Mesh Splatting for End-to-end Multiview Surface Reconstruction
Authors: Ruiqi Zhang
JiachengWU
Jie Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Multi-View Stereo
Geometry Processing
Meshe Synthesis
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We soften meshes into differentiable layers, increasing their 3D receptive field and enabling the learning of intricate geometry via volumetric rendering.

Abstract: Surfaces are typically represented as meshes, which can be extracted from volumetric fields via meshing or optimized directly as surface parameterizations. Volumetric representations occupy 3D space and have a large effective receptive field along rays, enabling stable and efficient optimization via volumetric rendering; however, subsequent meshing often produces overly dense meshes and introduces accumulated errors. In contrast, pure surface methods avoid meshing but capture only boundary geometry with a single-layer receptive field, making it difficult to learn intricate geometric details and increasing reliance on priors (e.g., shading or normals). We bridge this gap by differentiably turning a surface representation into a volumetric one, enabling end-to-end surface reconstruction via volumetric rendering to model complex geometries. Specifically, we soften a mesh into multiple semi-transparent layers that remain differentiable with respect to the base mesh, endowing it with a controllable 3D receptive field. Combined with a splatting-based renderer and a topology-control strategy, our method can be optimized in about 20 minutes to achieve accurate surface reconstruction while substantially improving mesh quality.

Published as a conference paper at ICLR 2026
MESH SPLATTING FOR END-TO-END MULTIVIEW SURFACE RECONSTRUCTION
Ruiqi Zhang, Jiacheng Wu, and Jie Chen Department of Computer Science Hong Kong Baptist University {csrqzhang, cs jww,, chen jhenniee@@co www.hk.b................. aalkuueddu. edu.h
[e] Ours
(b) SuGaR
GaussianSurfe B01K/CD:
[d] Neuralangelo 329K/CD:4.66
(e) Poisson Recon. with GT Points
1291/CD:2.44
17U9K/CD:
Figure Comparison of reconstruction paradigms. Yellow points denote ground-truth point clouds. "Verts" and "CD" denote the number of vertices and the Chamfer distance, respectively. (a) Our method optimizes meshes end-to-end and uses remeshing for topology control, achieving accurate surfaces with the fewest vertices. (b) SuGaR Guédon & Lepetit (2023) also optimizes meshes but relies on single-layer Gaussian-splattin proxy and cannot perform remeshing, which limits accuracy. (c-d) As volumetric methods, GaussianSurfel
et al. (2024) and Neuralangelo/ et al. (2023) require meshing step to extract surfaces, which accumulated ates errors and often yields unnecessarily dense meshes; note the misalignment between their meshes and the point clouds (red the ground ground-truth points shows that with accurate point clouds, meshing can still introduce errors-e.g, eeg.. omission of points (blue circle)-which constrains the practical upper bound of volumetric pipelines.
circle). (e) Poisson reconstruction
ABSTRACT
Surfaces are typically represented meshes, which can be extracted from volu- metric fields via meshing or optimized directly as surface parameterizations. Vol- umetric representations occupy 3D space and have large effective receptive field along rays, enabling stable and efficient optimization via volumetric rendering: however, subsequent meshing often produces overly dense meshes and introduces accumulated errors. In contrast, pure surface methods avoid meshing but capture only boundary geometry with a single-layer receptive field, making difficult learn intricate geometric details and increasing reliance on priors (c.g., shading normals). We bridge this gap by differentiably turning surface representa- tion into a volumetric one, enabling end-to-end surface reconstruction via volu- metric rendering to model complex geometries. Specifically, we soften a mesh into multiple semi-transparent layers that remain differentiable with respect to the base mesh endowing with a controllable 3D receptive field. Combined with splatting-based renderer and topology-contee strategy, method can be optimized in about 20 minutes to achieve accurate surface reconstruction while substantially improving mesh quality.
INTRODUCTION
Surface reconstruction from images a critical process for efficiently generating 3D assets across in- dustries. including film production, video game development, and virtual/augmented reality. Among the diverse 3D representations (c.g., point clouds, signed distance fields, and meshes), meshes are widely preferred in practice due their ease of manipulation and versatility Blender (2024).

---

Published as a conference paper at ICLR 2026
Meshes can be extracted from volumetric fields through meshing techniques optimized directly as surfaces. Volumetric methods include implicit Mildenhall et al. (2020); Wang et al. (202T), ex- plicit Kerbl et al.] (2023); Huang et al.] (2024); Dai et al. (2024), and hybrid approaches Gu et al. (2025) Müller et al. (2022): Li et al. (2023). They occupy 3D space and enjoy a large effective receptive field along rays, enabling stable and efficient optimization via volumetric rendering. After optimization, meshes are typically extracted from volumetric representations using algorithms such as Marching Cubes Lorensen & Cline (1998), Marching Tetrahedra (Shen et al. (2021). Poisson Re- construction Kazhdan & Hoppe (2013), and TSDF- methods (Zhou et al. 2018). To improve surface reconstruction performance, prior work has focused on enhancing the accuracy and smooth-
ness of the underlying surfaces represented volumetrically WWng et al. (2021): Li et al. (2023); Dai
ct al. (2024); Huang et al. (022); Fu et al. (2022). For example, NeuS Wang et al. (202 introduces surface constraints by reparameterizing the NeRF Mildenall et al. (2020) density field as signed
distance field to improve surface smoothness; Neuralangelo
al. (2023) leverages hash-encoding
networks Muller et al. (2022) to capture intricate details; Geo-NeuS Fu al. (2022) incorporates sparse foructureffrom-ii Motion (SfM) points as additional supervision; and 2DGS Huang et al. (2024)
and GOF al. (2024) incorporate accurate normal/depth rendering into Gaussian Splatting Kerbl iet al. 2023). Despite these advances, volumetric methods inevitably rely on a meshing step that can produce overly dense meshes and accumulate errors. Although remeshing Hoppe et al. (1993) can improve mesh quality, it is typically not differentiable within the optimization process and introduces additional error accumulation.
A parallel line of work, end-to-end mesh optimization Munkberg et al. (2022): Yang et al. (2025), ; Nicolet et al.) (202T), avoids reliance on meshing and can control mesh quality through remesh- ing Hoppe et al. (1993) during optimization. However, meshes capture only boundary geometry with a single-layer receptive field, which hinders learning of intricate geometric details and in- creases reliance priors (e.g.. shading normals). Some methods reparameterize meshes using volumetric proxies-such such as tetrahedral grids in NvdiffRec Munkberg et al. (2022), point clouds in IMLS- MMLS-Splatiig Yang al. (2025), and tetrahedral spheres in TetSphere Guo et al. (2024) to im- prove topology stability during optimization. Nonetheless, they still optimize a single-layer surface with prior-based supervision: the primitive projected image space remains the mesh, which lacks a volumetric receptive field and makes geometric detail recovery difficult; moreover, shading is hard to estimate under complex materials, and normal/depth estimation introduces additional errors.
To address the shortcomings that volumetric methods rely error-prone meshing while surface-based methods lack sufficient 3D con- text and have a single-layer receptive field, we bridge the gap by differentiably converting surfaces into pseudo-volumetric representa- tion. Specifically, we soften a mesh into sev- eral semi remi-transparent layers that remain differ- entiable with respect to the base mesh render them to image space using a splatting- pixel via volumetric rendering. The semi-transparent layers randomly sampled around the base mesh thereby enlarging the 3D receptive field for learning intricate details. In Fig. 2 we present a comparison between regular meshes and the proposed soft mesh. For regular meshes in Fig. 2 a), when the mesh does not overlap the real surface, multi-view observations can only optimize the color at point A and provide little spatial gradient move the geometry toward the true surface. In contrast, in Fig. (bb), softening the base mesh into multiple semi-transparent layers increases the receptive field and creates overlap with the real surface, with transparency computed differentiably from the signed distance to the base mesh. During multi-view optimization, points A and B are both observed; since A lies near the true surface, it exhibits similar appearance across views and receives a higher blending weight in volumetric compositing. This, in turn, reduces its signed distance and pulls the base mesh toward point A, ultimately deforming the mesh to accurately recover the surface.
Camera
Camera
Camera
Camera
Real Surface
Reaf Surface
Mesh
Deformedd
Loyers
Meeh
(2) Regular Mesh
(b) Soft Mesh
Figure 2: Comparison between regular meshes and soft mesh
renderer, and composite multiple projected layers per
At the same time, the surface is represented by the base mesh preserving the mesh's structural characteristics and enabling mesh quality control via remeshing. In practice, to maintain topology stability during mesh optimization, we build upon NvdiffRec Munkberg et al. (2022) by using tetrahedral grid (DMTet) parameterization in the early optimization stage and employ Continuous Remeshing Palfinger (2022) for further topology control after convergence.
2

