ReconViaGen: Towards Accurate Multi-view 3D Object Reconstruction via Generation
Authors: Jiahao Chang
Chongjie Ye
Yushuang Wu
Yuantao Chen
Yidan Zhang
Zhongjin Luo
Chenghong Li
Yihao Zhi
Xiaoguang Han
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Generation
3D Reconstruction
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: ReconViaGen injects reconstruction priors into diffusion-based 3D generation to reliably complete and align 3D reconstructions with sparse multi-view inputs.

Abstract: Existing multi-view 3D object reconstruction methods heavily rely on sufficient overlap between input views, where occlusions and sparse coverage in practice frequently yield severe reconstruction incompleteness. Recent advancements in diffusion-based 3D generative techniques offer the potential to address these limitations by leveraging learned generative priors to ‘‘hallucinate" invisible parts of objects, thereby generating plausible 3D structures. However, the stochastic nature of the inference process limits the accuracy and reliability of generation results, preventing existing reconstruction frameworks from integrating such 3D generative priors. In this work, we comprehensively analyze the reasons why diffusion-based 3D generative methods fail to achieve high consistency, including (a) the insufficiency in constructing and leveraging cross-view connections when extracting multi-view image features as conditions, 
and (b) the poor controllability of iterative denoising during local detail generation, which easily leads to plausible but inconsistent fine geometric and texture details with inputs. Accordingly, we propose ReconViaGen to innovatively integrate reconstruction priors into the generative framework and devise several strategies that effectively address these issues. Extensive experiments demonstrate that our ReconViaGen can reconstruct complete and accurate 3D models consistent with input views in both global structure and local details.

RECON VIAGEN: TOWARDS ACCURATE MULTI-VIEW 3D OBJECT RECONSTRUCTION VIA GENERATION
Jiahao Chang 1.2 Chongjie Y Yushuang Wu?
Yuantao Chen'
Yidan Zhang!
Zhongjin Luo² Chenghong Li
Yihao Zhi
Xiaoguang Han 1,2.3 H,n
1 School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen
Shenzhen Future Network of Intelligence Institute
GGanggoog Provincial Key Laboratory of Future Networks of Intelligence
Multi-view Input
Generation (TRELLIS)
Completonesa
Consistency with Input views
ReconVisGen (Ours)
Complelanese
Consistancy
Reconstruction (VGGT)
Completeness
Consistency
Imput
clouts
Figure 1: the task of 3D object reconstruction from multi-view images, existing pure reconstruc- tion methods can only produce incomplete results, while generation- methods can get plausible complete results but with strong inconsistency with input images. Our Recon ViaGen integrates 3D reconstruction and diffusion-based generation priors into one framework that leads to accurate re- constructions.
ABSTRACT
Existing multi-view 3D object reconstruction methods heavily rely on sufficient overlap between input views, where occlusions and sparse coverage in practice frequently yield severe reconstruction incompleteness. Recent advancements diffusion-based 3D generative techniques offer the potential to address these lim- itations by leveraging learned generative priors to "hallucinate" invisible parts of objects, thereby generating plausible 3D structures. However, the stochas- tic nature of the inference process limits the accuracy and reliability of gen- eration results, preventing existing reconstruction frameworks from integrating such 3D generative priors. In this work, comprehensively analyze the rea- sons why diffusion- based 3D generative methods fail achieve high consistency, including (a) the insufficiency in constructing and leveraging cross-view con- nections when extracting multi-view image features as conditions, and (b) the poor controllability of iterative denoising during local detail generation, which easily leads to plausible but inconsistent fine geometric and texture details with inputs. Accordingly, struction priors into the generative framework and devise several strategies that effectively address these issues. Extensive experiments demonstrate that our Re- con ViaGen can reconstruct complete and accurate 3D models consistent with input views in both global structure and local details. Project page: httpp: //jiahao620. hhub. 1o/ how..ooocconniiaeenn rewooviicgignnn
propose ViaGen to innovatively integrate
"Equal contribution. "orresponning Author.

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INTRODUCTION
In the field of 3D computer vision, multiview 3D object reconstruction has long been a fundamen- tal yet challenging task, with numerous applications in areas such VR, AR, and 3D modeling. Existing multiview reconstruction methods typically rely on enough visual cues and learned cor- respondences between views to estimate the 3D structure and appearance of the object [Mildenhal] iet al. (2020); Kerbl et al. (2023); Leroy et al.] (2024); WWang et al. (2024a) 2025a). However, these methods often face significant limitations when dealing with weak-texture objects incomplete image captures due to occlusions or the presence of support surfaces. As a result, reconstructed 3D models tend have holes, artifacts, and missing/blurred geometric details, which severely restrict the reconstruction completeness He et al. (222)) :: Xu et al. (2024c).
