DiMeR: Disentangled Mesh Reconstruction Model with Normal-only Geometry Training
Authors: Lutao Jiang
Jiantao Lin
Kanghao Chen
Wenhang Ge
Xin Yang
Yifan Jiang
Yuanhuiyi Lyu
Xu Zheng
LI JING
Yinchuan Li
Ying-Cong Chen
Venue: ICLR 2026 Poster (2026)
Keywords: LRM
3D Reconstruction
3D Generation
Image-to-3D
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: We propose DiMeR, a novel geometry-texture disentangled feed-forward model with 3D supervision for sparse-view mesh reconstruction. Existing methods confront two persistent obstacles: (i) textures can conceal geometric errors, i.e., visually plausible images can be rendered even with wrong geometry, producing multiple ambiguous optimization objectives in geometry-texture mixed solution space for similar objects; and (ii) prevailing mesh extraction methods are redundant, unstable, and lack 3D supervision. To solve these challenges, we rethink the inductive bias for mesh reconstruction. First, we disentangle the unified geometry-texture solution space, where a single input admits multiple feasible solutions, into geometry and texture spaces individually. Specifically, given that normal maps are strictly consistent with geometry and accurately capture surface variations, the normal maps serve as the only input for geometry prediction in DiMeR, while the texture is estimated from RGB images. Second, we streamline the algorithm of mesh extraction by eliminating modules with low performance/cost ratios and redesigning regularization losses with 3D supervision. Notably, DiMeR still accepts raw RGB images as input by leveraging foundation models for normal prediction. Extensive experiments demonstrate that DiMeR generalises across sparse‑views-3D, single‑image-3D, and text‑to‑3D tasks, consistently outperforming baselines. On the GSO and OmniObject3D datasets, DiMeR significantly reduces Chamfer Distance by more than 30%.

Published as a conference paper at ICLR 2026
DIMER: DISENTANGLED MESH RECONSTRUCTION MODEL WITH NORMAL-ONLY GEOMETRY TRAINING
Lutao Jiang Jiantao Lin
Kanghao Chen Wenhang Ge Xin Yang!
Yifan Jiang'
Yuanhuiyi Lyu' Xu Zheng
Jing Li
Yinchuan Li Ying-Cong Chen 1.27
University of Science and Technology (Guangzhou) The Hong Kong University of Science and Technology
The Hong Kong
3 INSAIT, Sofia University "St. Kliment Ohridski" *Knowin 'ByteDance
Figure 1: DiMeR takes the image or text inputs and generates detailed 31) meshes.
ABSTRACT
We propose DiMeR, novel geometry-texture disentangled feed-forward model with 3D supervision for sparse-view mesh reconstruction. Existing methods con- front two persistent obstacles: (i) textures can conceal geometric errors, i.e., vi- sually plausible images can be rendered even with wrong geometry, producing multiple ambiguous optimization objectives in geometry-texture mixed solution space for similar objects; and (ii) prevailing mesh extraction methods redun- dant, unstable, and lack 3D supervision. To solve these challenges, we rethink the inductive bias for mesh reconstruction. First, disentangle the unified geometry- texture solution space, where single input admits multiple feasible solutions, into geometry and texture spaces individually. Specifically, given that normal maps are strictly consistent with geometry and accurately capture surface variations, the normal maps serve the only input for geometry prediction in DiMeR, while the texture is estimated from RGB images. Second, we streamline the algo- rithm of mesh extraction by eliminating modules with low performance/cost ratios and redesigning regularization losses with 3D supervision. Notably, DiMeR still accepts raw RGB images as input by leveraging foundation models for normal prediction. Extensive experiments demonstrate that DiMeR generalises across pparse-vicws-to-3D single-image-to-- performing baselines. On the GSO and OmniObject3D datasets, DiMcR signifi- cantly reduces Chamfer Distance by more than 30 %. Project Page
and text-to-3D tasks, consistently out-
INTRODUCTION
The tasks of 3D reconstruction and generation have garnered significant attention, largely due the advancements made by NeRF (Mildenhall et al. 2021) and 3DGS (Kerbl et al.) 2023). However,
Contribute equally. Corresponding author.

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Published as a conference paper at ICLR 2026
transforming them into the mesh poses a challenge. In this paper, focus mesh representation, which is easy to adapt to downstream applications, such as the gaming industry, VR, robotics, etc.
Enhanced by the introduction of the exten- sive 3D dataset, Objaverse (Deitke et al.) 2023] 2024). numerous 3D reconstruction and genera- tive models emerge. One notable advancement is LRM (Hong et al., 2023), which pioneers the feed-forward generation of NeRF model from
Texture
Geometry
Normal
Texture
Geametry
Normal
(a) Texture Hides Geometry
Input
Prediction
GT
RGB images. Subsequent works (Xu et al.
Generator aererttor
2024a; Wei et al. 2024; Wang et al.] [2255 Yang al., 2024; Liu al., [024]] Ge et al. 2024) extend LRM's 's NeRF representation to mesh. However, two key issues persist in these methods. First, the reliance on RGB images as input leads significant ambiguity in train- ing. As shown in Fig. 2aa), the texture often hides the underlying geometry, thereby leading to the conflict of training objectives exhibited in Fig. b))). Furthermore, as demonstrated in Fig. C), RGB images can be rendered from compositions of multiple wrong geometries and textures, driving the network toward an unde- sirable averaged solution. Second, most of the existing mesh reconstruction methods employ FlexiCubes (Shen et al. 2023) to extract the mesh and utilize differential rasterization for optimization. However, the Signed Distance Field (SDF) grid defined in FlexiCubes only promises the meaning of positive and negative signs for surface extraction, which makes it dif- ficult to apply 3D supervision. Moreover, some of its components are redundant for this task, and its regularization losses lead to serious instability in training.
OK, right
OK, but fail
(b) Training Objective Conflict
Geo-Tex Solution Space
Disentangled Solution Space (Ours)
Geo
Tex
Geo
Tex
One-to-Many
One-to-One (c) Solution Space Disentangle
One-to-One
Figure 2: (a) exhibits difficulty in distinguishing geometry from RGB images. (b) shows the con- flict input-GT pairs in datasets due to problem (a), hindering the training.
(c) illustrates our idea: dis- entangle the mixed solution space containing mul- tiple feasible solutions into two separate spaces with unbiased input. Samples are from the Ob- javers dataset (Deitke et al. 2023).