---

Published as a conference paper at ICLR 2026
The contributions of this work are summarized as follows:
We propose a method that bridges volumetric and surface representations by softening a mesh into several semi- aami-transpaentt layers, thereby increasing the 3D receptive field for learning intricate details while preserving mesh characteristics, including topology control via remeshing techniques.
We introduce a splatting based renderer that efficiently renders the semi- transparent layers into images, enabling training about 20 minutes.
We present a hybrid oopoloyy-contro strategy that combines DMTet meshing and Contin- uous Remeshing, improving topology stability during optimization and substantially en- hancing final mesh quality.
RELATED WORKS
2.1 SURFACE RECONSTRUCTION WITH VOLUMETRIC REPRESENTATIONS
Volumetric representations occupy 3D space with nonzero density or transparency and are typi- cally optimized via volumetric rendering Mildenhall et al. (2020). Representative examples include NeRF, which models scenes with a coordinate- based MLP that outputs density and Mildenn hall et al. (2020), and 3D Gaussian Splatting (3DGS), which parameterizes scenes with large sets of transparent ellipsoids Kerbl et al. (203)) Depending on the parameterization, approaches can be categorized as implicit Wang et al. (202T): I); |Fu et al. (2022), explicit Huang al. (2024): Dai Gu
iet al. (2024); Chen et al. (2024). or hybrid Li et al. (2023); Gu et al. (2025). Early works such as NeRF Mildenhall et al. (2020) and 3DGS Kerbl al. (2023) primarily target novel view synthesís, while recent efforts adapt these models to surface reconstruction with meshing techniques.
Mainstream research focuses on improving the accuracy and smoothness of the underlying surface modeled by volumetric representations. NeuS Wang et al. (202I) and VolSDF Yariv et al. (202I) introduce surface constraints by reparameterizing NeRF's density field as a signed distance field to promote smoothness. Neuralangelo Li et al. (2023) leverages multi-resolution hash encoding Müller
al.] (2022) capture fine geometric detail, while Geo-NeuS Fu et al.) (2022) incorporates sparse
Structure-front wero--rooiioooo (SIM) points for additional supervision. In the explicit camp, GaussianSur-
fel Dai et al. 2024). 2DGS Huang et al. (2024), and GOF Yu et al. (2024) incorporate accurate normal and/or depth rendering into Gaussian splatting to better regularize surfaces.
After optimization, volumetric representations are converted to meshes via meshing techniques. Im- plicit models such as NeuS Wang et al. (2021) and Neuralangelo Li et al. (2023) typically evaluate the field on predefined grids and apply Marching Cubes LorbesLorensen & Cline (998). For explicit meth-
ods, GaussianSurfel Dai et al. (2024) applies Poisson Reconstruction Kazhdan & Hoppe (2013),
while GOF Yu et al. (2024) employs Marching Tetrahedra [Shen et al. (2021). To faithfully cap- ture details, these pipelines often require high resolutions depths, which can yield overly dense meshes, hindering practical deployment. Although downstream remeshing Hoppe et al.] (1993) can improve quality, it is typically non-differentiable and can introduce additional error accumulation.
Our approach differs in that we optimize directly on meshes rather than on volumetric fields, avoid- ing neshing-induced error accumulation and low mesh quality. Moreover, we can control mesh topology during optimization via remeshing, yielding strong reconstruction accuracy even under tight vertex budgets suited to real applications.
2.2 SURFACE RECONSTRUCTION WITH MESHES
Surface representations concentrate mass on opaque surfaces, such as point clouds Yifan et al. (2019) and meshes Hoppe et al. (199)). We focus on meshes due to their manipulability and versatility.
Meshes are typically rendered with differentiable rasterizers Ravi et al. (2020); Laine et al. (2020);
Kato et al. (2020); Liu et al. (2019) and optimized with prior-based supervision, e.g., shading under known reflectance models normals/depth predicted by monocular estimation networks Eftekhar
et al. (202T). Several works improve topology stability during optimization: NvdiffRec Munkberr et al. 20022 and IMLS- Spllatting Yang al. (2225) reparameterize meshes as tetrahedral or bic grids and rely on shading- based supervision, while SuGaR Guédon & Lepetit (2023) attaches
flattened Gaussian ellipsoids meshes and optimizes them jointly.
3

