SimpliGuard: Robust Mesh Simplification In the Wild

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-03-31 15:30:25

Abstract: Polygonal meshes are widely used to represent complex geometries. However, the increasing complexity of models often leads to large meshes with millions of triangles, raising significant challenges for storage, transmission, and computation. Mesh simplification, a process of reducing the number of triangles in a mesh while preserving its overall shape and important features, has emerged as an indispensable technique to address these challenges. In this work, we focus on the problem of obtaining a visually consistent ultra-low-polygon mesh for complex meshes. Unlike previous methods, we design a robust simplification framework, SimpliGuard, to handle any meshes in the wild. Firstly, a reconstruction module is used to construct a low-polygon mesh with a similar shape but a manifold topology. Then, a texture initialization module is employed to quickly initialize the entire texture map. After that, a differentiable rendering module is utilized to optimize the overall structure and texture details, ensuring high-quality results. For meshes with skeletons, the correctness of motion can be preserved with our designed motion post-processing module. Experimental results demonstrate that SimpliGuard significantly outperforms previous methods and various featured software, including Blender and Simplygon.

Main paper: 
 introduction: 
 Meshes have been widely employed in various domains, such as virtual reality and game development. However, as the complexity of meshes increases, the required computational and rendering resources also increase, thereby limiting their performance on mobile devices, VR, and other edge devices. To address this issue, mesh simplication techniques have been proposed.Traditional methods, such as QEM (Quadric Error Metrics [Garland and Heckbert 1997] ), employ a greedy strategy to perform edge collapses and merges on the original mesh. To preserve texture correctness as much as possible, some variants [Garland and Heckbert 1998 ] incorporate the attributes as constraints into the optimization process. However, these traditional methods essentially generate a sub-mesh from the original mesh, regardless of whether the original mesh's structural design is friendly to decimation. This leads to the following issues: 1) the lower bound of the reduced face count is constrained by the structural design of the original mesh; 2) the output quality varies for dierent meshes, and severe structural errors, such as holes, can occur for meshes in the wild; 3) the texture distortion and fragmentation after simplication result in poor visual perception. Consequently, traditional mesh simplication algorithms are dicult to be directly used, and are often used as auxiliary tools for designers.Recently, some works based on dierentiable rendering, like Hasselgren et al. [2021] , have emerged. They utilize dierentiable rendering to optimize the texture and topological structure simultaneously. However, these methods only rely on dierentiable rendering to optimize the structure and texture, which can lead to the following issues: 1) careful parameter tuning is necessary for dierent meshes; otherwise, the results are unstable and of varying quality; 2) the optimization process is extremely slow; for instance, Nvdi requires over half an hour to achieve satisfactory results, which is impractical for real-world usage; 3) for meshes with skeletons, the animation of the simplied mesh cannot be guaranteed.In this work, a robust framework, SimpliGuard is proposed. With SimpliGuard, a mesh of any complex structure can be reduced to a target number of faces within a few minutes while maintaining similar shape and texture. For meshes with skeletal animation, SimpliGuard guarantees the correctness of the output mesh's motion. The overall structure consists of four parts. The reconstruction module can transform any complex mesh in the wild into a manifold mesh with a similar shape. The texture initialization module quickly generates coarse texture maps, which has two benets: 1) it signicantly reduces the learning time of the dierentiable rendering module for textures, and 2) for scenes that do not require close-up observation, these texture maps can be directly used. The dierentiable rendering module, following the texture initialization module, further optimizes the structure and texture details to improve the overall quality of the generated mesh. Finally, the motion post-processing module ensures that the animated mesh does not suer from structural problems even when reduced to a small number of faces. The overall contributions are as follows:1. We propose SimpliGuard, a framework that can generate highquality simplied meshes for meshes in the wild. For rigged meshes, SimpliGuard ensures correct motion and eliminates structural issues even after simplication. The advantages are achieved through the combined eorts of various modules.2. The framework can generate reliable results in a few minutes. This is achieved through our meticulous module design and initialization acceleration.3. We propose multiple loss functions that signicantly improve the results of dierentiable rendering, enhancing the usability of the generated meshes.4. Qualitative and quantitative experiments show that our method is superior to the previous academic approaches and featured software in terms of metrics and visual quality. 
related work: 