To solve these two challenges, we propose DiMeR, geometry-texturr disentangled feed-forward sparse-view mesh reconstruction model with 3D supervision. To address the first challenge, training ambiguity, we exploit the inductive bias derived from the consistency between normal maps and 3D geometry. As shown in Fig. 2 (a) and (c), the normal maps consistently align with the surface of the 3D model, offering a more reliable input format for geometry reconstruction. Building on this inductive bias and the Principle of Occam's Razor (Blumer et al. 1987). we disentangle the geometry-texture unified solution space, where multiple solutions correspond to one input, into two individual spaces. To get the geometry, we exclusively utilize normal maps as the sole input, while utilizing RGB images for obtaining the texture. Each of them is trained with task-specific supervi- sion: the geometry branch is constrained by normal, depth, and silhouette-mast losses, whereas the texture branch is guided by appearance- capable of rendering the light map correctly under arbitrary environmental conditions and various materials. Therefore, we add the statistical expectation supervision signal by placing the predicted untextured mesh model in multiple environments with randomly assigned materials. To address the second challenge, limitations remaining in FlexiCubes, we replace the original regularization losses with eikonal loss (Gropp et al.] (2020) and incorporate 3D supervision via ground-truth mesh. Fur- thermore, simplify the modules with low performance/cost ratios. Our improved mesh extraction algorithm allows us a higher extraction resolution, compared with other reconstruction models.
objectives. Moreover, accurate geometry should be
Leveraging recent foundation models for normal map prediction YYe et al., 2024 He et al., 2024). we can generate highly accurate normal maps from RGB images with minimal error and latency (only 200ms). To validate this point and choose the optimal model for DiMeR, we conduct a bench- mark evaluation for object-level normal prediction. To further improve the robustness of DiMeR in practical applications, we introduce noise to the input, normal maps, during training. Equipped with these models, DiMeR also accepts RGB images raw input, the same as other methods. Our DiMeR model capable of effectively handling various tasks, including sparse-view recon- struction, maggleimmae-te and text-to-3D. Extensive experiments demonstrate that DiMeR sig-
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nificantly outperforms previous methods. Specifically, on the GSO dataset, DiMeR reduces Chamfer Distance by 22%. with an upper bound improvement of 32% % when using real normal map inputs. In general, our contributions can be summarized as follows:
Rethinking the inductive bias for mesh reconstruction, we propose DiMeR, disentangled framework to train and predict geometry from normal maps and texture from RGB images separately, with decoupled supervision signals.
We enhance the mesh extraction algorithm for this task, introduce the 3D GT supervision, and Physics-based Rendering Expectation losses.
Numerous experiments demonstrate the superiority and robustness of our DiMeR on re- construction, single-image-to- and text-to-3D tasks. We also conduct a benchmark for the foundation models of normal map prediction in object-level tasks.
RELATED WORKS
2.1 3D GENERATIVE MODELS
Building upon advancements in 2D diffusion models, DreamFusion (Poole al..
2022) introduced
score distillation sampling (SDS) to train 3D representation models like NeRF MMildnnhall et al.
2021] and 3DGS (Kerbl al. 2023) based on text input. Subsequently, numerous methods have
been developed to enhance this approach (Wang et al. 20236 Liang et al. 2023; Chen et al. 2023
Shi al., 2023b Wang et al., 2023a Metzer et al.) 2023 Bai et al. 2023] Jiang et 2024|
[ al,
2025 Tang et al. 2023). However, significant limitation of these methods is the need to train
separate 3D model for each text input, which can take tens of minutes or even hours per text. Some approaches attempt to address this by employing SDS to train a feed-forward network (Lorraine)
jet al. 2023] Jiang & Wang] 2024 Li et al.) 2023b; Qian et al.) 2024), but these are limited to few specific text subjects, reducing the diversity of the outputs. Recently, the introduction of large-scale 3D datasets, such as Objaverse (Deitke et al. 2023 2024), has enabled models like LRMMs (Hong al. 2023 Tochilkin et al. 2024) to explore forward reconstruction from a single image. Following this, several methods have been developed to create sparse-view reconstruction models (Tang et al., 2025 Xu et al. 2024b) Zhang al. [022a]] Li al. (023a) based on NeRF or 3DGS. To support real-world applications, leveraging differential marching cube algorithms (Wei) al. 2023 Shen et al. 2023). some methods focus on direct mesh generation (Xu al., 2024a
Wei et al.] 2024| Wang et al., 2025 Liu et al. 2024 Ge et al., 2024). Additionally. several 3D
diffusion models Zhang et al. 2023 Li et al. 2024b Zhang et al. [024b) Ren et al. 2024 Zhang
& Wonka 2024 Xiang et al.) 2024 Hua et al. 2025 Jia et al. 2025) emerge, but they are limited to the generation task and lack strict correspondence with input images. Morcover, their inference latency ranges from tens of seconds to several minutes. Inspired by auto-regressive models (Tian) 2024 Zhou et al.] (024)) some researchers have shifted focus to mesh AR generation (Siddiqui let al. 2024 Chen et al. 2024b Tang et al. 2024). However, these methods typically require the number of mesh faces to be fewer than 6,000. Concurrently, similar to us, Hi3DGen (Ye et al. 2025) also found that exclusive utilization of normal maps can enhance the quality of geometry and implemented a diffusion model based on this.
ctt al.
In this paper, we focus on feed-forward sparse-view mesh reconstruction. Differently, we disentan- gle the framework into dual branches that predict geometry solely from normal and predict texture from RGB. To ensure that each branch performs its intended role, we assign branch-specific, unam- biguous supervision signals.
2.2 MULTI-VIEW DIFFUSION MODEL
Multi-view diffusion models are designed to generate multi-view images or normal maps from a single image or text prompts, instead of directly producing corresponding 3D models. This approach is gaining popularity due to the relative simplicity of its task definition, where multi-view images are synthesized first, followed by the use of sparse- view reconstruction models to complete the 3D) model generation process. Zero123 (Liu et al.) 2023) introduces explicit control by embedding camera parameters into the conditions of 2D diffusion models. Following, many methods have achieved significant success to synthesis multi-view images and normal maps (Shi et al.) 2023a bj
Li al. 2023a Melas-Kyriazz et al. 2024; Wang & Shi 2023 Voleti et al. 2024; Wu et al. 2024
Li et al. 2024a Long al. 2024| Lu et al.) 2024 Lin et al. 2025). We employ the image-input
2.5D model, such as zero (Shi et al. 2023a) and ERED (Li et al. (024a). to perform the
single-image-t--DD task, while
the text-input 2.5D diffusion model, such as Kiss3DGen,
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Geometry Branch (Sec. 9.1)
GT sDF
Eikonal
Decoter
PBR Ronder
RobiC
Encodor
Spacular
Potch Enbeedding
SBF Gnd
Predicted
Diftuse Light
|Sec. 3.2)
Coordnatess
Rasterice
Different c. T,
Forword Stop
iage Frooder
Triplane
Supervision Signal
Derader
3D Countiratios
SGB Images
Pabch Embedding
Triolane
Festure
Feature
Peoplanl
Resut Image
GT image
Pool Feature
Figure 3: The framework of our DiMeR. The upper part is the geometry branch, and exclusively uses normal maps as input. The lower part is the texture branch.
to accomplish the text-to-3D task. With the continuing progress of such models, DiMeR has the potential to further enhance generation quality.