---

Published as a conference paper at ICLR 2026
Jayer
V
Layer'
Marching Tetrahedia
Ditferentiable Mesh splatting
Reudering
Soften
Loss
Betroved
Convergeace
Laper
Rendering
Initial Telrahedrs
Base Mesh
Solt Mesh
Tmagge
Ingul
Figure 3: Overview of the proposed method. An initial tetrahedral grid stores signed-distanee
values at its vertices, and base mesh is extracted using Marching Tetrahedra. The base mesh is then softened into multiple layers by offsetting vertices along their normals, transforming it from a surface into a -volumeee representation. The multi-layer mesh is rendered via the proposed Differentiable Mesh Splatting based on tile-based rasterization, and supervised by the input images through a rendering loss.
However, two limitations persist. First, the opaque, single-layer nature of meshes restricts their effective 3D receptive field, hindering the recovery of intricate geometry. Second, supervision via normals, depth, or shading is generally less accurate and informative than direct image supervision used in volumetric rendering. Normals from monocular predictors Eftekhar al. (2021) can be noisy; depth captured by sensors Zhang (2012) carries measurement uncertainties; and real-world illumination often violates assumptions made by shading models Munkberg et (2022).
Our method addresses both issues. We soften the mesh into multiple semi- comi-transparent layers sampled around the base surface, converting it into ggeudo-volumetti representation with a controllable 3D receptive field. This multi-layer structure enables optimization via volumetric rendering directly from image observations, reducing reliance on priors such as shading, normals, and depth.
2.3 OTHER MESH SOFTENING TECHNIQUES
We bridge surface and volumetric representations by softening meshes into multiple transparent lay- Related ideas have appeared primarily in novel view synthesis rather than surface reconstruc- tion. For instance, Gaussian Shell Maps (GSM) Abdal et al. (2024) and DELIFFAS Kwon et al. (024) build several layered shells around a base mesh (c.g.. SMPL Loper et al. 2015) to simulate light fields Levoy & Hanrahan (003),, enabling realistic rendering with coarse geometry. Adap- tiveShell (aan (2023) and Gaussian Frosting Guédon & Lepetit (202)) extract base mesh from a pre-trained implicit model and envelop it with two-layer shell, constraining radiance fields or Gaussian splats within the enclosed region for accelerated rendering. Quadrature Fields [Sarma et al. (2024) and Volumetric Surfaces Esposito et al. (2024) construct grids or multi-layer struc- tures around an extracted mesh to approximate SDF samples and achieve real-time rendering via rasterization-bant based intersection.
While these methods also place transparent layers around base mesh, they typically target novel view synthesis and lack differentiability between the layers and the base mesh. Consequently, the base mesh remains fixed after initialization and cannot be refined using gradients from the semi- transparent layers. In contrast, our layers are differentiable with respect to the base mesh allowing gradients from the volumetric rendering loss to update the base geometry, which crucial for end- to-end surface reconstruction.
METHOD
An overview of the pipeline shown in Fig. 3 We first soften the base mesh into several semi- transparent layers that remain differentiable with respect to the base mesh (Section 3.1]. We then render these layers using Differentiable Mesh Splatting (Section 3.2). Following prior mesh-based methods Munkberg al. (2022); Yang et al. (2025), we employ hybrid topology-conte control strategy to maintain mesh quality during optimization (Section
3.1 MESH SOFTENING
To construct the soft mesh in Fig. 2(b), we offset the vertices of the base mesh along their normals to form multiple sampled layers. We denote the base layer Mo and the multi-layer mesh as
4

---

In addition to the alpha a3. we attach per-vertex parameters needed for rendering: appearance fea- tures f. vertex normals nj, viewing directions ry. and positions x5. The viewing direction rý is the normalized vector from the camera center to the vertex and models view-dependent effects. These Published as a conference paper at ICLR 2026
{M,);- derived from it. For the j-th base vertex v3 Mo. the corresponding vertex the i-th layer is calculated as:
+ d' ny.
where dž is the offset distance and ny is the unit normal at vf.
We compute the transparency of each sampled layer from its signed distance to the base mesh. Since each softened vertex is generated by offsetting its source base vertex, it naturally has closest-point correspondence. Let stop(.) denote the stop-gradient operator. The signed distance vj is:
sign(dd)) stop(v;)) - -vill2-
(2)
and we stop gradients through vj that of remains differentiable with respect vg. Otherwise, substituting equation I into equation 2 collapses the expression to s). = sign(d)) |dd ny n1ll2 which is independent of vl and cannot drive base-geometry updates.
We convert signed distances alpha weights using variant of the VoISDF mapping Yariv et al. 2021 Omitting indices for brevity, we define
1 2/3)
s<0 O,
2
(3)
1/3
820, 0,
23
where B > 0 is a learnable parameter controlling how tightly the density concentrates around the base mesh (smaller (3 yields sharper concentration).
parameters are initialized on the base mesh and copied to different layers during rendering.
3.2 DIFFERENTIABLE MESH SPLATTING
Given the softened mesh, render its semi-transparent layers into images with splatting-based, differentiable pipeline that treats triangle faces as primitives. Using tile-based rasterization, we project triangle vertices to the image plane, find the triangles overlapping each pixel, and sort them from near to far by depth. For triangle with vertices {vi, viz, vś)) covering pixel p, we find the ray- triange intersection x; and compute its barycentric coordinates W; = (W1, W2, Wg) with respect to the projected vertices {uI U2 Uz ), corrected by their depths {21, 22, 23 } as in Gu et al. (0025)
Wi = correct (p, {n, u2 uz}. {21, B2, 2312)
(4)
With wi, we obtain the per-intersection attributes {at, f,, nt Fi. xi} by barycentric interpolation of the vertex-attached parameters. The color at the intersection then predicted by an MLP:
MLP (f,, nj, F1, Hash(x)))
(5)
where we use hash-encoded coordinate features Hash(x;) Müller et al. (2022) to inject nonlinearity and avoid overly smooth interpolation within triangles.
Finally, composite all overlapping triangles per pixel using the volumetric rendering equation:
Cp
Σ c. (1- ak). iEN
km
where A is the set of overlapping triangles sorted from near far. The rendered image is supervised by the ground-truth image via a photometric loss, and gradients backpropagate through the layers to update the base mesh and its attached parameters.
5

---

Published as a conference paper at ICLR 2026
3.3 HYBRID TOPOLOGY CONTROL
Direct mesh optimization is prone to defects that are difficult to repair. Following Nvd- iffRec Munkberg et al. (2022), reparameterize the surface via Deep Marching Tetrahedra (DMTTet) Shen
Shen et al. (2021) during the early stage to stabilize topology. Concretely, we initialize tetrahedral grid with aagned-distane values and extract the base mesh using DMTet. For grid vertex at coordinate Xg. we initialize the SDF as ||x, O yielding an imitial spherical sur- face of radius centered at Appearance-related parameters are stored on tetrahedral vertices and interpolated to mesh vertices.
This tetrahedral reparameterization provides robustness to topological artifacts early in training- Note that the SDF on tetrahedral vertices is unrelated to the signed-distance computation for soft- ened mesh vertices equation 2 the former stabilizes early geometry extraction, while the latter generates optimization gradients for the base mesh.
Because DMTet's resolution scales cubically and conflicts with explicit topology control (c.g., isotropic remeshing Hoppe et al. (1993)), adopt the following strategy: after the DMTet stage converges, we freeze the mesh extracted from DMTet as a base mesh and disable further DMTet reparameterization. We then switch to Continuous Remeshing Palfinger (2022) explicitly adjust topology and improve element quality, applying remeshing after each optimization step to maintain near-isotropic triangles and reduce defects.
3.4 IMPLEMENTATIONS
Supervision. Our approach inherits benefits from both surface and volumetric formulations. In addition to the image loss on volumetrically rendered images, we rasterize the base mesh Laine al.) (2020) and apply shading supervision following AMLS-Spplatte Yang et al. (2025) and monocular normal supervision following GaussianSurfell Dai et (2024). We also include mesh smoothness loss from Py Torch33 Ravi et al. (2020).
Hyperparameters. We initialize DMTet at resolution 128 within a 2.5 m bounding box (object- centric). We train with DMTet for 5,000 iterations, then remove it and continue for 10,000 iterations with Continuous Remeshing; the minimum edge length is controlled to be approximately 5 mm. The base mesh is softened into 5 layers with offsets limited to 10 em. Training is performed single NVIDIA V100 (32 GB) GPU and takes about 20 minutes per scene.
4
EXPERIMENTS
4.1
EXPERIMENTAL SETUP
The main contribution of our method soften mesh, bridging the gap between surface and volu- metric representations. This increases the effective 3D receptive field for detailed geometry recovery while maintaining the topology-conte constructions. To showcase reconstructed detail, evaluate object-centric datasets and report Chamfer Distance (in cm) and vertex counts: DTU Jensen et al. (2014). which features complex illumination and relatively simple geometry, and BlendedMVS Yao et al. (2020). which contains more intricate shapes. Although our approach is not theoretically confined to object-centric settings, it can be applied to scene-level datasets with modest modifications the mesh reparameterizzttio (as in IMLS-Splatting Yang et al. (2025)). To validate this, we initialize scene-level experiments with coarse meshes produced by GaussianSurfel Dai et al. (2024) and further optimize them using
capabilities of meshes, enabling accurate, high-quality re-
our method, yielding realistic reconstructions, as shown in Fig. g
For comprehensive comparison, we include representative methods from multiple paradigms: im- plicit (NeuS Wang et al.) (2021)). hybrid (Neuralangelo Li et al. (2023)), explicit Gaussian- (GOF Yu et al. (2024). GaussianSurfel Dai al. (2024). 2DGS Huang al. (2024), ), and mesh-
based (SuGaR Guédon & Lepetit 2023 IMLS-Splatinn Yang et al. (2025).
4.2 COMPARISON WITH SOTA METHODS ON OBJECT-CENTRIC DATASETS
In Fig. we illustrate beshing-inducce error accumulation and unnecessarily dense meshes in volumetric pipelines, well the limitations of prior mesh-based methods. The quantitative com-