 Mesh Simplication. Traditional mesh simplication algorithms [Luebke 2003 ] can be roughly divided into two categories: local strategies and global strategies. One representative algorithm of global strategies is Vertex Clustering [Low and Tan 1997; Rossignac and Borrel 1993; Valette and Chassery 2004; Valette et al. 2005 ]. This kind of algorithm sorts each vertex based on a dened cost function to ensure that less important vertices are more likely to be merged. However, these methods can completely change the topology of the input mesh in an unpredictable way, and the resulting structure is not visually similar to the original structure, which is unacceptable. In the local feature-driven approaches, vertex decimation [Schroeder et al. 1992; Soucy and Laurendeau 1996] iteratively selects a vertex, deletes it along with the surrounding faces, and re-tessellates the resulting hole. However these methods are eective for manifold surfaces and not suitable for in-the-wild scenarios. As another kind of local strategy, edge contraction, proposed by [Hoppe et al. 1993] , has been the most commonly used simplication operation. Its core idea is to merge individual edges into a single vertex. The selection of edges and the optimal merged vertex depend on the dened cost function. Consequently, several related algorithms have been proposed. Ronfard and Rossignac [1996] measures the distance between points and planes using a quadric matrix. Inspired by Ronfard and Rossignac [1996] , Garland and Heckbert [1997] introduced a new error matrix to approximate geometric deviation, known as QEM. Subsequently, many QEM-based variants have been proposed, including Garland and Heckbert [1998] , Hoppe [1999] , Wu et al. [2004] , Li and Zhu [2008] , Thiery et al. [2013] and Liu et al. [2017] . Recently, there have been some methods based on neural networks. Potamias et al. [2022] uses a graph neural network to simplify a given mesh in one pass. Hasselgren et al. [2021] utilizes dierentiable rendering to simultaneously t the shape and texture of the target mesh. However, these methods essentially results in a subset of the original mesh, and thus are limited by the original mesh structure and are dicult to handle for complex meshes in the wild. Surface Remeshing. Surface remeshing is a widely researched eld [Khan et al. 2020] . A typical method is constructing a Centroidal Voronoi tessellation on the surface of the mesh [Du et al. 1999] . Due to slow convergence, it requires a good initial sampling. Alliez et al. [2003] introduces an error diusion-based algorithm that eectively nds better initial sampling. But this method operates in the global parameter domain, which may not nd suitable parametrization. To address this, Surazhsky et al. [2003] performs local modications while referencing the geometry of the original mesh. Although it is more ecient and accurate, the sampling quality is not guaranteed. Thus, some new algorithms have been proposed, including Schreiner et al. [2006] based on advancingfront paradigm, Fu and Zhou [2008] based on 2D fast Poisson disk sampling algorithm, and Fuhrmann et al. [2010] based on novel resampling strategies. However, these methods are dicult to directly apply to our task. On one hand, they mainly focus on improving the accuracy of the tted mesh, which often leads to an unacceptable number of faces. On the other hand, many algorithms only consider the mesh structure and neglect the mesh's texture properties, making it challenging to apply to assets in real-world scenarios. The framework consists of four modules. Given a high-poly mesh M tar , we rst apply the reconstruction module to obtain a low-polygon mesh with similar shape and manifold topology. Then, the texture fast initialization module is employed to quickly initialize the entire texture map within few seconds. After that, the dierentiable rendering module is utilized to optimize the overall structure and texture details with ours proposed losses. For meshes with skeletons and weights, the correctness of motion can be preserved with the designed motion post-processing module. 
method 3.1 overview: 
 Given a mesh M tar = (V t , F t , Tex t , BW t [opt]), our goal is to obtain a new mesh M src = (V s , F s , Tex s , BW s [opt]) with a reduced number of faces, where V, F, Tex, and BW respectively represent the vertices, faces, texture maps, and skeletal bone weights. M src is required to be visually similar to M tar as much as possible. Additionally, if there are animated skeletons, M src needs to keep the structural correctness under large deformation. To achieve this objective, we propose a unied framework, SimpliGuard, as shown in Fig. 2 .Most of the previous QEM-based method achieve decimation by performing edge contraction, essentially resulting in a subset of the original mesh. Take QEM as an example. Without loss of generality, let's assume that during the merging process, E 1 is selected as the merged point for E 1 and E 2 . Then, the faces with an edge connecting E 1 and E 2 will be removed, and E 1 will replace E 2 in all the faces and edges with E 2 as a vertex of them. As a result, the remaining faces are a subset of the original faces with some vertex positions changed. Therefore, for such algorithms to achieve good results on meshes in the wild, the assumption must hold true that a subset of the mesh's faces can well represent the original structure. In reality, however, most meshes do not adhere to this assumption. To address the structural problem, we propose the reconstruction module, which includes Surface Reconstruction, Traditional MeshSimplication, InvisibleFaceRemoval and Auto UVUnwrapping.Surface Reconstruction. Our objective is to obtain a mesh that is similar to the original