Recent advances in diffusion- based 3D generative techniques have shown great promise in address- ing these limitations. These techniques leverage 3D generative priors learned from large-scale 3D data to generate complete 3D outputs from sparse- or even single-view images Li et al. (2024b); Zhao et al. (2025;; ; Li et al. (2025a): Zhang et al. (2024b); Ye et al. (2025). Such strong genera- tive priors can effectively "hallucinate" the invisible portions of objects with plausible high-quality geometry and appearance, thereby showing great potential in 3D reconstruction by filling in the missing details and improving the completeness. However, the stochastic nature the diffusion- based inference process introduces significant uncertainty and variability in the generated results, making it challenging to achieve high accuracy and reliability, especially the pixel-level alignment required in accurate reconstruction. This stochasticity has largely hindered the effective integration of diffusion-based 3D generative priors into existing multi-view reconstruction frameworks.
Pioneering explorations have been made in the field of 3D diffusion-based generation from multi- view images Xiang et al. (2024); Zhao et al. (2025). However, their predictions still suffer from inaccurate global structures and inconsistent local details. The inherent key reasons of the failure include (i) the insufficiency in constructing cross-view correlations when extracting multi-view im- age features as conditions, resulting in inaccurate estimation in both object geometry and texture, at the global and local level, (ii) the poor controllability and stability of the denoising process during inference, which easily results in inconsistency with input views especially in detailed geometry and texture estimation. To address these issues, we present Recon ViaGen that innovatively integrates multi-view stereo priors into the diffusion beafud generative framework for object reconstruction. Our solution includes three stages: (i) pre-trained strong reconstructor Wang et al. (2025a) is de- veloped to build a multi-view stereo understanding of the object geometry and texture, aggregated into a single global token list and set of local token lists, for representing the global geometry and the detailed per-view appearance, respectively; (ii) a coarse-to-fine 3D generator Xiang et al. (2024) first estimates the coarse structure and then produces the fine textured mesh, under the conditioning of global and local tokens from the first stage, respectively; (iii) refining the estimated poses from the reconstructor using the generation from the second stage, and encouraging the pixel-wise alignment with input views using a novel rendering-awee aware velocity compensation mechanism, where input im- ages coupled with estimated camera poses are used to explicitly guide the denoising trajectory of local latent representations.
Extensive experiments on the Dora-bench Chen et al. (2024) and OmniOhject3D Wu et al. (2023b) datasets validate that our ReconViaGen can achieve state-of-the-art (SOTA) reconstruction perfor- mance in both global shape accuracy and completeness and local details in geometry and textures. Our contributions are summarized as follows:
We propose a novel framework called Recon ViaGen, which is the first to integrate strong recon- struction priors into diffusion-based 3D generator for accurate and complete multi-view object reconstruction. A key design is to aggregate image features rich in reconstruction priors as multi- view-aware diffusion conditions.
The generation adopts a coarse-to-fine paradigm, which leverages global and local reconstruction- based conditions to generate accurate coarse and then fine results in both geometry and texture. Additionally, a novel rendering- strains the denoising trajectory of local latent representations for detailed pixel-level alignment.
velocity compensation mechanism is proposed that con-
Extensive experiments on the Dora-bench and OminiObject3D datasets conducted that vali- date the effectiveness and superiority of the proposed Recon ViaGen, which achieves SOTA per- formance.
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MV Input
Reconstruction based Conditioning
Coarse to fine Generation
4.0 Mhight
Output
FRC
MOQUHOC
Condioon
Complete Geametry
Conditin MEN
Condition
Festured Neath
Tarsber
Gross sttetion Maci
SUITIKA
VSGT
Figure 2: An overview illustration of the proposed Recon ViaGen framework, which integrates strong reconstruction priors with 3D diffusion-based generation priors for accurate reconstruction at both the global and local level.
RELATED WORK
Single-view 3D Generation. Great developments have been made in single-view 3D Object Gen- eration. Recent methods can be divided into two groups: 2D prior-based and 3D native generative methods. DreamFusion Poole et al. (0022) and its following successors Tang et al. (2024): |Quu al.) 2024):[Wang et al. ( Lin et al. (2023); Tang et al. (223a)) distill the 3D knowledge from pre- trained 2D models. Another line work develops multi- diffusion based on pre-trained image
or video generators and conducts view fusion for 3D outputs Li al. (2023)) Xu et al. (2024b) Tang ict al. (2025); Xu et al. (2024d); Wang et al. (2025b); Wei et al. (2024): y Liu et al. (2023al (202)); Xu
cet al. (2024c) ; Li al. (2024a): Zuo et al. (2024); Wu et al. (2024a). Differently, 3D native gener-
ative methods employ diffusion based on different 3D representations like point clouds Luo & Hu
(2021: Zhou et al. (2211) Nichol et al.