---

Published as a conference paper at ICLR 2026
Table 1: Surface reconstruction accuracy on DTU Jensen al. (2014) dataset. Best results are
highlighted as 1st and 2nd Approximate vertex counts (in thousands) and training time (minutes) shown on the right.
Training
Neu52
0.55
0.75
092
0.71
1.22
0.89
0.55
0.20
1000
Newaakugelo
[0.74
0.69
0.62
1000
600
Sarfel
0.8
1.20
200C
0.09
0.79
0.6
0.92
1.24
1.28
0.6
0.64
1.58
0.45
0.50
0.78
500
COF
0.50
0.82
1.18
1.29
090
0.34
SML.S-pphating
0.52
1.02
0:32
0.56
1.15
00.76
0.67
0.37
Ouzs we XS
0.499
1,00
0.86
0.40
0.85
0.99
0.63
1.23
1.24
0.62
0.67
0.63
0.35
0.51
0.49
0.73
300
20
10:6
0.73
1.00
0.955
0.59
0.62
Table 2: Surface reconstruction accuracy on BlendedMVS Yao et al. 2020 dataset. Best results are
highlighted as 1st and 2nd
Rockeihl
Bear
Caraec
Bugge
Fraxpiy
Eseno
Min
Manso
Sanw
Nas
7.66
1:00
3.54
3:14
3.51
3.76
1.47
3.50
2.68
Sursa.
1:59
-11
3.31
3.37
3.95
4.01
1099)
1826
(3:40]
1130
2:46
IMISS SelSS-pparigg
7.48
1.86
7.60
361
3.66
3.60
3.85
2.33
3.90
3.74
7.85
1.26
3.00
5.10
3.61
1.40
3.09
7.84
2.75
Oas Wh
1:41
1:34
0.84
2.0
2.38
1.52
2.00
2.59
1.34
2.58
096
3.56
1.72
1.59
1.58
1.52
1.94
Oat
8:36
10
1.30
1.57
2.36
8.90
(090
1.34
920
1.30
1058
1.7
parisons in Tables and 2 further show that our method reaches state-of-the-art (SOTA) accuracy with fewer vertices, attributable to explicit control over mesh quality and topology. We note slight accuracy gap versus IMLS-Splatting Yang et al. (2025) on DTU (Table LMLS-Splatting converts point clouds to grid, extracts meshes, and optimizes them with shading loss. To isolate the ef- fect of our softening- based volumetric supervision from 3D representation choices, we ablate the softening stage and train with shading-only supervision ("Ours w/o MS" in Tables [nnd] and 2). This ablation underperforms the full model, indicating that our end-to-end volumetric optimization pro- vides stronger geometric supervision than shading alone. We expect further gains if our softening mechanism is paired with more flexible 3D parameterizations like those used in IMLS-Splattine
Visual comparisons highlight the qualitative advantages of our approach. In Fig. our method recovers clean, smooth surfaces and fine structures, such as the window frames in Scan24, scissor blades in Scan37, the eyes Scan/06, and facial lines in Scan/ 14. By contrast, GaussianSur- fel Dai et al. (2024) and GOF Yu et al. (2024) represent scenes with discrete Gaussian primitives that lack the inherent surface smoothness surface artifacts in Scan/ /4, as well as missing details like the eyes in Scan /06 and smile lines in Scanl 14. These failures are consistent with error accumulation introduced by downstream mesh- ing. On BlendedMVS (Fig. 55.. broken bases (c.g., Dragon) and missing indentations (c.g., Stone) similarly reflect the lack of surface smoothness and meshing-inducee artifacts.
meshes, leading to broken structures in Scan37 and
Compared to IMLSSSplating Yang al. (2025) in Fig. 5 we observe the limitations of shading-on only supervision, such as noisy surfaces on Monster and Stone. Moreover, the grid resolution in IMLS- Splatting scales cubically, whereas our pipeline optimizes pure mesh in later stages, enabling higher vertex densities to capture more detail. To further illustrate the limitations of shading-only training, we show "Ours w/o MS" in Fig. (5) although this variant uses denser meshes, it still fails to reproduce the indentation at the top of Stone. This confirms that without the multi- softening (and its enlarged 3D receptive field), shading-only supervision insufficient for reliably recovering intricate geometry.
4.3 RESULTS ON NERF SYNTHETIC DATASET
We provide results on the NeRF Synthetic Dataset Brrtin- et al. (2021) to highlight our method's effectiveness in capturing thin structures. As shown in Fig. [6] we compare our approach with Gaussian Surfel Dai et al. (2024). The results demonstrate that method can accurately represent thin structures and consistently outperforms Gaussian Surfel, particularly for the mast of the ship and the vase in the ficus example. Furthermore, our approach achieves these improvements with significantly reduced vertex counts and optimized mesh topology, as evident in the leaf blade of the ficus.
These improvements can be attributed to two main reasons. First, our method introduces accurate volumetric supervision meshes, enabling the model to learn flexible geometric details, such as
7