mesh and has a high-quality structure (watertight and manifold). These properties ensures that the mesh will not suer from structural issues such as discontinuous triangle faces during optimization. To achieve this goal, we introduce surface reconstruction, which is a well-explored eld of research [Alliez et al. 2008; Chen et al. 2023; Khan et al. 2020; Khatamian and Arabnia 2016; Peng et al. 2005] . A typical approach involves transforming the mesh into a signed distance eld and then extracting the surface using isosurface extraction techniques, which are subsequently converted into a watertight and manifold triangular mesh [Huang et al. 2018 ]. This kind of technique generally satises our structural requirements. However, considering the requirement for ecient execution, we need to modify these algorithms to minimize memory usage and ensure fast performance. Specically, when applying the marching cube based methods [Lorensen and Cline 1998], we only retain the relative relationships between the cubes and the mesh. Then, we employ a ner grid representation for the surface regions, while for non-surface regions, a coarser grid representation is used to conserve memory. Additionally, an octree data structure is employed to expedite the computation process by storing information between nodes. As indicated in Figure 7 , for a mesh with 30,000+ faces, the reconstruction can be completed within 4.4 seconds.Traditional MeshSimplication. After surface reconstruction, we have obtained a high-quality mesh that is manifold. In this section, our objective is to quickly minimize the face count to achieve the target face count, without strictly pursuing the quality of the reduced mesh. Therefore, we employ the classical QEM algorithm. It should be noted that, since we are reducing the face count of a watertight and manifold mesh obtained through surface reconstruction, various structural issues that may be present in the original mesh are resolved. However, there might be the problem of selfintersection. To address this problem, we additionally propose a curvature constraint. Given v i and the faces f 1 , . . . , f M it belongs to, we compute the normal vectors N 1 , N 2 , . . . , N M . Then, the curvature of v i is calculated as follows: c i = 1/ Õ " j=1 angle n i , N j , where n i is the normal vector of v i and angle n i , N j is the angle between n i and N j . Since the original quadric of each v i can be written as& (v i ) = Õ M j=1 area(f j ) •& (v i , f j ), where & (v i , f j ) = v T i Av i + 2b T v i +3, we can represent & (v i , f j ) using a simple representation [Hoppe 1999]: & (v i , f j ) = (A, b, 2). Now we need to incorporate curvature into & (v i , f j ).Here we directly give the expression, and the proof can be found in the supplementary material.Q 2 (v i , f i ) = © ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ´© ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ´gg ) . . . 0 . . . g . . . 0 . . . . . . 0 . . . . . . 0 . . . 0 . . . 0 . . . g ) • • • 0 • • • 1 • • • 0 • • • . . . 0 . . . . . . 0 . . . 0 . . . 0 . . . ™ AE AE AE AE AE AE AE AE AE ¨, © ≠ ≠ ≠ ´@g 0 @ 0 ™ AE AE AE ¨, @ 2 ™ AE AE AE AE AE AE AE AE AE ẅhereg and @ can be computed by:© ≠ ≠ ≠ ´vT 1 1 v T 2 1 v T 3 1 N T 0 ™ AE AE AE ¨© ≠ ≠ ≠ ´g @ ™ AE AE AE ¨= © ≠ ≠ ≠ ´c1 c 2 c 3 0 ™ AE AE AE The nal & total (v i , f i ) = & (v i , f i ) + U& 2 (v i , f i ),where U is a hyperparameter used to balance QEM and the curvature constraint.InvisibleFaceRemoval. After traditional mesh simplication, there may exist completely invisible faces in the idle state. These faces have no visual impact but increase computational complexity. Furthermore, during animation, these invisible faces can potentially cause self-intersection between the inner and outer surfaces, leading to structural issues. Thus they are necessary to be removed.Here we adopt a straightforward approach to identify the invisible faces. We generate multiple rays from v i and check for each ray if it intersects with the mesh. If there is no intersection, we increment a counter by 1; otherwise, the counter remains unchanged. Finally, we determine the visibility of the current point by evaluating whether the counter is greater than zero. This process can be expressed as H i = Ø ⌦ I (w) dw. ⌦ represents the set of all rays in the hemisphere. I (w) = 1 when the ray F doesn't intersect with the mesh, and I (w) = 0 otherwise. In practice, we approximate the process by randomly sampling rays from the hemisphere.Auto UVUnwrapping. Until now, the mesh we have obtained lacks UV coordinates, making it unsuitable for texture generation. Therefore, we employ a classic method for automatic UV generation known as Least Squares Conformal Maps [Lévy et al. 2002] . It partitions the surface of the mesh into multiple local patches and minimizes distortion within each patch.We have obtained a simplied mesh with a rough structure that lacks textures. To obtain the texture, an intuitive approach would be to directly optimize the texture using dierentiable rendering. However, iterative optimization with dierentiable rendering is a time-consuming operation. Considering that M src and M tar have similar shapes, we propose a texture fast initialization module based on rendering. In the rendering pipeline, after triangles are rasterized, they are lled with color values sampled from the texture However, although the obtained coarse texture map can roughly capture the color information of the original mesh, due to the limited rendering viewpoints and resolution, the texture map becomes discontinuous, resulting in numerous "spots" (as shown in Figure 3 ). To generate a more continuous texture map with the same computational resources, we propose a smoothing algorithm. For each pixel, we select a circular region with a radius of r and centered at the corresponding UV point on Tex t and Tex s . Then, we assign all the pixels within the circular region on Tex t to Tex s : To further improve the quality of the mesh, we introduce dierentiable rendering, which contains three types of loss functions: L tex , L img and L struct . The overall optimization objective is dened as:argmin Tex B ,V B E ⇥ U L tex + VL img + X L struct ⇤Loss of Texture. To generate a high-quality texture, two requirements should be ensured: 1) The rendered texture colors of M src should closely match that of M tar . 