2.3 NORMAL PREDICTION FOUNDATION MODELS
Surface normals precisely describe local surface variation and orientation, making them crucial for 3D reconstruction. The robustness and accuracy of recent normal prediction foundation models have reached practical levels. Marigold (Ke al. 20244) first integrates diffusion models into depth and normal estimation, by preserving the prior that the visual generative models learned. Subsequent works (Fu et al., 22224] Bae & Davison 2024 Ye et al. 22024, He et al., (024) have further boosted performance while markedly reducing inference latency. Collectively, these advances provide the possibility for high-quality 3D reconstruction exclusively from predicted normal maps, enabling DiMeR with the RGB image input.
METHOD
As shown in Fig- the objective of our DiMeR is reconstruct the 3D mesh geometry from normal maps and derive texture from RGB images. We introduce Geometry Branch in Sec. 3.1 Texture
Branch in Sec. 3.2 and applications in Sec. 3.3
3.1 GEOMETRY BRANCH
As illustrated in Fig. single RGB image admits many equally plausible solutions in the geome- try-texture joint solution space, encouraging the network to learn over-smoothed averages. Normal maps, in contrast, uniquely determined by the underlying surface and faithfully encode fine ge- ometric variation. Guided by Occam's 's razor (Blumer et al., 1987), we therefore feed only normal maps the geometry branch, eliminating appearance-induced ambiguities and simplifying the cor- respondence between input and output. This design establishes a clearer relationship between the network's 's inputs and outputs, ultimately reducing the training complexity. Supervision is likewise restricted to geometry sspecffcc losses, discarding ambiguous RGB rendering terms. We further reg- ularize geometry by rendering the untextured mesh with physically based rendering (PBR) under diverse illuminations and materials, matching the resulting lighting maps to statistical expectations. Finally, we improve the mesh extraction algorithm for greater efficiency and robustness and incor- porate direct 3-D) supervision.
Network Structure. As shown in Fig. 3 the geometry branch our DiMeR model initiates with normal maps A xxx x3 of K randomly selected views, alongside their associated camera embeddings Ç E R 16 We opt for random sampling of input views to improve the model's capa- bility to interpret camera embeddings from arbitrary directions and add slight noise to them, thereby enhancing robustness and reducing dependency on specific input configurations. Furthermore, this also reduces the requirements for the user input, allowing users provide inputs from unfixed view directions. The normal maps A' and their associated camera embeddings Ç are encoded into patch- wise representations Py E xgxxXx using a ViT-based Normal Encoder, where D the number of patches of each view and C' is the dimension of the feature. Similar to the approach taken by LRM (Wei et al. (004)), we utilize a Triplane Decoder to gather information from the Patch Embed- ding P, using several transformer layers (Vaswani et al. 2017) to synthesize triplane (Chan et al.) 2022) features F,, E 3 xW W',,, Subsequently, extract an SDF grid from the triplane fea-

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tures Fy apply the differential isosurface construction algorithm, FlexiCubes (Shen al. 2023 to obtain the vertices and faces for the mesh. Finally, we can rasterize the mesh to get the normal maps, masks, and depth maps for arbitrary views. By providing the environment map and assigning different materials (metallic and roughness) to the mesh, we can render the light map (including specular and diffuse) using PBR, for enhancing the supervision from different lighting conditions, which will be introduced in the following part.
Mesh Extraction Algorithm. Original FlexiCubes requires two MLP networks to output different weights (each edge and vertex in the grid) and the deformation of the grid. However, this incurs ex- cessive computational and GPU memory overhead. Specifically, for a N grid, it needs to compute the deformation of N vertices and the weight of 12 x N edges and 8 x N vertices. Though it is powerful for the tasks of Flexicubes itself, extensive experiments prove that these components con- tribute disproportionately high computational overhead with minimal performance gains. As shown in Tab. S we found that removing these networks from the pre-traine pre-trained model does not adversely affect performance. Therefore, to enable higher efficient training and higher spatial resolutions, prune these components to improve computational efficiency and improve the spatial resolution. Optimization. Given the inherent ambiguity introduced by the RGB texture shown in Fig. we clude RGB loss to enhance training stability. Consequently, we now exclusively employ geometry- related losses to supervise the geometry branch of our model.
In its original implementation. FlexiCubes incorporates three regularization losses to regularize the SDF grid values generated by the network. However, extensive experimentation reveals that this approach yielded low stability (Xu et al. [024a] Ge et al.) 2024). Furthermore, the design does not produce true SDF representations. To address these issues, we employ the eikonal loss (Gropp et al. (020)) to regularize the whole space as the SDF field, specifically by ensuring the norm of the gradient with respect to the coordinates is normalized to I. Nevertheless, computing the derivative for a N grid poses significant challenges in terms of computational and GPU memory costs and potential overfitting at specific grid positions. To mitigate this, we propose randomly sampling po- sitions within the space compute the eikonal expectation loss, effectively reducing computational demands while maintaining the integrity of the regularization, i.e.,
Lesk = Ex (Il x SDF(#) la 1)², E R3 ~ Uniform(-1,1 1),
(I)
where we sample 200K æ in each iteration to calculate the expectation. Moreover, we use the GT SDF value to supervise the SDF value of grid vertices E R x3 in FlexiCubes,
Lagf = [SDD(() (DF)l(b))]l
(2)
To reduce computational overhead, we cache these SDF values for each object in the training set. Drawing inspiration from Photometric Stereo (Woodham) 1980). we introduce the PBR (Kajiyal 1986) losses. The premise is that if the specular and diffuse light maps of 3D mesh under different environmental lighting conditions and various materials can be accurately rendered in PBR, then the geometry of the predicted mesh model can be deemed correct. Therefore, introduce the statistical expectation loss of PBR to supervise the geometry branch,
Lapec EE,m. (Spec(O, e, m) Spec(O,, e, m,,)
+ LPIIP
m, ()) Spec
(3)
Caif = -Ee,.r (ii m, Diff e, em,
+ LPIPS [Diff( e, m, r). Diff(O,,,
(4)
where Ô and O are the predicted and ground truth mesh separately, e, m, r are the randomly sam- pled environment, metallic, and roughness, Spec(-) and Diff(-) are rendering functions of specular and diffuse light map, L.PIP(() is the perception loss (Zhang et al.) 2018). Notably, during the training. we sample different environment, metallic, and roughness to render the light maps for single object.
We also employ the commonly used normal, depth, and mask losses to supervise the geometry branch. Specifically.
Lno = MaT & (1 11- Nar),
(5)
Ldep MGT @ 1D= Darl;
(6)
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Lmask (M-Mar))
where @ denotes element-wise production, MMrr and M are the rendered mask from ground truth mesh model and predicted mesh model, similarly, .V and D represent normal map and depth map. In general, the overall loss function is
gg = Lesk + Ladf + Espec + Laifs + Lnor + Ldep + Lmask.