---

Published as a conference paper at ICLR 2026
Seat
Scarl
Inpur Image
Ours
S-p.S-Sphiiing
Gursinnuuril
GOF
Figure 4: Qualitative comparison on DTU dataset.
Dragon
Monseer
Stone
Imput Imauge
IMLS- SMplatipatinng
Figure 5: Qualitative comparison on BMVS dataset.
the vase's wrinkles and the ship's mast. Second, by employing remeshing during optimization and circumventing post-optimization meshing, our method achieves better mesh topology with fewer vertices, which is vital for downstream applications such physical simulation.
We also observe that the cable the ship is not produced by either method. For our method, this indicates that isotropic remeshing may not be well-suited for extremely thin structures, such as cables or other fine structures like human hair. In this case, adaptive remeshing could be explored to generate slender triangular faces for cable-like structures while maintaining isotropic faces for flat surfaces. For Gaussian Surfel, although Gaussian ellipsoids can positioned to represent cables, fails to reconstruct meshes from them due limited meshing resolution, resulting in accumulated errors from ellipsoids--o-nee conversion.
4.4 ABLATION
4.4.1
RENDERING EFFICIENCY: MESII SPLATTING VS. ITERATIVE MESII RASTERIZATION
To assess the efficiency of our splatting-based renderer, we implement an alternative based on iterative mesh rasterization, using depth peeling Nydiffrast [aine al.) (2020), to render the softened mesh (i.e., multiple semi-transparent layers) into images, Table 3 reports results on DTU scan/22 and includes Gaussian Splatting (as implemented in Gaussiannurfel Dai et al. 2024 reference. The original image olution 1600 1200; we evaluate at 1/4, 1/2, and full resolution, and report GPU memory usage and training time for each method.
Table 3: Memory and training time for Mesh Splatting (MS), Iterative Mesh Rasterization (IMR), and Gaussian Splatting (GS) on DTU scan at different image scales.
Memory (GB) L
Training (Minutes) |
Resize Scale
1/4
172
1/4
172
GS
2.
5
13
IMR
8
25
0OM
40
90
N/A
MS
2
13
12
15
22
8

---

Published as a conference paper at ICLR 2026
Image
Figure 6: Reference results on the NeRF Synthetic Dataset Martinn-rualley et al. (2021). We present results for the ship and ficus examples better visualize thin structures.
The results indicate that Mesh Splatting (MS) is significantly more efficient than iterative mesh rasterization (IMR). At 1/4 resolution, MS consumes 2 GB of memory compared to 8 GB for IMR, and on an NVIDIA V100 (32 GB) GPU, IMR runs out of memory (OOM) at full resolution, whereas MS remains feasible. Gaussian Splatting (GS) is still more efficient than MS, suggesting room for engineering improvements to the MS implementation, such as culling invisible triangles and adaptively controlling mesh vertex density.
w/o DMTet
[1
(256)
Mesh
Mesh
Model
Figure 7: Qualitative ablations. Hybrid topology control (DM'Ter + Continuous Remeshing) cap- tures global topology and fine details more reliably than using either component alone.
Ref. Image
Initia Ezation GaussianSurfll
Optimized w/ Ours
Figure 8: Reference results on scene-level datasets. Initial coarse meshes from GaussianSurfel Dai al. (2024) refined with softening and splatting, plus Continuous Remeshing. The data is from Mip-NeRF360 Barron et al. (2022) and TandT Knapitsch et al. (0177)
4.4.2 EFFECT OF HYBRID TOPOLOGY CONTROL.
We combine DMTee Shen al. 2021 (early stage) with Continuous Remeshing Palfinger (022)) (later stage) to jointly stabilize global topology and improve element quality. As shown in Table and Fig. 7 removing DMTet ("w/o DMTet") fails to capture global topology (e.g., holes), whereas using DMTet alone resolution 128 ("DMTet (128)") yields sparse meshes and inferior accuracy. Increasing to resolution 256 improves accuracy but still lacks fine detail, and its training time already comparable to the full model due to cubic scaling. In contrast, the hybrid strategy captures global topology with sparse DMTet and recovers fine detail with explicit remeshing, providing the best overall accuracy and mesh quality.

---

Published as a conference paper at ICLR 2026
Table 4: Ablation metrics.
w/o DMTet
DMTer (128)
DMTet (256)
Donse Mesh
Sparse Mesh
Full Model
Mcmory(GB)
7
6
28
15
23
Training
Vertices
80K
2K
10K
487K
127K
306K
4.4.3 VERTEX NUMBER CONTROL
Continuous Remeshing exposes a minimum edge-length parameter that directly controls mesh den- sity. As seen in Table 4 and Fig- our method is robust vertex count within a wide range. In practice, we set the minimum edge length to approximately 5 mm, yielding around 300k vertices per object and favorable balance between accuracy and efficiency.
4.5 LIMITATIONS
While the proposed framework- mesh softening, splatting- based rendering, and hybrid topology control-is general, scalability to very large scenes can be constrained by tetrahedral-gric resolution and GPU memory. This limitation does not affect our central contribution: converting mesh into pseudo-volumetric multi-layer representation increases the effective 3D receptive field and enables image-supervised optimization while preserving mesh characteristics and topology control.
To illustrate applicability in scene-level settings, we initialize with coarse meshes obtained from GaussianSurfel and then optimize them using our softening and splatting modules. with Continuous Remeshing to maintain element quality. As shown in Fig. our method consistently adds geometric detail over the initialization. A remaining failure case occurs when the base mesh far from the true surface (e.g., distant background): softening into thin band around the base surface may not sufficiently overlap the target region to provide volumetric gradients. Addressing this at scale will likely require modifying the mesh reparameterization and sampling strategy (e.g., adaptive layer bandwidths bierarchical softening), which we leave for future work.
5 CONCLUSION
Mesh Splatting softens a base mesh into differentiable semi-transparent layers and renders them volumetrically, enabling end-to-end mesh optimization with controllable 3D receptive field and direct image supervision. With an efficient splatting renderer and hybrid topology control (DMTet early, Continuous Remeshing later), we achieve strong accuracy with fewer vertices and shorter training time; ablations confirm the importance of multi-layer softening and the superiority of the hybrid control. Future work will scale larger scenes and further optimize the renderer (e.g., triangle culling and adaptive density).
ACKNOWLEDGMENTS
This research was supported by the Theme-based Research Scheme, Research Grants Council of Hong Kong (T45-205/21-N), and the Guangdong and Hong Kong Universities "1+1+1" Joint Re- search Collaboration Scheme (2025
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Rameen Abdal, Wang Yifan, Zifan Shi, Yinghao Xu, Ryan Po, Zhengfei Kuang, Qifeng Chen, Dan Yeung, and Gordon Wetzstein. Gaussian shell maps for efficient 3d human generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp- 9441-9451, 2024.
Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf' 360: Unbounded anti-aliased neural radiance fields. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 5470-5479, 2022.
Blender. Blender. https:/ www. http:/wwwwwww........mmm.mmm..ep org/ 2024.
Danpeng Chen, Hai Li, Weicai Ye, Yifan Wang, Weijian Xie, Shangjin Zhai, Nan Wang. Haomin Liu, Hujun Bao, and Guofeng Zhang. Pgsr: Planar-based gaussian splatting for efficient and high-fidelity surface reconstruction. IEEE Transactions on Visualization and Computer Graphics, 2024.
Pinxuan Dai, Jiamin Xu, Wenxiang Xie, Xinguo Liu, Huamin Wang, and Weiwei Xu. High-quality surface reconstruction using gaussian surfels. In ACM SIGGRAPH 2024 Conference Papers, pp- 1-11, 2024.
Ainaz Eftekhar, Alexander Sax, Jitendra Malik, and Amir Zamir. Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans. In Proceedings of the IEEE/CVF International Conference Computer Vision, pp. 10786-10796, 2021.
Stefano Esposito, Anpei Chen, Christian Reiser, Samuel Rota Bulò, Lorenzo Porzi, Katja Schwarz, Christian Richardt, Michael Zollhöfer, Peter Kontschieder, and Andreas Geiger. Volumetric sur- faces: Representing fuzzy geometries with layered meshes. arXiv preprint arXiv:2409.02482, 2024.
Qiancheng Fu, Qingshan Xu, Yew Soon Ong. and Wenbing Tao. Geo-neus: Geometry-consistent neural implicit surfaces learning for multi-view reconstruction. Advances in Neural Information Processing Systems, 35:3403-3416, 2022.
Chun Gu, Zeyu Yang, Zijie Pan, Xiatian Zhu, and Li Zhang. Tetrahedron splatting for 3d generation. Advances in Neural Information Processing Systems, 37:80165 17:80155-80190, 2025.
Antoine Guédon and Vincent Lepetit. Sugar: Surface-aligned gaussian splatting for efficient 3d
mesh reconstruction and high-quality mesh rendering. arXiv preprint arXiv:2311.12775, 2023.
Antoine Guédon and Vincent Lepetit. Gaussian frosting: Editable complex radiance fields with real-time rendering. In European Conference on Computer Vision, pp. 413-430. Springer, 2024.
Minghao Guo, Bohan Wang. Kaiming He, and Wojeiech Matusik. Tetsphere splatting: Representing high-quality geometry with lagrangian volumetric meshes. arXiv preprint arXiv:2405.20283, 2024.
Hugues Hoppe, Tony DeRose, Tom Duchamp, John McDonald, and Werner Stuetzle. Mesh opti- mization. In Proceedings of the 20th annual conference on Computer graphics and interactive techniques, pp. 19-26, 1993.
Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically accurate radiance fields. In ACM SIGGRAPH 2024 Conference Papers, pp. 2024.
Rasmus Jensen, Anders Dahl, George Vogiatzis. Engin Tola, and Henrik Aanæs. Large scale multi- view stereopsis evaluation. In Proceedings the recognition, pp. 406-413, 2014.
the IEEE conference on computer vision and pattern
Hiroharu Kato, Deniz Beker, Mihai Morariu, Takahiro Ando, Toru Matsuoka, Wadim Kehl, and Adrien Gaidon. Differentiable rendering: A survey. arXiv preprint arXiv:2006. /205,, 2020.
11