2) The transitions in the rendered texture should be smooth when observing the mesh closely. Previous research focuses more on optimizing texture color but overlooks the texture smoothness. To address this, we propose L tex . The loss randomly selects pixel coordinates, called P B , on the texture map during each iteration and applies small random perturbations to these coordinates, resulting in new coordinate points. The objective is to constrain the color of the new coordinate points to be as similar as possible to that of the original points: Loss of Image. Optimizing texture using MSE loss on rendered images is a common practice in dierentiable rendering. However, this approach often struggles to capture ne details in the texture. The reason is that MSE loss does not impose constraints between pixels, which can lead to issues such as unclear edges in the images.L tex = ||Tex B (P B ) Tex B (P B + n)|| 2 , where n ⇠ N (0, f 2 ).To address this problem, we introduce a new loss. This loss calculates the gradients of the rendered image r s and compares them with the gradients of the target image r t . When the rendered image exhibits blurriness in the edge regions or roughness in smooth regions, it results in larger gradients in those areas. This allows the gradient descent algorithm to eectively optimize the texture in those specic areas. The nal loss is dened as follows:L img = ||Grad(r t ) Grad(r s )|| 2 + ||r t r s )|| 2 .Loss of Structure. Optimizing the mesh structure only based on rendering images is unfeasible, as it may lead to issues such as mesh interpenetration, spikes, and surface irregularities. To alleviate this challenge, we impose Laplacian smoothing:L laplacian = 1 |V s | |V s | ' 8=1 1 |S i | ' v j 2S i v j v i 2Here, |V s | is the number of vertices in M src , and S i is the neighbourhood of v i . Furthermore, to ensure smoothness in M src , we apply a smoothness constraint on the normal vectors:L normal = 1 |F s | ' (v 1 ,v 2 ) 2E s (1 cos(n 1 , n 2 ))F s represents the set of faces in M src , and E s represents the set of all connected edges in M src . n 1 and n 2 are the normal vectors of vertices v 1 and v 2 , respectively. L struct can be represented as:L struct = w normal L normal + w lap L laplacianIn real-world applications, many objects are animatable. If these objects cannot be properly animated, they still cannot be used even if the triangles are reduced. Some related research utilizes a neural network to obtain the bone weights [Mosella-Montoro and Ruiz-Hidalgo 2022; Xu et al. 2020] ). However, these methods only optimize bone weights or vertex coordinates. It is reasonable for high-poly meshes. But in the case of low-poly meshes, simply moving the coordinates of vertices and optimizing bone weights cannot guarantee the absence of motion artifacts. In this section, we introduce a simple post-processing module. Firstly, given V t , F t , and BW t , we compute BW s for V s through the K-nearest neighbor algorithm:BW s (i) = Õ j2Neighbor r (i) (1 dist(V s (i) V r (j) ) Õ k2Neighborr (i) dist(V s (i) V r (k) ) ) ⇤ BW r (j)Then, we identify the planes where the joints lie and split the faces on the mesh that intersects with these planes. Specically, we nd all points in V s where the dierence in the rst two dimensions of BW s is smaller than X. Then, based on the physical properties of BW s , these points can be clustered into J groups, and the average of each group's points will serve as the center point for each of the J joints. Based on the orientation of the joints, the normal vectors of the planes can be calculated. With the points and the normal vectors, we can determine J joint planes, which can be used to calculate the intersection points between the planes and the mesh. Finally, these points are used as new vertices to split the faces. 
experiments 4.1 experimental setup: 
 Dataset. To fully validate the eectiveness of the algorithm, we collect 884 assets from Sketchfab and convert them into .obj le format. Details can be seen in the supplementary material.Metrics. We measure the visual quality of the low-poly mesh based on PSNR and SSIM. To better measure perceptual similarity, we design a metric called NLPIPS (Normalized LPIPS), which is based on LPIPS: NLPIPS = 1 LPIPS(I src , I tar )/LPIPS(Noise, I tar ), where Noise is the noise image. All these metrics are computed with the images rendered by 80 camera views.To measure structural similarity, we use the 3D IoU metric [Ravi et al. 2020] . Figure 5 : Comparison with featured software. Blender fails to handle complex meshes, resulting in fragmented structures in the generated output. Simplygon often leads to texture distortion. In contrast, SimpliGuard consistently produces excellent results even when dealing with complex high-poly meshes.We conduct a comparative analysis with ve algorithms, and set three target levels: 