(2022). voxel grids Hui et al. (2022): Tang al. (2023));
Müller et al. (2023) Triplanes Chen et al. (2023); ; Wang et al. (2023b); Shue et al. (2023). 3D Gaus-
CraplanesCCheneeee sians Zhang al. (0044)). More recently, 3D latent diffusion has been explored to directly learn
the mapping between the image and 3D geometry Zhang et al. (2023); Zhao et al. (2024); Li et al.
(002b): Zhang et al.] (2024b): Wu et al. (2024b): Li al. (2025a); Zhao et al. (2025); Ye et al. 2025). which greatly improves the generation quality. However, mulu-vieww 3D generation is still exder-explored, suffering from high variations in generation, easy inconsistency with input images, or strong reliance on input viewpoints Xiang et al. (2024); Zhao et al. (2025), which hinders their direct application in accurate 3D object reconstruction.
Multi-view 3D Reconstruction. Traditional methods conduct multi-view stereo (MVS) to recon-
struct the visible surface of objects by triangulating correspondences across multiple calibrated im-
ages Furukawa et al. (2015); Galliani et al. (2013); (ccönbeerger et al. (2016) Xu & Tao 2019))
Learning-baser MVS methods Yao al. [2018 2019); Chen et al. (2019); Cheng al. 2220);;
Gu et al. (2020); Yang et al.] (2020); Wang et al. (021a) employ deep neural networks to enhance
both reconstruction quality and computational efficiency. Scene-specific NeRF methods Lin et al. [
Wang et al. (2021b) adopt bundle adjustment from conventional SIM pipelines to jointly op- timize camera parameters along with radiance field from dense views. Recently, DUS Wang al. (2024a) and its follow-up Sorks|Smart
Smart et al. (2024); [eroy et al. (2024); Wang et al. (2025a) together estimate point clouds and camera poses from paired or more views, which releases the reliance on camera parameters, but suffers from incomplete reconstruction results caused by the point cloud representation. Focusing on object reconstructions, large reconstruction models Hong et al. (2023) explored to produce complete reconstructions via regressing more compact structured 3D representation (e.g. 3D Gaussians Kerbl et al. (2023) and Triplane) from multi-view inputs Wei et al. (2024); Xu et al. (2024b); Tang et al. (2025) Xu al. (2024d), but requiring view inputs from cer- tain camera poses. Follow-up up methods further support pose-free reconstructions Wu et al. (2023a); Wang et al. (2023a); Jiang al. (2023); He et al. (2024), while they tend to predict smooth and blurred details, especially in invisible regions. Differently, our method introduces diffusion- 3D generation priors to advance pose-free object reconstruction in fidelity and completeness.
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Generative Priors in 3D Object Reconstruction. Generative priors are introduced into 3D construction frameworks to assist in predicting plausible geometries or textures in invisible portions of objects. Existing methods mainly introduce two kinds of priors: (i) diffusion- bbaeed 2D genera- tive prior and (ii) regression-based 3D generative prior. The former is often used single-view 31) reconstruction by generating plausible multi-view images first and conducting reconstruction Yang iet al.] (2024): Li et al. (2023) 2024a): Xu et al.
(2024b); Tang et al. (2025); Xu et al. (2024d); )
Wang et al. (2025b); Wei al. (2024) Liu et al. (2023a 2024): Wu al. (2024a). For pose-free Liu et al. (2023b) predic- tions within an optimization pipeline align poses and generate novel views for reconstruction. However, the inconsistency between views still limits the performance of this pipeline. Regression- based 3D generative priors are introduced to regress unified compact 3D representation, avoiding this issue, for example 3D neural volume volumeJiang et al. (2023). Triplane Hong et al. (2023); Wei et al.
sparse-view reconstruction, iFusion Wu et al. (2023a) leverages Zerol Liu
(2024; Wang et al. (2023a), and 3D Gaussians He et al. (2024): Xu et al. (2024c): Smart et al.
2024 Diffusion- based generative priors prove superior to regressive ones generating detailed
results in both geometry and texture Li et al. b: Zhao et al. (002)) | Zhang et al. (2024b): Ye et al. (2025). One2345++ and its follow-up work Liu et al. (2024); Xu et al. (2024a) develop 3D volume diffusion conditioned by multi-view inputs. However, 3D volume suffer from poor com- pactness, so trade-off between the diffusion learning difficulty and representation capability limits their performance. Differently, our method builds upon strong diffusion based 3D generative pri- ors Xiang al., (2024), with powerful reconstruction priors Wang et al. (2025a) constraining the denoising process for accurate 3D outputs of high-fidelij, details.