---

Published as a conference paper at ICLR 2026
Michael Kazhdan and Hugues Hoppe. Screened poisson surface reconstruction. ACM Transactions on Graphics (ToG), 32(3):1-13, 2013.
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian splat- ting for real-time radiance field rendering. ACM Transactions on Graphics, 42(4), 2023.
Arno Knapitsch, Jaesik Park, Qian-Y Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics (ToG), 36(4):1-13, 2017.
Youngjoong Kwon, Lingji Liu, Henry Fuchs, Mare Habermann, and Christian Theobalt. Deliffas: Deformable light fields for fast avatar synthesis. Advances in Neural Information Processing Systems, 36, 2024.
Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Scol, Jaakko Lehtinen, and Timo Aila. Modular primitives for high-performance differentiable rendering. ACM Transactions on Graphics, 39(6), 2020.
Marc Levoy and Pat Hanrahan. Light field rendering. In Seminal Graphics Papers: Pushing the Boundaries, Volume 2. pp. 441-452. 2023.
Zhaoshuo Li, Thomas Müller, Alex Evans, Russell H Taylor, Mathias Unberath, Ming- Yu Liu, and Chen -suun Lin. Neuralangelo: High-fidelity neural surface reconstruction. Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 8456-8465, 2023.
Shichen Liu, Tianye Li, Weikai Chen, and Hao Li. Soft rasterizer: A differentiable renderer for image-based 3d reasoning. In Proceedings of the IEEE/CVF International Conference on Com- puter Vision, pp. 7708-77077777 7718-771, 2019.
Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. SMPL: A skinned multi-person linear model. ACM transactions on graphics, 34(6):1-16, 2015.
William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. Seminal graphics: pioneering efforts that shaped the field, pp. 347-353. 1998.
Ricardo Martin-Brualla, Noha Radwan, Mehdi SM Sajjadi, Jonathan T Barron, Alexey Dosovitskiy, and Daniel Duckworth. Nerf in the wild: Neural radiance fields for unconstrained photo collec- tions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7210-7219, 2021.
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NERF: Representing scenes as neural radiance fields for view synthesis. In European conference on computer vision, pp. 405-421, 2020.
Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics prim- itives with multiresolution hash encoding. ACM Transactions on Graphics (ToG), 41(4):1-15, 2022.
Jacob Munkberg, Jon Hasselgren, Tianchang Shen, Jun Gao, Wenzheng Chen, Alex Evans, Thomas Müller, and Sanja Fidler. Extracting triangular 3d models, materials, and lighting from images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp- 8280-8290, 2022.
Baptiste Nicolet, Alec Jacobson, and Wenzel Jakob. Large steps in inverse rendering of geometry. ACM Transactions on Graphics (TOG), 40(6):1-13, 2021.
Werner Palfinger. Continuous remeshing for inverse rendering. Computer Animation and Virtual Worlds, 3315), 2022.
Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan- Yen Lo, Justin Johnson, and Georgia Gkioxari. Accelerating 3d deep learning with pytorch3d. arXhn:2007.08501, 2020.
Gopal Sharma, Daniel Rebain, Kwang Moo Yi, and Andrea Tagliasacchi. Volumetric rendering with baked quadrature fields. In European Conference on Computer Vision, pp. 275-292 Springer, 2024.
12