1,000 faces, 2,000 faces, and 4,000 faces, which are the typical number of faces that can be rendered in VR scenes for a large number of assets. Since the face count for each asset in the dataset is greater than 30,000, the reduction ratio is at least approximately 86.7%. All results from SimpliGuard are obtained using the same parameters (details in the supplementary material).Comparisons with academic approaches. We select two traditional methods, QEM [Garland and Heckbert 1997] and QEM++ [Garland and Heckbert 1998] , and one dierentiable rendering-based method, Nvdi, as comparison benchmarks. Since QEM++ often struggles to achieve the target face count, we calculate the metrics for QEM++ with the meshes that it can simplify to below 5000 faces, which counts for 773 in total. As shown in Table 1 , SimpliGuard signicantly outperforms other approaches in all metrics. Additionally, it is worth noting that even for other methods evaluated at 2,000 faces, their results do not surpass our method at 1,000 faces.Comparisons with industry software. We select two widely used industrial software, including Blender and Simplygon, as comparison benchmarks. As can be seen in Table 1 , our method still achieves the best performance across all metrics.Visualization analysis. To visually compare dierent methods, we randomly show some results in Figure 4 and Figure 5 . It can be observed that QEM and QEM++ exhibit rough structures and distorted textures. Nvdi, Blender, and Simplygon also fail to achieve similar 3D structures or high-quality textures. In contrast, our method achieves the best results in terms of both structural delity and texture clarity. This observation aligns with the quantitative metrics.To validate the animation for rigged characters after simplication, we conduct a series of clips to drive the characters. As shown in Figure 6 , the low-poly meshes accurately preserve the integrity of the overall shape and exhibit no structural issues even for large deformation. This demonstrates the robustness of SimpliGuard in preserving the essential geometric features for character animation.We run SimpliGuard on a V100 GPU, and the overall time is illustrated in Figure 7 . To remesh a 30,000-face mesh to 1,000 faces, SimpliGuard takes 84s totally. In particular, the reconstruction module takes 4.38s, the texture fast initialization module takes 0.33s, We replace the module with QEM while keeping the other modules unchanged. The mesh structure with the module is signicantly superior to the one using QEM, which highlights the necessity of the reconstruction module.the dierentiable rendering module takes 77.99s, and the motion post-processing module takes 1.60s. For comparison, we also plot the time distribution of Nvdi in Figure 7 . Our method outperforms Nvdi in terms of both runtime and visual quality.In this section, we validate the necessity and eectiveness of various components in SimpliGuard.The Reconstruction Module. To validate the impact of this module, we replace it with QEM. It takes the reference mesh as input and generates a mesh with the desired number of faces. This mesh then goes through the subsequent modules to obtain the nal result. The results, shown in Figure 8 , clearly demonstrate that using QEM leads to dissimilarity to the reference mesh and structural issues. In contrast, the structure obtained with the reconstruction module is signicantly superior to the QEM-based version both in terms of structure and visual appearance. This emphasizes the necessity of the reconstruction module to achieve desirable results.The Texture Fast Initialization Module. As shown in Figure 9 , with the inclusion of this module, SimpliGuard already produces preliminary results at 2s, while without the module, the textures remain signicantly blurry. Besides, to achieve the same PSNR, the inclusion of this module takes approximately 1/10 of the time compared to when it is not included. This strongly demonstrates the eectiveness of the module in accelerating convergence.The Dierentiable Rendering Module. We conducted ablation experiments on the proposed losses. From Figure 11 , it is visually apparent that the inclusion of the Gradient Loss enhances texture details and reduces the blurriness of texture edges. From Figure 11 , with the inclusion of the texture loss, the overall texture transitions become smooth, and no abnormal textures are present.The Motion Post-processing Module. For a low-poly mesh, as shown in Figure 10 , incorporating the module ensures the preservation of a robust 3D structure even under animation. In contrast, without the module, some triangles are likely to span across joints, leading to noticeable structural degradation during animation. Therefore, this module plays a crucial role in meshes with skeletons. 
conclusions: 
 We have presented our proposed framework, SimpliGuard for handling arbitrary meshes. Compared to previous methods, our approach oers several advantages: 1) No assumption constraints are imposed on the input mesh. 2) It can achieve extreme mesh simplication in a few minutes, resulting in high-quality meshes in terms of both texture and structure. 3) For rigged meshes, SimpliGuard ensures the correctness of the structure during motion even after extreme simplication. These advantages are achieved through the combined eorts of various modules and the designed loss functions. In the experiments, we showcase the signicant advantages of our method compared to other approaches by conducting mesh simplication on complex meshes in the wild. This highlights the enormous potential of our method for practical applications.  
 