(8)
3.2 TEXTURE BRANCH
Network Structure. As demonstrated in Fig. the texture branch starts from RGB images I R x3 with the camera embeddings ç. Similar as the geometry branch, we use a ViT-based Image Encoder to get the Patch Embedding Pa Kxxxx and utilize Triplane Decoder to assemble the information from P. get the triplane features Fe E R wW'CC, for the texture field representation (Oechsle et al. 2019). Given the predicted shape from the geometry branch, we rasterize the vertex coordinates into imgge space,
Coordz Rast(u, Camera),
where the pixel value Coordy #x the global coordinate. Next, we query the texture feature FI Rxxxxx x0 on triplane F. for each pixel,
FI Gaople(Coordz, Fc).
(10)
Finally, we decode the color feature predict the image
(11)
Optimization. In this branch, only use RGB loss supervise the network. Specifically,
L1 (Î I (I,
(12)
3.3 APPLICATIONS
Besides the sparse-view reconstruc- tion task, DiMeR is also capable of performing image/text-to-3D tasks. Single-image-to-3D. Given the in- put image, first employ Zero-1-2
Sparse-views-to-D
Normal Pred Pred
DIMeR
3++ Shi et al. 2023a) or Era3D (Li iet al. 2024a) to generate six images from different viewpoints. Specif- ically, the output from zero123++ consists of six views, including the combinations of azimuth and eleva- tion. (30, 20), -10,. (150, 20),
(b) Single-Image magge-to-3 Task
MV Diffusion
Normal Pred
DiMeR
(210,-10). (270, 20). and (330, -10).
Text-to-3D Task
Next, we apply the SoTA normal pre-
diction model Lotus (He et 2024)
"a shiny
Text-to-MV
DIMeR
silver robot
or StableNormal (Ye et al.) 2024) to predict the normal maps for these six views. Since the predicted normal maps are initially in the local camera coordinate system, transform them into the global coor- dinate system using the transformation matrices corresponding the six view directions. Finally, we feed the six transformed normal maps and the RGB images into our DiMeR model to generate the textured mesh.
cat"
Figure 4: Pipelines for sparse-views single-image-, and text-to-3D.
subsequently
Text-fo-3D. This task is approached through two distinct pipelines: (I) The first pipeline involves using text-to-image model generate an RGB image from the input text. Subsequently, we apply the single-image-to-3) pipeline to complete the reconstruction. (II) With the advancement of diffusion models, Kiss3DGen (Lin al. 2025) fine-tunes the SoTA text-to-image generative model, FLUX (BlackForestL.ate 2024), to simultaneously output RGB images along with corresponding normal maps, ensuring high multi-view consistency. Since our DiMeR supports a dynamic number of input views, reconstruction. The generated high-quality models are presented Fig, | and the supplementary materials.
directly feed the four views from Kiss3DGen into DiMeR for 3D model

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Table 1: Quantitative results for reconstruction task. and DiMeR (SN) are the reconstruction results from the normal map predicted by Lotus (He et al., 2024) and StableNormal (Ye et al., 2024) separately. DiMeR (GT) is from the ground truth normal.
CD means Chamfer Distance. DiMeR (Lotus)
value means first-best,
value means second-best, value means third-best.
Dataset
GSO
OmniObject3D
Metric
CD (4)
Fl (1)
PSNR (1) SSIM (?) LPIPS
CD (4)
(1)
PSNR (1)
SSIM (1)
LPIPS ())
InstantMesh
0.045
0.964
18.51
0.846
0.150
0.039
0.983
18.44
0.153
PRM
0.041
0.977
21.68
0.869
0.126
0.034
0.991
21.65
0.865
0.135
DiMeR (GT)
0.028
0.992
23.40
0.883
0.095
0.024
0.996
23.04
0.871
0.112
A
31.7%
+0.015
+1.72
+0.014
24.6%
29.4%
+0.005
+1.39
+0.006
17.0% 4
DiMeR (Lotus)
0.033
0.988
22.57
0.874
0.103
0.034
0.989
21.88
0.866
0.126
DiMeR (SN)
0.032
0.988
22.89
0.875
0.103
0.030
0.993
22.15
0.865
0.121
4
EXPERIMENT
4.1
IMPLEMENTATION DETAILS
We train DiMeR with the filtered Objaverse (Deitke et 2023) total of 98, 526 objects. For test datasets, we choose the widely used GSO (Downs et al., (022) and OmniObject3D (Wu et al., 2023). We use all 1, 029 objects in GSO and randomly select objects for each class in OmniObject3D. More details can be found in appendix.
according to the mesh quality, in
4.2 QUANTITATIVE COMPARISON
Reconstruction Task. As shown in Tab. we compare our DiMeR on the sparse-view reconstruc- tion task using the same 6 randomly sampled input views. Since some sparse-viee -iew reconstruction methods, like CRM (Wang et al. 2025), are limited to only support specific views (six orthogo- nal views), we compare them in single-image-to-31 tasks. Additionally, because MeshFormer (Liu et al. 2024) is not open-source work, we are unable to perform an accurate quantitative comparison. Therefore, we only provide qualitative visual comparisons. For the comparison, we select state-of- the-art (SoTA) methods that are accessible, including InstantMesh (Xu et al.) (2024a) and PRM (Ge) al., 2024). Experiments show that our method can surpass the SoTA methods by a large mar- gin, whatever using GT (31.7% gain) or predicted normal maps (22.0% gain) from StableNormal- Turbo YYe et al. 2024). Notably, when equipped with normal map prediction models, the input to DiMeR remains the same the baselines, relying solely on RGB images. Furthermore, fol- lowing the improvement of normal prediction models, there still room for continued improvement in the performance of DiMeR.
Iine--mmage-o-3D Task. As demonstrated compare our DiMeR with CRM (Wang et al.] (005)) MMesh_RR RRM (Wei al. 2024). InstantMesh (Xu et al.] (2024)), and PRM (Ge et al. 2024), using same sin- gle image input. Our pipeline for this task is shown in Fig. [b), where we use Lotus (He al, 2024) to predict normal maps from the output of zerol 13++ (Shi et al., 2023a). Since inherently
Table 2: Ligle-imagceto-3D task. All the meth- ods use the same single image input. Our DiMeR is equipped with Stable-Zero 13++ (Shi et al., 2023a) and StableNormal Ye et al. 2024).
in Tab. 2
Dataset
GSO
OmniObject3D
Metric
CD (L)
Fl (f)
CD (L)
Fl (f)
CRM
0.144
0.781
0.114
0.854
MeshLRM
0.079
0.933
0.086
0.915
the single-image-to-2 ill-posed, the unseen portions of the data can- not be accurately inferred from a single image select 500 relatively clear data points for meaningful and valuable evaluation. In contrast, the reconstruction models, such DiMeR, PRM, and InstantMesh, have the advantages for the accurate alignment with input image based on the prediction of zero123++.
problem
InstantMesh
0.066
0.950
0.074
0.937
PRM
0.059
0.961
0.064
0.957
DiMeR
0.052
0.981
0.060
0.964
alone. Consequently,
4.3 QUALITATIVE COMPARISON
Sparse-view-to-J1 As demonstrated in Fig. we present a visual qualitative comparison of var- ious methods. A comparison between the rows labeled "Ours" and "Ours (Lotus)" shows similar performance, highlighting that normal prediction models effectively support DiMeR. This suggests that DiMeR, when combined with such models, is capable of surpassing previous methods in real-

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Input
Ours
Ouss(Lotur)
PAM
InstantMesh
Figure 5: The qualitative comparison for sparse view reconstruction.