---

Published as a conference paper at ICLR 2026
Tianchang Shen, Jun Gao, Kangxue Yin, Ming- Yu Liu, and Sanja Fidler. Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis. Advances in Neural Information Processing Systems, 34:6087-6101 2021.
Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. arXiv preprint arXiv:2106. 10689, 2021.
Zian Wang, Tianchang Shen, Merlin Nimier-David, Nicholas Sharp, Jun Gao, Alexander Keller, Sanja Fidler, Thomas Müller, and Zan Gojcic. Adaptive shells for efficient neural radiance field rendering. arXiv preprint arXiv:2311.100% 2023.
Kaizhi Yang, Liu Dai, Isabella Liu, Xiaoshuai Zhang, Xiaoyan Sun, Xuejin Chen, Zexiang Xu, and Hao Su. Imls-splatting Efficient mesh reconstruction from multi-view images via point representation. ACM Transactions on Graphics (TOG), 44(4):1-11, 2025.
Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, and Long Quan. Blendedmvs: A large-scale dataset for generalized multi-view stereo networks. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 1790-1799, 2020.
Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendeingg of neural implicit surfaces. Advances in Neural Information Processing Systems, 34:4805-4815, 2021.
Wang Yifan, Felice Serena, Shihao Wu, Cengiz Öztireli, and Olga Sorkine Hornung. Differentiable
surface splatting for point-based geometry processing. ACM Transactions Graphics (TOG),
3814):1-1,, 2019.
Zehao Yu, Torsten Sattler, and Andreas Geiger. Gaussian opacity fields: Efficient adaptive surface reconstruction in unbounded scenes. ACM Transactions on Gruphics (ToG), 43(6):1-13, 2024.
Zhengyou Zhang. Microsoft kinect sensor and effect. IEEE multimedia, 19(2):4-10, 2012.
Qian Yi Zhou, Jaesik Park, and Vladlen Koltun. Open3d: A modern library for 3d data processing. arXiv preprint arXiv: /at///0011.0077 2018
13

---

Published as a conference paper at ICLR 2026
A PSEUDO CODE FOR TRAINING
To aid understanding of our method, present pseudocode for Mesh Splatting Training.
Algorithm 1: Mesh Splatting Training
Input:
Tetrahedral grid with vertex SDF S;
Training images {T&T) } and cameras {C");
Total iterations Т; warmup TMM remesh period T Temeshi
Number of soften layers N; renderer Raplati Photometric loss p(:
Output: Final base mesh Mo
for t to I do
// Topology control schedule
if < TArTet then
// Early stage: re-extract mesh from current SDF
Mo + (M
else
// Late stage: refine mesh quality/topology
Mo + Remeshing(MM)
end
// Generate softened layers on-the-fli fly
{M,) + Mo,ten(Mu,.N)
// Render a view
Select view V 1- pree Raplan (MM (Mo
// Photometric loss
L+ Alpread 18r)
// Backpropagation and parameter updates
if t < TDMTE then Update S using V
else
Update Mo (vertex positions/attriiutess) using V.M L
end end
B COMPARISON WITH NEURALANGELO
Input Image
Ours
Neuralangelo
Input Image
Ours
Neuralangelo
Figure
Qualitative comparison on Neuralangelo.
We additionally compare against Neuralangelo Li et al. (2023). Owing to its prohibitive training time, rely the Neuralangelo results reported by IMLS- IMLS-Splating Yang al. (2025). As shown in Table ! our method achieves comparable accuracy while using far fewer vertices and substan- tially shorter training time. Qualitatively (Fig. 9. reconstructions capture details par with
14

---

Published as a conference paper at ICLR 2026
Neuralangelo (e.g., the hands), and exhibit smoother surfaces in reflective regions, seen in the two leftmost examples.
C SHADING SUPERVISION
In addition to the image reconstruction loss computed on volumetrically rendered images, our method incorporates direct shading supervision the rasterized base mesh, closely following IML.S-platting. Specifically, we utilize Differentiable Mesh Rasterization Laine et al. (220)) to project the base mesh onto the image plane, producing foreground mask In R
mal map In E x x3 and 2D feature map Iç E R x (interpolated from vertex-attached features). Similar to IMLS-Splatting erties at each pixel, including diffuse color ca. specular tint s, and specular feature fg via an MLP 4. Additionally, we employ lightweight MLP 4 to predict the specular color for each pixel given its specular feature fs. view directions w, and reflection direction wy, enabling efficient modeling of complex occlusions and view-dependent effects. The final color for each pixel computed as:
decode the feature map into spatially-varying surface prop-
C ca
where denotes the direction vector from the camera to the corresponding surface point the mesh, and the reflect direction is defined as Wr 2(-w n)n + w, with surface normal n derived from In. The final rasterized color image is obtained by applying the foreground mask Im to the computed color с at each pixel.
D OTHER VISUALIZATION RESULTS
We provide the mesh files used the teaser in the Supplementary Material: meshes generated by GaussianSurfel, Neuralangelo, and method (SuGaR is omitted due to its large file size). We also include a mesh produced by Poisson reconstruction of the ground-truth point clouds, and video showing the optimization process for the BlendedMVS Yao et al. (2020) dragon, which takes approximately 23 minutes.
E STATEMENT ON THE USE OF LARGE LANGUAGE MODELS
We would like to clarify that Large Language Models (LLMs) were exclusively utilized for linguistic refinement and the polishing of the manuscript. LLMs were not involved in the development of the research concepts, experimental design, analysis, or the formulation of the core ideas presented in this paper. All scientific contributions and methodological innovations originated solely from the authors.
15