 Appendix:  


Decision: This paper presents SimpliGuard, a robust mesh simplification framework for complex meshes. It consists of four modules, i.e., reconstruction, texture fast initialization, differentiable rendering, and motion post-processing. All reviewers have positive final ratings, are satisfied with the response, and recommend accepting the paper. I agree with their recommendation. Thanks for the authors' effort and rebuttal.

Reviews: review: paper_summary: paper_summary: The paper presents a novel framework for mesh simplification which incorporates texture and rigging in to account. The proposed method is structured into four distinct modules, each addressing a specific aspect of the process: reconstruction, texture initialization, differentiable rendering, and motion post-processing. Experiment has been conducted on selected models from Sketchfab with the evaluation based on four metrics: PSNR, SSIM, NLPIPS, IOU3d.
strength_weakness: strength_weakness: + The paper extends the scope of mesh simplification by incorporating additional aspects such as texture and rigging.
+ The structure of the paper is well-organized and easy to follow.
limitations: limitations: - Given that the objective is mesh simplification, it would be beneficial to include an evaluation of the resulting mesh quality.
- For the evaluation based on rendered results, a detailed examination including zoomed-in views is recommended to adequately assess features such as specular highlights.
- The effectiveness of differentiable rendering module is not straightforward, and the paper does not include an ablation study comparing the results with and without this module.
paper_summary: paper_summary: The paper introduces SimpliGuard, a robust mesh simplification framework for complex meshes. It consists of four modules: reconstruction, texture fast initialization, differentiable rendering, and motion post-processing. The reconstruction module prepares meshes by converting them into manifold structures. The texture fast initialization module quickly generates texture maps to reduce optimization time. The differentiable rendering module enhances structure and texture quality using advanced loss functions. The motion post-processing module ensures the structural integrity of animated meshes. Experiments show SimpliGuard outperforms traditional methods and industry software like Blender and Simplygon. It delivers high-quality results efficiently, suitable for real-world applications. Ablation studies validate the effectiveness of each module.
strength_weakness: strength_weakness: + SimpliGuard presents a framework for mesh simplification that effectively handles complex, unstructured meshes in the wild. Unlike traditional methods, it combines multiple advanced modules to ensure high-quality results.
+ The framework employs a comprehensive approach by integrating several key components: reconstruction, texture initialization, differentiable rendering, and motion post-processing. Each component addresses specific challenges in mesh simplification.
+ The paper provides a thorough evaluation of SimpliGuard through extensive experiments. It compares the framework with traditional methods (QEM, QEM++) and industry software (Blender, Simplygon), using both qualitative and quantitative metrics (PSNR, SSIM, NLPIPS, 3D IoU).
limitations: limitations: - The framework involves multiple intricate modules and techniques, such as the reconstruction module, texture fast initialization, and differentiable rendering with specialized loss functions. Re-implementing such a framework for real application could be challenging.