Table 3: The ablation studies of different input and output formats.
Table 4: The ablation studies of 3D regu-
larization and PBR expectation.
Inpur Output
CD(L)
FI(()
Input Output
CD(L)
FI(f)
RGB + Normal - Geometry Texture
0.043
0.976
w/o 3D Regularization (Eq. 1-2)
0.037
0.975
RGB Normal Geometry
0.041
PBR Expectation (Eq.3-4)
0.973
Only Normal Geometry (Ours)
0.028
0.992
Our Full Design
0.028
0.992
istic applications. Furthermore, DiMeR outperforms previous mesh reconstruction models, such as InstantMesh and PRM, in terms of reconstructing finer details. Single-image-to-B1
As shown in Fig. 6 we compare our method with SoTA methods including Trellis (Xiang et al. (002)) PRM (Ge et al. 2024), MeshFormer (Liu et al. (2024), InstantMesh (Xu jet al., 2024a) and CRM (Wang et al.J 2025). Notably, since the 3D results for MeshFormer are only available from their project page and the corresponding input images are not provided, we are unable to conduct a direct comparison using the same input. In contrast, the other methods use the same input images for comparison. Due to the inherent nature of the generative in diffusion models, Trellis often generates 3D mesh models that exhibit inconsistencies with the input images, although maintains high quality. Specifically, the cup's holes in the second column and the number of pillars in the third column are mismatched. Moreover, the other methods encounter difficulties in generating holes and rings accurately, In summary, DiMeR achieves the best quality.
4.4 ABLATION STUDIES
Input & Output Disentanglement. As shown in Tab, 13 we compare the different compositions of input and output for the geometry branch training. The comparison between the first two columns, "RGB- Normal Geometry Texture" and "RGB Normal - Geometry", proves that the disentanglement for geometry and texture achieves higher reconstruction accuracy for geometry. The comparison between the second and third columns, "RGB + Normal + Geometry" and "Only Normal Geometry". demonstrates the huge gain for discarding the RGB input and supervision. Loss Design. As demonstrated in Tab. we show that Eq. D and Eq. can replace the original loss functions used in FlexiCubes, performing improved performance. With the regularization loss employed FlexiCubes. the training process becomes unstable and struggles to proceed beyond 10,000 iterations, resulting in unsatisfactory network convergence. By introducing the eikonal loss
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Input
Ours
Trellis
PRM
Meshformer*
InstantMesh
CRM
HTT
Figure 6: The qualitative comparison for sasgll-imagetto-- Please note that the results of Mesh- Former obtained from their project page and do not use the same input as other methods.
and incorporating 3D ground truth, we stabilize the training process, achieving significantly better performance. We also validate the effectiveness of the PBR expectation loss (Eq, and Eq. 4. If the lighting map can be accurately computed under varying environmental lighting conditions and across different materials, we can conclude that the predicted mesh aligns well with the ground truth mesh. To achieve this, we assign different materials to the single predicted mesh and place it in various environments. The introduction of PBR losses leads to significant improvements.
Deformation and Weight MLP in FlexiCubes. As shown in Tab. 5 we demonstrate that the improvements gained from the deformation and weight MLP are not worthy enough compared with their computational cost. The experiments are conducted using the official pre- trained weights of InstantMesh, and similar experiments based PRM are provided in the supplementary mate- rial. Upon removing the deformation network and weight network from FlexiCubes, observe minimal impact inference performance, almost no decrease. However, these two networks significantly increase computational workload (about 2.5 x computation overhead) and GPU memory consumption (about 1.5x GPU memory occupancy in training). Consequently, we opt to exclude them from DiMeR in order improve the spatial resolution.
Table 5: The ablation studies of the ef- fectiveness of Deformation and Weight MLP. GPU Mem is training occupancy.
Method
CD
F1
GPU Mem
Infer
0.045
0.964
73GB 48GB
0.5s
w/o
0.045
0,963
0.2s
Grid resolution. As shown in Tab. 6 to validate the improvement, we add comparison experi- ment at 128 grid resolution. The experiment proves the improvement of our network architecture simplification. Furthermore, after reducing the resolution, our accuracy only decreased slightly and still remains higher than previous methods, InstantMesh and PRM.
4.5 MORE DISCUSSIONS.
Reconstruction model vs Generative model. As shown in Tab. 7 and Tab. g we compare trellis in three settings based on its original single-view version and official multi-view version. Even the multi-view version of the Trellis is still not reconstruction model. Trellis is primarily designed as a generative model. Specifically, Trellis's multi-view input strategy involves randomly sampling viewpoints during different denoising steps. Only one randomly selected view is received at each denoising step. This cannot aggregate all views simultancously, which may limit its ability to fully exploit multi-view consistency in reconstruction setting. Moreover, Trellis can't 't generate 3D meshes that perfectly match the input images. These points explain its lower reconstruction accuracy under our evaluation protocol.

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Published as a conference paper at ICLR 2026
Table
Comparison with
Table Comparison with Trellis on single-view.
Table 6:
Experiments
Trellis on miew
about grid resolutions.
Method
CD(J)
Fi(f)
Method
CD(L)
FI(f)
Resolution
CD
Trellis(MV)
0.136
0.835
Trellis original
0.119
0.859
128
0.032
0.987
DiMcR(GT)
0.028
0.992
Trellistzero 23plus
0.155
0.783
192
0,028
0.992
(iMcR(SN)
0.032
0.988
DiMeR
0.052
0,981
Table 9: Quantitative results for reconstruction task with different normal predictors. CD means Chamfer Distance. Error is the angle between predicted normal vectors and gt normal vectors.
Dataset
GSO
OmniOhject3D
Metrie
CD (4)
F1 (t)
Error (4)
CD (1)
F1 (t)
Error (1)
Geoaizad+DDMMeR
0.039
0.982
17.673
0.037
0.987
23.129
MariGold+DiMeR
0.037
0,986
17.400
0.038
0,984
22.934
DSINE+DiMeR
0.040
0.978
17.953
0,046
0.974
23.010
Lotus-G+DiMeR
0.033
0.988
17.151
0.036
0,988
21.523
Lotus-D+DiMeR
0.035
0.987
16.606
0.034
0.989
21.065
StableNormal-DiMMRR
0.032
0.988
16.818
0.030
0.993
21.205
Table 11: Robustness to Gaussian noise in nor- mal maps.