METHODOLOGY
3.1 PRELIMINARY
Given a set of N uncalibrated multi-view images of object I= {I,) the task of pose-free multi-view reconstruction aims to obtain the complete 3D object O. Our framework leverages two kinds of strong priors achieve complete and accurate reconstruction results: the reconstruction prior from VGGT Wang et allaa11220)) (2025a) and the generation prior from TRELLIS Xiang et al. (2024). In this section, we first introduce these two priors as preliminaries.
Reconstruction prior of VGGT VGGT Wang et al.] (2025a) achieves SOTA results in pose- free multi-view 3D reconstruction, providing powerful reconstruction prior. It adopts a feed- forward transformer architecture designed for efficient and unified 3D scene reconstruction from single/multiple images. Multi-view images I are first fed into a DINO-based VIT Oquab et al. (204) simultaneously for tokenization and feature extraction into Qdin- Then, 24 self-attention layers further address ciaino into 3D-aware features 124 with an alternating attention strategy, switching between frame-wise and global self-attention to balance local and global information and enhance multi-view consistency. Finally, four prediction heads decode the output of 4 layers (4-th, 11-th, 17-th, and 23-rd), i.e., (Z) = {ő1. Φ11, 117, )21). into camera parameters, depth map, point map. and tracking feature predictions. To adapt object reconstruction, we fine-tune VGGT on an object-reconstruction dataset (see Sec. 4.1 for details). A LoRA fine-tuning on the VGGT aggregator is employed preserve the pre-trained 3D geometric priors, with multi-task objective:
LVGGT(0) = Leamern + f.depth + Lrmapp
where 9 is the LoRA parameters, Leamera- Laepth and Lamup denote the camera pose loss, the depth loss, and the point map loss, respectively. In the following text, simply use "VGGT" to refer to this fine-tuned VGGT.
Generation prior of TRELLIS TRELLIS Xiang et al. 20244) is a SOTA 3D generative model that provides a strong generation prior. proposes novel representation called Structured LATent (SLAT) that combines a sparse 3D grid with dense visual features extracted from powerful vision foundation model, which captures both geometric (structure) and textural (appearance) information and enables decoding into multiple 3D representations. We choose TRELLIS as the 3D generator in our framework because it has shown great potential in 3D object generation He et al. (2025) ) Li et al. (025b) and inspired many works in downstream applications Yang al. 2025); Cao et al. (2025);
Wu et al. (2024c). It employs a coarse-to torsee-to-fnn to-fine two-stage generation pipeline: generating the sparse structure (SS), represented as sparse voxels (p: J. via SS Flow and then predicting structured latents
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(SLAT) for active SS voxels, represented X (Ps. 2;) ),,, via SLAT Flow, where Pt, 2;, and V denotes the voxel position, the latent vector, and the number of voxels, respectively. The generation in both stages adopts rectified flow transformers Liu et al. (2022) with DINO-encoded image features as conditions. The result of SLAT Flow is then decoded into 3D outputs represented by radiance fields (RF), 3D Gaussians (3DGS), as time-dependent vector field v(x, t) = Vz(z), the transformers Vy in both stages are trained by minimizing the conditional flow matching (CFM) objective Lipman al. (0033
meshes, Le., O Dec( Modeling the backward process
LCF Re (elo0o -
(c zo)ll2
Overview Our Recon ViaGen framework conducts reconstruction and generation simultancously and utilizes the priors in complementary fashion. It builds upon TRELLIS to generate com- plete 3D outputs with strong generation priors to plausibly hallucinate invisible portions to com- pensate for the limitation of reconstruction. The proposed ReconViaGer adopts a coarse-to- toneine reconstruction pipeline. As shown in Fig. 2 reconstruction-bant based multi-view conditions at both the global and local levels. In the next stage, respectively feed the global geometry and local per-view conditions into the SS and SLAT Flow transformers, for multi-view-awan generation. Finally, we further refine the estimated camera poses from VGGT using the generation and introduce pixel-level alignment constraints only in the infer- ence stage for reconstructions highly consistent with input views in detailed geometry and textures.
in the first stage, we use pre-trained VGGT to provide
3.2 RECONSTRUCTION BASED CONDITIONING
We first introduce reconstruction priors in VGGT to provide strong multi-view-aware conditions for the coarse and detailed shape and texture generation of TRELLIS.