Reviews:
[{"review_id": "JcA2uyPMxp", "reviewer": "Reviewer_Cj7U", "summary": "*Motivation:*\n- volumetric methods produce overly dense sampling and can be hard to mesh\n- surface methods do not req meshing but make it hard to model intricate geometric details\n\n*Method:*\n- authors introduce a \"soft mesh\" representation that comprises multiple semi-transpatent\nmesh layers that allow for underlying base mesh to be optimized\n- this mesh representation is optimized via \"differentiable Mesh Splatting\" - a differentiable\nrasterization algorithm that supports transpatent triangles, directly supervised by photometric loss. The final color is obtained by passing a set of interpolated (via barycentrics) features to an MLP and integrated via volumetric rendering.\n\n*Evaluation:*\n- Method performs favorably compared to several strong baselines (SuGaR, 2DGS, etc)", "questions": "1. Is the \"receptive field\" a common terminology in the context of geometry representations?\n- L034-035: \"have a large receptive field along rays\" - unclear what is meant here?\n- L037: \"... only boundary geometry without \"\nBoth remarks are a bit confusing (and both abstract and intro contain the exact same wording which does not make it any more clear in intro) because some volumetric surface representation (e.g. SDF) also in a way only model boundary / surface.\nWhy relying on priors is a inherently bad thing? And what exactly is meant by \"shading or normals\"? Does this mean that one has to introduce an image formation model for the underlying\nmesh representation to be optimizable? Clarification of this would be appreciated.\n\n2. What is the main bottleneck in the speed/memory wrt to e.g. Gaussian splatting?\nAuthors did allude that implementation can be improved, but it would be helpful\nto understand if there are any fundamental constraints? E.g. since there is a need for sorting triangles - potentially one might have performance issues on object borders/scenes where large\nnumber of triangles fall in the same pixel.\n\n3. Why not have the same set of methods in evaluation on different benchmark datasets (Table 1 vs Table 2)?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "*Clarity:*\n- The method description is clear and experimental evaluation is overall well-documented.\n\n*Originality / significance:*\n- Proposed approach is interesting as it can be seen as a hybrid formulation\nbetween volumetric and surface-based representations, and allows for direct\nmesh extraction (which is very useful e.g. for physics simulations).\n\n*Evaluation:*\n- Method performs favorably with respect to several strong baselines on\nsurface reconstruction task.\n- The mesh splatting method seems more efficient than existing\ndifferentiable rendering approaches (nvidiffrast).", "weaknesses": "*Clarity/Motivation:*\n- The motivation for the approach is not particularly clear - authors point out to\nissues with volumetric rep-s and mesh rep-s, and use the concept of receptive field - which is\nnot clearly defined. My best guess to what they mean is: meshes are typically optimized per-vertex - and thus the corresponding parameterization has a large number of effective degrees of freedom, and thus. However, there is a number of very established parameterizations (control points, ARAP, spectral methods) that do not share this weakness. Similarly, additional\nreguralization terms and multi-node constraints (e.g. local smoothess) effectively act\nas a way to restrict the \"receptive field\" (aka reduce effective degrees of freedom).\n\n*Method Limitations:*\n- As indicated by authors themselves (Sec 4.4), the method is severely limited by the\nquality of the underlying base mesh - so it would not work well in settings with poor quality\nof base meshes obtained by off-the-shelf mesh reconstruction. Because of this, the representation and algorithm introduced in the paper itself can be considered more\nof a mesh refindement strategy than a standalone algorithm.\n- (minor) 20 minutes per scene does not seem like performance suitable\nfor some real-world applications.\n\n*Novelty:*\n- (minor) The idea of using a soft mesh is not particularly new: e.g. [Esposito'2024] introduces\na very similar representation. And more generally, it can potentially be seen as a variant of control point parameterization or neural cages [Yifan'2020].\n- (minor) MeshSDF [Remelli'2020] introduces a differentiable iso-surface extraction method, which allows differentiating through meshing. This sounds like a relevant related work.\n\n*Experimental Evaluation:*\n- A reasonable set of baselines is used for volumetric reconstruction, but potentially it\nwould also be interesting to compare against other differentiable rendering frameworks with \"standard\" differentiable rendering (nvdiffrast or DRTK) and an off-the-shelf mesh parameterization (e.g. mesh + smoothness).\nI do appreciate the performance comparison with nvdiffrast-based iterative rendering implementation, but I wonder if there is a way to dissect\nquality wrt to a) mesh representation b) rendering algorithm.\n- It is not fully clear why the set of methods is different between Table 1 and Table 2.\n- I understand that authors focus specifically on mesh reconstruction, but since the method\nactually uses volumetric rendering and produces opacity and color, it seems natural to conduct\nevaluation in terms of rendering quality - this might also bring up potential issues on complex\ngeometries for which meshes are a poor representation. I believe it would be helpful for the readers to understand if the method is also useful as a NVS approach.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Amb6BhG0FR", "reviewer": "Reviewer_q1sR", "summary": "The authors propose a method for reconstructing 3D shapes from multi-view images. They introduce a pseudo-volumetric representation called a soft mesh, assigning per-vertex opacity and features to enable alpha blending. Furthermore, they use DMTet to initialize mesh topology and continuously remesh the shape to improve topological stability. Experiments show that the proposed method achieves high quality shape reconsturction on DTU and BlendedMVS datasets.", "questions": "The author should provide more explanation. I am open to adjusting my rating since representing 3D scenes with multi-surface is critical and might be a potential research direction in the future, although the performance of the proposed method is poor.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Strengths\n1.The paper shows that optimizing the proposed soft mesh outperforms optimizing a single layer.\n2.The reconstruction is both fast and memory-efficient.", "weaknesses": "Weeknesses:\n1.The performance advantage of the soft mesh is not well justified. While the authors claim that its pseudo-volumetric nature facilitates optimization, an alternative is to convert the base mesh into an SDF and train with NeuS or VolSDF, which remain differentiable with respect to the mesh. The proposed soft mesh seems to approximate this idea primarily for faster rendering. Intuitively, the SDF-based variant should be weaker than standard NeuS/VolSDF because it is constrained by the topology of the base mesh and thus sacrifices flexibility; nevertheless, the reported results indicate the soft mesh surpasses NeuS. Please explain the mechanisms behind this improvement.\n2.The procedure for optimizing DMTet in the initial 5,000 iterations is not described. Please elaborate on the optimization objectives, and constraints used during this phase.\n3.The explanation of the alpha weights is insufficient. The method adopts VolSDF’s mapping, which was originally used to compute attenuation coefficients; the authors should justify its suitability for alpha weights, additionally clarify whether the expected depth or the most visible point lies on the base mesh.\n4.The paper does not report first-stage reconstruction quality. Including quantitative metrics for this stage would clarify the effectiveness of the subsequent stage.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8XSU3ottsF", "reviewer": "Reviewer_hBem", "summary": "This paper proposes a novel framework that bridges the gap between volumetric and surface-based 3D reconstruction methods. The core idea is to soften a base mesh into multiple semi-transparent, differentiable layers, effectively transforming it into a pseudo-volumetric representation while preserving the explicit structure of the mesh. These softened layers are rendered through a splatting-based differentiable renderer and optimized end-to-end with image-level supervision. By combining volumetric gradient propagation with explicit topology control, the method achieves high-quality surface reconstructions with fewer vertices and significantly reduced training time compared to existing volumetric or mesh-based approaches.", "questions": "1.  The paper primarily evaluates object-centric datasets, while scene-level reconstructions are only briefly discussed in the ablation section. For larger-scale environments (e.g., Mip-NeRF360), it would be helpful to clarify how the authors adapt or extend the DMTet initialization to achieve more stable and accurate reconstruction at scene scale.\n2. Since the framework relies on DMTet for base mesh reconstruction, exploring FlexiCubes as an alternative could potentially enhance the quality and robustness of the initial mesh. Such a comparison or discussion would further strengthen the technical completeness of the work.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The proposed concept of differentiably softening meshes into volumetric layers is both innovative and practical. It effectively bridges the gap between surface- and volume-based approaches, enabling volumetric supervision while preserving explicit mesh controllability and topology refinement.\n2. The paper demonstrates convincing quantitative and qualitative results on the DTU and BlendedMVS benchmarks, showing clear visual and numerical improvements over prior methods.\n3. The overall presentation is clear, well-structured, and easy to follow.", "weaknesses": "1. Since the proposed framework claims to reconstruct intricate geometric details, I recommend including comparisons on the Ship or Ficus scenes from the NeRF Synthetic dataset, as these cases involve thin structures that are particularly challenging to recover accurately.\n2. The overall novelty of the framework appears slightly limited, as the pipeline is largely built upon DMTet and splatting. Prior approaches such as VMesh [1] and Radiance Surfaces [2] have also explored incorporating volumetric supervision into surface-based representations.\n3. The method still relies on tetrahedral grids, which may restrict its scalability and generalization to large-scale or unbounded scenes, potentially limiting its broader applicability.\n\n[1] Guo, Y. C., Cao, Y. P., Wang, C., He, Y., Shan, Y., & Zhang, S. H. (2023, December). Vmesh: Hybrid volume-mesh representation for efficient view synthesis. In SIGGRAPH Asia 2023 Conference Papers (pp. 1-11).\n\n[2] Zhang, Z., Roussel, N., Muller, T., Zeltner, T., Nimier-David, M., Rousselle, F., & Jakob, W. (2025, August). Radiance surfaces: Optimizing surface representations with a 5D radiance field loss. In Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers (pp. 1-10).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)