Table 10: Different normal sources for single- image-to-3D task with Zero
Noise std
CD(J)
FI(1)
Method
CD())
FI(t)
0.0
0.028
0.992
0.05
0.031
0,991
DiMeR Zero 23plus joint Normal
0.061
0.960
0.032
0.991
DiMeR + Zero123plus SN Normal
0.052
0.981
0.3
0.036
0.987
0.5
0.042
0.982
Robustness different normal prediction models. As shown in Tab. 9 we conduct the experi- ments based on different normal foundation models as DiMeR's input. DiMeR empirically shows strong robustness across different normal prediction models. Furthermore, CD value exhibits a decreasing trend as the normal prediction error decreases.
observe that the
Besides different normal prediction models, also conduct experiments the joint normal maps output from zero 123plus Shi et al. (2023a). As shown in Tab I0 these results indicate the robustness of our DiMeR can adapt to any normal source.
Finally, as shown in Fig. I we conduct the experiments on giving some noise to the objects' normal maps.
CONCLUSION
In this paper, we propose DiMeR, a disentangled dual-stream framework with 3D supervision for feed-forward sparse-view mesh reconstruction. By driving the geometry branch exclusively with normal maps and leaving RGB information to separate texture branch, DiMeR clearly separates conflicting objectives and grounds training unambiguous supervision signals. To enhance the training effectiveness and spatial resolution, DiMeR improves the mesh extraction algorithm by redesigning the regularization losses, introducing 3D ground-truth supervision, and removing dundant modules. Extensive experiments confirm that DiMeR surpasses state-of-the-al baselines across multiple tasks, such searse-viewwto-311 image-to-3D, and text-to-3D, highlighting both its effectiveness and robustness. As normal-prediction models continue to improve, DiMeR's 's perfor- mance is likely to advance further.
Limitations. 1) Since we train DiMeR on the object-wise normal maps with white background, we can not accept scene-level sparse views as input. 2) On the single-imagett-33D task, enhancement of the 2.5D diffusion model, including resolution and accuracy, would help DiMeR achieve better quality.
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ACKNOWLEDGMENTS
This project is partially supported by National Key R&D No.2023YFFO725001.
Program of China (Grant
REPRODUCIBILITY STATEMENT
We have uploaded the complete project code in the supplementary materials ensure the repro- ducibility of our work. Additionally, the results presented for all other methods were obtained directly from their official repositories, ensuring that they are accurately reflected as per their pub- lished implementations.
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A IMPLEMENTATION DETAILS
Evaluation Protocol. For 3D metrics, we sample 32, 000 points on the surface to compute com- monly used Chamfer Distance (CD) and F1-Score@0.1 to evaluate the quality of geometry. For 2D metries, we compute the PSNR, SSIM, and LPIPS to evaluate the rendering quality over rendered views. We rescale and align the generated meshes and ground truth meshes for fair comparison. Training. We set the total batch size to 64, with learning rate X 10 6 for geometry branch and 4 x 10 for texture branch. The resolution of the triplane is 3 x 64 x 64, and the SDF grid is 1923, which is higher than baselines benefiting from our enhancement of mesh extraction methods. The resolutions of input and supervision are 512 512. For PBR statistical expectation loss, place the predicted meshes in 10 different lighting environments and apply 10 different materials, rendering from different 10 views during training. We train the geometry branch for two days and the texture branch for one day on 8 H100 GPUs.
B BENCHMARK FOR NORMAL PREDICTION MODELS
To determine whether recent normal-predictio foundation models meet the quality requirements of pipeline, we evaluate Lotus (He et al. 2024), StableNormal (Ye et al., 2024). DSINE (Bae &
Davison 2024), Marigold (Ke et al. 2024). and GeoWizard (Fu et al., 2024) on the GSO (Downs al. (022) and OmniObjeet3D (Wu et al. 2023) benchmarks. For each object, six randomly sam- pled views are rendered, producing paired RGB images, masks, and ground-truth normal maps. Using the RGB inputs, measure
and median angular error, the proportion of pixels with error below 11.25%, 22.5", and 30,, and inference time. When computing these metrics, we only use the foreground pixels. As summarised in Tab. 12 StableNormal and Lotus offer the best balance of accuracy and speed, adding only negligible latency. Correspondinglyy as demonstrated in Tab. even with errors, our DiMeR still outperforms previous methods by a large margin, accepting the same RGB input. Among the reported metrics, the mean error and the fraction of pixels with error below the threshold 30 are most indicative of prediction stability. Large errors markedly im- pact reconstruction quality. Ongoing advances in normal nomaal-prediction models are therefore expected to further improve DiMeR's performance. We also provide the qualitative comparison in Fig. 7
Input
GeoWizard
Marigoi
DSINE
Latuo-G
Lotus-O
Figure 7: The qualitative comparison for normal prediction foundation models.
Table 12: Benchmark for normal map prediction of foundation models on object scenario. The latency evaluated on a single A800 GPU.
Datasct
GSO
OmniObject3D
Metric
mean (4) median ({)
11.25* (t) 22.5" (1) 30 (1) mcan (L)
median (L) 11.25 (1) 22.5" (1) 30" (t)
Latency (L)
GeoWizard
17.673
14.307
48.309
74.908
83.097
23.129
20.156
28.272
61.215
74.122
2102 ms
Marigold
17,400
14.305
47.303
76.058
89.
22.934
20.243
28.867
61.201
73.824
260 ms
DSINE
17.953
14.857
45.543
72.951
82.535
23.010
20.116
29.182
62.140
75.082
ms
Lotus-G
17.151
13,920
45.343
76.831
85.277
21.523
19.048
30,836
64.828
77.359
130
SN V1.8.1
16.818
14.743
39.860
74.524
86.424
21.205
19.468
25.677
61.917
77.916
236
Lotus-D
16.606
13.377
47.218
78.076
86. 166
21.065
18.622
32.118
66.216
77.968
130 ms
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Published as a conference paper at ICLR 2026
C DISCUSSION ABOUT THE IMPORTANCE OF NORMAL-ONLY
normal map is simply an image whose three channels no longer describe colour but the z.y and components of the unit normal vector every pixel. In practice each component, which lies in [-1, 1], is linearly re-encoded into the 0-255 range and written into the R-GG and B-channels. When this map is projected back onto the mesh under the same camera pose, every RGB triplet can be decoded to recover the outward-facing surface normal at that point. Because it is a dense field of orientations, the map tells "how the surface tilts here" rather than "what colour is here". In other words, it provides first-derivative information about shape-much like having a slope-at-every description of a landscape.
Specifically, training RGB images introduces substantial ambiguity. The network must infer whether changes in colour are due to actual shape (c.g., surface relief) or merely superficial appear- (e.g.. paint lighting). As illustrated in Figure of the paper, visually identical features such as black dots on a die-may mmy stem from entirely different causes: painted texture or recessed geom- etry. This ambiguity forces the network to learn additional heuristics, increasing inductive bias and complicating generalisation. Normal maps eliminate this burden. Because they encode only surface orientation, no appearance disentanglement needed. The network can instead learn a more direct and well-conditioned mapping from normals to 3D shape. Empirically, this reduced-biag setting yields clear benefits: substituting RGB supervision with normal maps improves Chamfer distance from 0.041 to 0.028 and raises the Fl score from 0.971 to 0.992. These results confirm that normal- based learning is not only simpler, but also more effective.