Global Geometry Condition VGGT learns strong reconstruction prior to encode explicit 3D lifting information into multi-view image features. Therefore, we first aggregate VGGT features Ouggt into global geometry representation, serving SS Flow conditions to generate more accurate coarse structures. Note that we did not use explicit reconstruction results like point clouds because VGGT features convey richer information including camera poses, depth, point maps, and tracking. A fixed-length token list Tg is aggregated from &rgat via a proposed Condition Net design shown in Fig. 2 Starting from randomly initialized learnable token list Tiits four transformer cross- attention blocks progressively fuse layer-wise features of &vegt with the initial token list and produce Ty Formulated as:
7 CrossAAtt(Q((TT) K (Ovgg), ((dvgg)), iE 1,,1,,
where 7 is initialized with Tima 3 the final output Tg, Q(-), K(-), and (-) are linear layers respectively for query, key, and value projection, and &vggi is the VGGT features that concatenate all views on the token dimension. At the training stage of SS Flow, we freeze the VGGT layers and train the Condition Net together with DiT.
Local Per- View Condition A single token list condition provide limited fine-grained infor- mation for geometry and texture generation in detail. We further adopt the Condition Net design to provide local per-view tokens SLAT Flow conditions for fine-grained generation in both geomet- ric and texture details. A random token list is initialized for each view and fed into the Condition Net to produce view-specific token list Pk, & E [1,
pi+1 ((ossALtn(Q(PP)). K vgst V( Y88 )), i (0, 1,23) and (n)
(4)
where xx transformers offering per-view object appearance guidance for fine-grained generation.
yegt is VGGT features the k-th view. The set of [ sent into SLAT diffusion
3.3 COARSE-TO-FING GENERATION
The overall generation process consists of three stages: (i) coarse structure generation via SS Flow with global geometry condition; (ii) fine detail generation via SLAT Flow with local per-view con- dition; (iii) rendering-awarc pixel-aligned refinement the inference stage only.
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Reconstructoo--ooniionn Flow To integrate the reconstruction prior into generation, the two stages of SS and SLAT Flow in TRELLIS take the global geometry condition Tg and local per-view conditions { | for coarse and fine diffusion guidance, respectively. In the first stage, we simply compute the cross-attention between the condition I and the noisy SS latent in each SS DiT block. In the second stage, as illustrated in Fig. 2 we encourage the gross-attention between the noisy SLAT and each view's condition Pk: and conduct weighted fusion in each SLAT DiT block, which can be formulated as:
(rossAun(Q(())) K(P)) Wik j E {m)
Mj+
where M is the number of SLAT DiT blocks, v/j is the self-attention layer output of the noisy SLAT input yj, and wi (0, 1) is the fusion weight computed via an MLP taking the cross-attention result as input. After the first two stages, the 3D generator can generate multi-view-awarr geometry and texture at both the global and local levels.
Rendering-aware Velocity Compensation To further encourage pixel-aligned consistency be- tween generation results and input views, we develop a rendering- constrain the diffusion trajectory according to inputs. In doing so, we first estimate camera pose with VGGT using the generation results from the second stage, with detailed implementation de- tails included in the appendix. Inspired by the explicit normal regularization used in Hi3DGen Ye ct al. (2025) to improve the inpul-output consistency, when & < 0.5, decode the SLAT into O (e.g. a textured mesh) and conduct rendering for alignment. The SLAT Flow process initializes and updates large number of noisy latents for all voxels simultaneously, which results in challenging collaborative optimization problem. To solve this issue, we novelly propose a mechanism called Rendering-awand -aaaee Velocity Compensation (RVC) to correct the predicted for more accurate gen- eration consistent with input views. Specifically, we render images for O from the refined camera pose estimations C' and calculate the difference between the rendered images and input images as:
velocity compensation to
(Rvc(ve) = LSSIM + LLPIPS + DreamSSim
where LSSIM. LLPIP. and DreamSim are SSIM Wang et al. (2004). LPIPS Zhang et al (2018) and DreamSim losses Fu et al. (2023) (inspired by the practice in V2M4 Chen et al. (2025)) ). respon- sible for measuring the structural, perceptual, and semantic similarity, respectively. To exclude the influence of inaccurate pose estimation, we discard the losses corresponding to some images if their corresponding losses are higher than 0.8. By minimizing LRVe, we iteratively correct the predicted velocity in each SLAT denoising step with a compensation term Av, derived as:
OL azh Oc
Av:
Beg Ou,
Ozio'
where represents Lave for simplicity and di is the predicted target SLAT at current timestep t, computed as di Ft. 1. Vr. The noisy SLAT of next step Tigm can be updated as:
Ft - fprew (u +a +a.Aw)
(8)
where is a pre-defined hyperparameter that controls the extent of the compensation. In this way, the input images serve as a strong explicit guidance to find a denoising trajectory for each local SLAT vector, which leads to more accurate 3D results consistent with all input images detail.
