArtGS, a method for reconstructing articulated objects from multi-view RGB images captured at two different states. The approach uses 3D Gaussian Splatting enhanced with unbiased-SDF regularization to generate high-quality surface meshes.\n\nThe key innovation is an \"unbiased SDF guidance\" that regularizes the Gaussian opacity fields, addressing insufficient 3D constraints in the reconstruction. The method learns kinematic parameters (translation and rotation) during optimization, enabling it to estimate the articulation model and generate intermediate poses between the two input states. Experiments on synthetic PartNet-Mobility and real-world AKB-48 datasets show REArtGS outperforms existing methods like PARIS.\n\nSTRENGTHS AND WEAKNESSES:\n- REArtGS demonstrates superior performance over existing methods on standard benchmarks, outperforming approaches like DITTO and PARIS on PartNet-Mobility and AKB-48 datasets. \n- The method produces high-quality textured meshes and shows good real-world generalization, even in cluttered environments. \n- Ablation studies validate the contribution of key components like SDF regularization.\n# Weaknesses\n- **Insufficient evaluation.** For articulated objects reconstruction, kinematic parameters are as the same significant as part meshes. However, I found that the authors put the evaluation results of kinematic parameters (Ang Err) and part meshes (CD-m) in the supplementary and ignore a strong baseline ArtGS. The authors need to explain the reason.\n- **Insufficient Technical Innovation.** While the \"unbiased SDF regularization\" is highlighted as a core contribution, it is not an innovation proposed by the authors.  Using unbiased SDF regularization for 3D Gaussian learning has been proposed by GOF and this paper apply this for articulated objects reconstruction. I recognize the engineering contribution of this paper but it't not enough to be a core contribution.\n- **Single-Part Restriction.** The method is limited to objects with only one movable part.\n\nQUESTIONS:\n- **Inadequate  Evaluation**: Why are the critical kinematic parameter results (Ang Err, Pos Err) and part-level evaluations (CD-m, CD-s) relegated to the supplementary material when these metrics are fundamental for articulated object reconstruction? Additionally, why is ArtGS excluded from the kinematic parameter comparison in Table 7, given that it's a strong baseline for articulated object modeling? Moving kinematic evaluation to the main paper and including comprehensive comparison with ArtGS across all metrics would demonstrate the method's true performance against state-of-the-art approaches.\n\n- **Questionable Technical Novelty Claims**: The \"unbiased SDF regularization\" is presented as a core contribution, but GOF already proposed using SDF regularization for 3D Gaussian learning. How does your approach fundamentally differ from GOF's method beyond applying it to articulated objects? What specific technical innovations justify this as a primary contribution rather than an engineering application?  Clear demonstration of novel technical components beyond existing SDF-Gaussian integration, or honest repositioning of the work as an engineering contribution with appropriate claims.\n\n- **Single-Part Limitation Acknowledgment**: The method appears fundamentally restricted to objects with one movable part, yet this critical limitation is not clearly stated in the main paper. Honest acknowledgment of the single-part restriction in the main paper, with discussion of its implications and potential solutions, would improve the paper's transparency and help readers understand its scope.\n\nLIMITATIONS:\nNo acknowledgment that the method is fundamentally limited to objects with one movable part. Additionally, when the number of movable parts increases, reconstructing articulated objects from two states will be more challenging due to the occlusion between these parts.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-05"}