Table 13: The ablation studies of different input formats.
Input Output
CD(J)
F1(1)
RGB RGB Normal
0,041
0.971
0.041
0.981
Normal
0,028
0.992
D ABLATION STUDIES FOR DEFORMATION AND WEIGHT NETOWRKS
To validate the inefficiency of deformation and weight networks in FlexiCubes, we additionally conduct the experiments based the official pre-trained weights of PRM. As shown in Tab. 14 removing these two networks doesn't damage the performance. Therefore, the experiments of Tab. and Tab. 14 can prove our conclusion, the deformation network and weight network are redundant for LRR series models. Cutting the cost of these two modules, we can improve the spatial resolution from 1283 to 1923. Moreover, the latency of each iteration in training is also reduced.
Table 14: The ablation studies of the effectiveness of Deformation and Weight MLP based on PRM. GPU Mem is training occupancy.
Method
CD
Fl
w/
0.041
0.977
w/o
0.041
0.977
E DISCUSSION ABOUT THE INFLUENCE OF NORMAL PREDICTION ACCURACY
In DiMeR's 's training, we take the unstable prediction from normal prediction models into consider- ation. Specifically, we add noise to the GT normal input to improve robustness in practical utiliza- tion. Though the slight accuracy drop inevitable, the overall accuracy still much higher than the baseline's. s. As shown in Tab. [ when replacing the GT normal map from the output of Lotus or StableNormal, the geometry accuracy only reduces by 0.004 (from 0.028 to 0.032), which gains 22.0% compared with the previous SoTA model, PRM.
F DISCUSSION ABOUT LOSS DESIGN
All of Eq.3-7 are texture-irrelevntt. Normal, Depth, Masks, and PBR light map losses are 2.5D, which can provide a higher spatial resolution supervision. Since the unaffordable computational burden when increasing 3D spatial resolution, the 3D SDF loss mainly fuction regularization of
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Published as a conference paper at ICLR 2026
the space (1929), such the object's internal. Therefore, the combination of high-resolution 2.5D supervision with relatively low-resolution 3D regularization is more reasonable.
G MORE VISUAL RESULTS FOR SPARSE VIEW TO 3D
Figure 8: More Visual Results for Sparse View to 3D. The left and right are input and output seper- ately.
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H MORE VISUAL RESULTS FOR TEXT TO 3D
"olaf the Snowway. three stacked spheres, proportionally larger base sahere, medium mid-sestion sphere, smaller head sphere, protrudlng conical nose, two dob-likes eyes, three buttons aligued vertically, twig-llke hair feature, two stéole-like arms, survilinear shapes"
"a redl apple, with green. Leaves"
"A vermaid with blue dress and æci fish tail sitting stove"
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Published as a conference paper at ICLR 2026
"A yellow owt and cat sat-shaped tay"
"a blook white cartoow okaraater with woouth and nose"
"9. stowe nannument top of pedestaL"
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Published as a conference paper at ICLR 2026
"a yellow plastic ohair weth ourved baokerest"
"z green octopus with tentacles and a Othulhu head""
"a hum.nnild Mexipaw cRotus wearing green and purple hat"
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Published as a conference paper at ICLR 2026
"A head wearing keimet googles"
"a white teddy bear"
"a blue and white octopus-like -lles oreature"
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Published as a conference paper at ICLR 2026
"a rabbét on a ball"
"A bull's head with horws"
88
"a girl with long white hatr, wearing a white dress and headshooee"
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Published as a conference paper at ICLR 2026
"A small white stone soulpture of monkey, seated posture, delineated mane"
"White shower head"
"A sArtoon Mickey Mouse"
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Published as a conference paper at ICLR 2026
Lotus
DiMeR
Figure 9: A failure case.
I FAILURE CASE
Since train DiMeR the object-level normal maps with white background, accept the image with a scene background as input. As shown in Fig. 9 DiMeR fail to synthesis the mesh model for scene-level input.
PBR SETUP
We use a microfacet BRDF with a Lambertian diffuse term and a Cook-Torrance specular term, with a Trowbridge-Ren -eitzg GGX normal distribution, Schlick Fresnel, and GGX-based geometry term. Materials are parameterized the usual metalness- roughness form: per-pixel albedo, metallic, and roughness. For lighting, we rely on image-based lighting with HDR environment maps. At training time, each rendered view uses a randomly sampled environment map from a large collection 679 indoor and outdoor HDRIs. We the standard split-sum approximation: the diffuse term is com- puted from low-frequene irradiance map, while the specular term obtained from prefiltered mipmapped environment map. The range of roughness and metallic parameters is [0,1].
K THE USE LARGE LANGUAGE MODELS (LLM)
We used OpenAl's GPT-5 to assist with the refinement and proofreading of certain sentences in this paper. The LLM was used exclusively to enhance the clarity and coherence of our writing. All content contributions are made by the authors.
25


Reviews:
[{"review_id": "YIYxKD64Tg", "reviewer": "Reviewer_68mg", "summary": "The paper presents a disentangled dual-branch model for 3D mesh reconstruction that separately learns geometry and texture. The geometry branch predicts shape using only normal maps, while the texture branch learns appearance from RGB images. This separation reduces ambiguity in cases where textures might hide geometric errors. This paper also improves mesh extraction with a simplified version of FlexiCubes, combined with 3D supervision and PBR-based regularization. The method achieves better results over existing approaches such as PRM and InstantMesh on both quantitative and qualitative metrics across sparse-view, single-image, and text-to-3D reconstruction tasks.", "questions": "Please follow the weakness section. \n\n* Why is Trellis omitted from the comparison in the sparse-view reconstruction task in Table 1? Since Trellis is capable of predicting meshes from sparse-view images, it seems reasonable to include it for a fair comparison.\n\n* In Table 2, why is only a single image provided as input to methods like Trellis, while DiMeR receives sparse-view inputs generated from Zero-123? Wouldn’t it be more consistent and fair to use the same input setup for all methods?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* The motivation of the paper is clear. The paper is well-written and the proposed method is easy to follow and the overall presentation is clear.\n \n* The paper is solving a relevant problem. Geometry ambiguity introduced due to texture is a common problem in optimization based 3D reconstruction pipelines.\n\n* Comprehensive evaluation is performed over several tasks.", "weaknesses": "* Novelty: While the idea of learning disentangled representations for geometry and texture using different input modalities (normals for geometry and RGB images for texture) is sound, the overall approach of using normal supervision to regularize reconstruction is very standard with already known components.\n\n* Experimental Results: Overall, the results presented in both the main paper and the supplementary material appear relatively simple compared to the quality achieved by recent state-of-the-art mesh reconstruction methods.\n\n* No analysis and discussions on failure cases and limitations has been done in the paper.", "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": "mlAAWNrrFF", "reviewer": "Reviewer_QJa6", "summary": "The paper proposes DiMeR, a feed-forward mesh reconstruction framework that explicitly disentangles geometry and texture: the geometry branch takes only surface normal maps; the texture branch takes RGB and is supervised by appearance losses. The geometry branch still accepts raw RGB at inference by first predicting normals with recent \"foundation\" normal estimators (e.g., Lotus, StableNormal). The authors also simplify FlexiCubes by removing deformation/weight MLPs and swap its regularizers for eikonal + GT-SDF supervision, allowing a higher resolution SDF grid at similar cost. Experiments on GSO and OmniObject3D report better geometry quality versus InstantMesh and PRM for sparse-view, single-image, and text-to-3D pipelines.", "questions": "1. Please add discussion on what DiMeR does that PRM cannot, and how your normal-only geometry branch differs from the recent \"normal bridging\" idea in Hi3DGen and mesh-LRM variants—ideally with side-by-side ablations where applicable.