- While the paper provides a thorough evaluation against traditional methods (QEM, QEM++) and industry software (Blender, Simplygon), it lacks comparisons with some of the latest neural network-based mesh simplification methods. Including these comparisons could have provided a more comprehensive evaluation of SimpliGuard's performance against the state-of-the-art in machine learning approaches, such as [1].

[1] Chen Z, Pan Z, Wu K, et al. Robust Low-Poly Meshing for General 3D Models[J]. ACM Transactions on Graphics (TOG), 2023, 42(4): 1-20.

- The paper focuses on meshes obtained from Sketchfab, which might not fully represent the diversity of real-world 3D models. Testing SimpliGuard on a broader range of datasets, including those from different sources and with varied characteristics would strengthen the claim of its robustness and generalization capabilities, for example, including more results on Thingi10k dataset [2].

[2] Zhou Q, Jacobson A. Thingi10k: A dataset of 10,000 3d-printing models[J]. arXiv preprint arXiv:1605.04797, 2016.

- Chamfer loss is a commonly used metric to measure the distance between vertices. Is it possible to include Chamfer loss in this method to improve geometric reduction performance? 

- The current reported results are not clear enough to adequately demonstrate the quality of the re-meshed results. Can this method avoid issues like inconsistent face orientation and self-intersections in complex topologies?

- It would also be beneficial to include the vertex and face count in each visual result.

- This paper employs the cycle-point strategy to reduce the occurrence of "spots." How about adding a linear interpolation post-processing step to address these spots? It seems that these scattered spots could be easily removed with this simple strategy.
paper_summary: paper_summary: It proposes a framework, SimpliGuard, for mesh simplification, handling meshes in the wild, and keeping a similar structure, texture, and good moveability after processing. 

SimpliGuard consists of four parts: the reconstruction module, the texture initialization module, the differentiable rendering module, and the motion post-processing module. The reconstruction module first simplifies meshes, and the texture initialization module generates coarse texture maps. Then, the differentiable rendering module optimizes the structures and textures of meshes. Finally, the motion post-processing module fine-tunes mesh structures for animation. Comprehensive experiments demonstrate the superiority of SimpliGuard over existing methods.
strength_weakness: strength_weakness: 1. It has good motivation and produces meaningful results. 
2. It simultaneously solves three key problems in mesh simplification: face reduction, texture keeping, and moveability for animation.
limitations: limitations: 1. About meshes in the wild:
   1. It is unclear how and why the proposed method can handle meshes in the wild. 
   2. Can the data from Sketchfab represent meshes in the wild?
2. The proposed method cannot handle the non-watertight meshes since the reconstruction module is based on the classical marching cubes.
3. The differentiable rendering takes too much time. Are the benefits of this module worth the time-consuming? I hope to have some discussions or ablation studies about this.
4. There is no discussion about limitations.
5. There are some writing problems:
   - Line 139, no reference for Nvdiff.
   - Lines 261 and 262, M_tar as the input and M_src as the output, are confusing. I suggest using the dense mesh and simplified mesh.
   - Lines 366-370, symbols in the equation overlap.
   - Line 578, there should be a blank between sentences.