EXPERIMENTS
4.1 EXPERIMENT SETUP
Datasets For LoRA fine-tuning of the VGGT aggregator and Trellis sparse structure transformer, we employ 390k 3D data from the Objaverse dataset Deitke et al. (2024), a large-scale 3D object dataset that provides rich variety of shapes and textures, with 60 views rendered per object for fine-tuning. For each object mesh we render 150 view images in a resolution of 512x512 under uniform lighting conditions following TRELLIS Xiang et al. (0024). For evaluation, two benchmark datasets to thoroughly assess the performance of our model: (i) Dora- Bench Chen et al. (2024), benchmark organized based on 4 levels of complexity, combining 3D data selected
selected

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from the Objaverse Deitke et al. (2023), ABO Collins et al. (2022) and GSO Downs al. (2022) dataset; and (ii) OmniObject3D a
3D object dataset containing 6,000 qualiiy textured meshes scanned from real-world objects, covering 190 daily categories. We randomly sample 300 objects from Dora-Bench and 200 objects covering 20 categories from OmniObject3ID. We follow |He et al.] (2024) to render 24 views different elevations, and randomly chose 4 of them as vulti-view input for evaluation on OmniObject3D. On Dora-Bench, we follow the camera trajectory of TRELLIS Xiang et al. (2024) to render 40 views and choose 4 views (No.0, 9, 19, and 29) with uniform interval to adapt to the setting of some baseline methods (LGM Tang et al. (2025) and InstantMesh Xu et al. (2024b)).
Evaluation Metries We We employ PSNR, SSIM, and LPIPS to evaluate the accuracy of synthesized novel views from 3D outputs, Chamfer Distance (CD) and F-score to evaluate the generated geom- etry accuracy and completeness. PSNR, SSIM, and LPIPS are evaluated on novel views of images rendered at the resolution of 512x512. CD and F-score are evaluated by sampling 100k points from the 3D outputs (using the center positions for 3D Gaussian outputs), with all object points normalized to the range of [, When calculating the F-score, the radius r is set to 0.1.
Baseline Methods Baseline methods for comparisons include (i) TRELLIS SS Xiang et al. (2044)
generates 3D meshes from multi-view images using TRELLIS in the stochastic mode, which ran- domly chooses one input view to condition each step of denoising; (ii) TRELLIS-M Xiang et al.
20224: TRELLIS in the multidiffusion mode, which computes the average denoised results con-
ditioned on all imput views; (iii) Hunyuan3D-2.0-mm
Zhao et al.] (202); concatenate DINO fea- tures of input images from fixed viewpoints as conditions to generate mesher"
[ (iv) InstantMesh Xu
et al. (2024b): predicts Triplane for mesh outputs from multiple images with fixed viewpoints: (v) 1.GM Tang et al. (2025): predicts pixel-aligned 3D Gaussians from multiple images with fixed viewpoints; (vi) LucidFusion He et al. (2024): predicts relative coordinate maps for 3D Gaussian outputs; (vii) VGGT Wang et al. (2025a): reconstructs the point cloud from mieti-view inputs in a feed-forward manner. We compare our methods with a wide range of existing SOTA baseline methods: (a) 3D generation models {i. ii, iii}; (b) large reconstruction models with known camera poses {iv, v): (c) pose-free large reconstruction models with 3DGS or point cloud outputs {vi, vii). For 3D generation models {i. ii, iii), we use the same approach as Camera Pose Estimation in fine detail reconstruction to align the generated 3D models to the ground-truth models. Besides, we also compare with closed-source commercial 3D generation models like Hunyuan3D-2.5 and Meshy-5 on in-the-wild testing.
Implementation Details For LoRA fine-tuning of VGGT aggregator and TRELLIS transformer, we set the rank as 64, the alpha parameter for LoRA scaling as 128, and the dropout probability for LoRA layers as O. We only apply the adapter to qkv mapping layer and the projectors of each attention layer. During fine-tuning VGGT aggregator, we randomly sample 1 ~ 4 views from 150 images and use the AdamW optimizer with a fixed learning rate of 1 x 10- For the fine-tuning of SS Flow and Slat-Flow transformer, we build upon TRELLIS Xiang et al. (2024), incorporating classifier-free guidance (CFG) with a drop rate of 0.3 and Adam optimizer with fixed learning rate of 1 x 10-1 We fine-tune the SS-Flow transformer using 8 NVIDIA A800 GPUs (80GB memory) for 40k steps with batch size of 192. Differently, with a batch size of 128. During inference, we set the CFG strengths in SS generation and SLAT generation to 7.5 and 3.0, and use 30 and 12 sampling steps to achieve optimal results. The a in rendering- aware velocity compensation is set to 0.1 in our practice.