\n2. Add discussions about resolution-control and sensitivity to normal FM outputs as mentioned in the weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper effectively illustrates the texture-hides-geometry problem in Figure 2, motivates normal-only geometry prediction to avoid multi-solution conflicts; the design is well illustrated and methodically grounded.\n2. Clear technical contributions e.g. replacing FlexiCubes regularizers with eikonal + GT-SDF supervision and adding PBR expectation losses. These modifications are technically sensible and addresses known stability issues.\n3. Empirical gains on GSO / OmniObject3D. DiMeR shows good amount of CD improvements. Single-image comparisons also improve over InstantMesh / PRM. Results remain strong even with predicted normals.\n4. Comprehensive evaluation. Tests across multiple tasks (sparse-view, single-image, text-to-3D). Ablations isolate the benefit of normal-only input and 3D regularization + PBR losses; FlexiCubes pruning demonstrates little accuracy loss but notable memory/latency savings. These provide valuable insights.", "weaknesses": "1. Novelty relative to contemporaries is moderate. Prior work has: 1) disentangled geometry/texture for 3D generation (e.g., Fantasia3D/CLAY), 2) used normals as strong geometry cues (PRM, photometric-stereo based LRM), and 3) pursued feed-forward reconstruction with triplanes/meshes (LRM family, InstantMesh, MeshLRM, MeshFormer). The paper should delineate what is fundamentally new beyond combining these threads, clearer positioning vs MeshLRM/MeshFormer would help.\n2. Fairness/controls in single-image pipeline. For single-image-to-3D, the pipeline uses Zero123++ -> Lotus normals -> DiMeR and claims all reconstruction baselines use the same Zero123++ views; however, Trellis is generative and not strictly comparable and meaningful here. Other relevant pipelines such as MeshLRM is not mentioned or included for comparison.\n3. Grid Resolution. DiMeR trains with 192^3 SDF grids \"benefiting from our enhancement\"; prior LRM-style baselines often default to 128^3 to the best of my knowledge. Please clarify whether your reported gains persist at equal grid resolution and whether CD improvements correlate with resolution. Ideally, a lower resolution variant should be included for completeness and would give a better understanding of the performance gain.\n4. Normal FM dependency and failure modes: more qualitative failure cases and error-to-CD curves would provide more insights and help understand about the robustness of the model.", "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": "B2yds3JwXg", "reviewer": "Reviewer_jDrM", "summary": "The paper introduces a disentangled dual-branch framework (DiMeR) for 3D mesh reconstruction. Unlike previous approaches that jointly train the texture and geometry space, DiMeR separates the geometry and texture learning using only normal maps and RGB inputs. It proposes geometry-specific losses and overall achieves large performance gains on two benchmarks for sparse-view, single-view and text-to-3D reconstruction.", "questions": "1. Is there any approach to measure how sensitive DiMeR's geometry performance is to errors in normal prediction?\n2. Since the geometry and texture branches are disentangled, how is consistency ensured during joint inference? Is it totally influenced by the normal map consistency with the RGB input, or any corner cases when the predictions of these two branches are not aligned well?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The motivation is quite clear and interestingly stated in Figure 2. The disentanglement is clean and effective. The performance improvement in Table 3 demonstrates the effectiveness of the proposed approach.\n2. The pruning of heavyweight FlexiCubes components achieves faster training and fewer overhead on GPU memory (in Table 5), which allows higher resolution without accuracy loss\n3. The evaluation is conducted comprehensively, including different modules in the ablations and comparison with previous baselines on 3 applications over two benchmarks.", "weaknesses": "The architecture is a bit like the combination of LRM with FlexiCubes. The backbone largely follows existing LRM-style architecture. Most novelty lies in the training strategy and disentanglement of texture and geometry. But overall, the adaptation with obvious improvement, especially on geometry, is promising.", "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": "fMFvV4V3wl", "reviewer": "Reviewer_SAKn", "summary": "This paper proposes DiMeR, a 3D mesh reconstruction framework that disentangles geometry and texture to reduce training ambiguity. It predicts geometry solely from normal maps while learning texture from RGB images in a separate branch. The authors further improve the pipeline with techniques like Eikonal and PBR expectation losses, and demonstrate significant performance gains across sparse-view, single-image, and text-to-3D tasks. The overall design is clean, stable, and achieves clear improvements over prior methods.", "questions": "- The specular rendering loss in the proposed PBR expectation term may suffer from gradient instability when the roughness parameter is small, as specular reflections can become highly discontinuous. Could the authors clarify how they stabilize this loss or whether any regularization is applied?\n- It would also be helpful to provide more details about the PBR setup — specifically, the BRDF model used, the range of roughness and metallic parameters, and the nature of the lighting conditions (e.g., sharp high-frequency environment maps vs. smooth diffuse ones).\n- Additionally, from the released code, it seems that only a single environment map is used for rendering; could the authors confirm whether multiple environments were actually sampled during training, as stated in the paper?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper introduces a novel framework that reconstructs geometry purely from normal maps, effectively disentangling geometry and texture spaces, (partially) resolving the ambiguity between shape and appearance in RGB-supervised 3D reconstruction.\n- The method is well-engineered, integrating improvements like Eikonal regularization and a PBR expectation loss. The latter is especially novel as it introduces varying lighting and material as a way of disentangling geometry and appearance.\n- The authors conducted extensive experiments on GSO and OmniObject3D, showing substantial improvements over prior methods. Comprehensive ablation studies confirm the effectiveness of each design choice, demonstrating strong technical rigor.", "weaknesses": "- In the Single-Image-to-3D and Text-to-3D experiments, the authors only use StableNormal and Lotus to generate normals, while models like Zero123++ can already output RGB and normal maps jointly. To validate robustness, the paper should test multiple normal sources (e.g., Zero123++, Era3D, Kiss3DGen) and report Chamfer/F1 variations under different normal qualities.\n- The model is trained with ground-truth normals but tested with predicted normals, which may lead to a training-testing gap. The authors are encouraged to train with noisy or predicted normals to better reflect real-world robustness.\n- Missing noise sensitivity analysis. The paper should include an additional experiment that perturbs GT normals with controlled noise levels and compares quantitative and qualitative results, to show how geometry accuracy degrades under realistic prediction errors.", "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)