finetune the SLat-Flow transformer
4.2 EXPERIMENT RESULTS
We present the quantitative comparisons between our Recon ViaGen and other baseline methods in Tab. | for evaluation on the Dora-bench and OminiObject3D dataset. The proposed method achieves consistently superior performance to other methods on both image- reconstruction consistency (PSNR, SSIM, and LPIPS), geometry accuracy (CD), and shape com- pleteness (F-score). Impressively, our Recon ViaGen seamlessly integrates the generation and re- construction priors from TRELLIS Xiang et al.) (2024) and VGGT Wang et al.] (2025a), whose performance surpasses both of them. Note that VGGT performs better on Dora-bence than on Om- niObject3D because
Quantitative Results
views can capture richer visual cues than random views.
'The fresh version, Hunyuan3D-2.55 has not been open-sourced, which is unsuitable for large-scale evalua- tion on henchmarks, so we use the open-sourced version, Hun2uan31D-2.0.
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Table I: Evaluation the Dora-bench and OmniObject3D dataset. Best results in bold.
Dora-bench LPIPS]
OmniObjeet3D LPIPS]
Method
PSNR1
SSIM
CD1
F-score]
PSNR1
SSIM
CD)
VGGT Wang et al.
2025a)
0.112
0.921
0.091
0.900
TREI 1S--
12124
16.562
0.876
0.103
0.176
0.807
16.021
0.771
0.264
0.102
0.906
TRELLIS-M a
2014
16.706
0.882
0.111
0.144
0.843
16.861
0.790
0.242
0.072
0.952
Hunyuan 3 D-2.amm Zio
2025
20.221
0.896
0.093
0..99
0.937
16,665
0.813
0.165
0.124
0.871
Tang
InstaniMtent All draie
2024b
18.922
0.870
0.120
0.110
0.865
17.499
0.818
0.145
0,094
0.907
LucidFusion
16.509
0.835
0.144
0.131
0.831
16.254
0.771
0.144
0.114
0.868
Recon Viaen (Ours)
22.632
0.911
0.090
0.090
0.953
19.767
0.847
0.141
0.059
0.959
TRELLIS-S
TRELLIS-M
LGM
InstarMaak
LuckdFusian
Humywan3 2.0mv
GT
Figure 3: Reconstruction result comparisons between our Recon ViaGen and other baseline methods on samples from the Dora-bench and OmniObject3D datasets. Zoom in for better visualization.
Besides, our method also gets better results than previous SOTA pose-free multi-view reconstruction methods that integrate regression-basee generation priors by a large margin, especially on PSNR, CD, and F-score, which validates the superiority of ReconViaGen On the settings of more input views, we separately evaluate VGGT that can accept an arbitrary number of inputs for comparison, which is included in the appendix. We also present the camera pose estimation accuracy in the appendix.
Qualitative Results We further present extensive qualitative comparisons to demonstrate the su- periority of Recon ViaGen. We first select some examples from the OmniObject3D and bora-bench dataset for visualization, as shown in Fig. 3 The reconstruction results of Recon ViaGen have the most accurate geometry and textures compared other methods. We further evaluate several base- line methods in-the-wild multi-view images. As shown in Fig. 4 ViaGen exhibits strong robustness even in comparison with the multi-view version of closed-source commercial 3D genera- tion models like H-nyuan3D-255555 and Meshy-5 More qualitative results are included in the appendix.
and
Table 2: Quantitative ablation results on the Dora-bench dataset.
GGC
PVC
RVC
PSNR*
SSIM
LPIPS.
CD1
F-score"
(a)
x
x
16.706
0.882
0.111
0.144
0.843
(b)
20.462
0.102
0.941
21.045
0.905
0.093
0.093
0.937
(d)
22.632
0.911
0.090
0.090
0.953
8

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MV Input
TRELLIS-S
TRELLIS-M
Moshy-S-mv
Hunyuan3 D-2.5-
Ours
Reconstruction results in-the-wild samples. Note that commercial 31) generators quire input images from orthogonal viewpoints, while ours can accept views from arbitrary camera poses for robust outputs. Zoom in for better visualization in detail.
Figure 4:
MV Impat
(e)
de
(6)
65) Pull
MV Impus
(B)
ch)
(i)
(4) Full
Figure 5: Qualitative comparisons for different variants of Recon ViaGen for ablative study. Zoom in for better visualization in detail.
4.3 ABLATION STUDY
The proposed Recon ViaGen framework comprises three novel designs to integrate reconstruction priors into the diffusion-based 3D generation: (i) the global geometry condition (GGC); (ii) the per-view condition (PVC); and (iii) the rendering-aware velocity compensation (RVC). We conduct ablation studies validate the individual effectiveness of each component. On the bench dataset, we start from a basic TRELLIS -M baseline (ReconViaGen ViaGen without all designs, Tab. (4) and progressively add one component, leading to variants (b,c,d). As shown in Tab. [ integrating GGC, which strongly improves the prediction accuracy of coarse structure, brings a large performance gain on almost all metries. Further integrating PVC can lead extra improvement, especially on PSNR, which proves the effectiveness in improving local per-view alignment. Finally, adopting RVC, though in the inference stage only, brings additional increments in both shape completeness and fine-ge tineggrinedd accuracy in geometry and texture. Qualitative comparisons in Fig. 5 visualize the positive effect of each component: global geometry conditioning greatly corrects the global shape, per-view conditioning produces local details in geometry and texture of high consistency with each view, and rendering-aware velocity compensating impressively refines the fine-grained appearance, leading to high-quality results.
Since Recon ViaGen can take an arbitrary number of images, natural question is how reconstruction quality scales with the number of images. To investigate this, we conducted an ablation study varying the number of input views on Bora-Bench, with results summarized in Tab. 3 We observe that reconstruction performance consistently improves as more images are provided. However, the marginal gains gradually diminish, indicating a saturation effect when the number of views becomes large. The visualization results shown in Fig. 6 which also shows that Recon ViaGen can process
9

---

MV Input
Rendresd Images
MV Input
Renskeed Images
Input
Bendend Images
MV Inpur
Rendered Imagos
Figure 6: Qualitative comparisons for different numbers of input images with ViaGen. Zoom in for better visualization in detail.
any number of input images from any viewpoint. More ablation results on the detailed designs, including the choice of condition form for SS and SLAT Flow, can seen the appendix.
Table Quantitative ablation results of the number of imput images on the Dora- bora-bench dataset.
Number Images
PSNR
SSIM
LPIPS.
CD1
F-scoref
18.438
0.887
0.106
0.135
0.838
19.568
0.894
0.099
0.131
0.867
22.632
0.911
0.090
0.090
0,953
22.823
0.912
0.089
0.084
0.958
23.067
0.914
0.090
0.081
0.961
5 CONCLUSION
In this paper, we have presented ReconViaGen, a novel coarse-to-fine framework that effectively integrates strong reconstruction priors with diffusion- 3D generative priors for accurate and complete multi-view 3D object reconstruction. We first analyze the inherent reasons leading to challenge leveraging diffusion-based 3D generative priors into reconstruction: insufficient cross-view correlation modeling and stochastic denoising process with weak constraint from input images. Therefore, we effectively use powerful reconstruction priors with three novelly designed mechanisms to enhance the multi-view correlation awareness in 3D diffusion learning and establish strong constraints for a reliable denoising process. Extensive experiments have demonstrated that Recon ViaGen achieves SOTA performance in both global shape accuracy and completeness well as local details in geometry and textures. As future work, with the development of 3D reconstruction and 3D generation, stronger reconstruction or generation priors can be integrated into our framework to further improve reconstruction quality via generation.
6 ACKNOWLEDGMENTS
The work was supported in part by Guangdong Provincial Outstanding Youth Fund with No. 202381515020055 the Shenzhen Outstanding Talents Training Fund 202002, the NSFC with Grant No. 62293482, the Guangdong Research Projects No. 2017ZT07X152 and No. 2019CX01X104, the Guangdong Provincial Key Laboratory of Future Networks of Intelligence (Grant No. 202281212010000 and the Shenzhen Key Laboratory of Big Data and Artificial Intelligence (Grant No. SYSPG2204421 the Guangdong Province Radio Science Data Center.
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15


Reviews:
[{"review_id": "6SfMA8mUg7", "reviewer": "Reviewer_Zbjq", "summary": "This paper proposes the ReconViaGen framework, which aims to combine strong reconstruction priors (from VGGT) with diffusion-based generative priors (from TRELLIS) to address the limitations of pure reconstruction methods that lead to incomplete results and pure generative methods that, while complete, lack consistency with the input view. The method employs a coarse-to-fine strategy: using Global Geometric Conditions (GGC) to guide coarse structure generation and Per-View Conditions (PVC) to guide fine detail generation. Additionally, a Rendering-aware Velocity Compensation (RVC) mechanism is introduced, which is used only during inference to ensure pixel-level alignment of the generated results with the input view. Experiments on Dora-Bench and OmniObject3D datasets demonstrate that this method achieves SOTA levels in both completeness and consistency.", "questions": "- The paper uses multi-view inputs as conditions for generation, but the results do not seem significantly better than current single-view 3D model generation methods (like Direct3D-S2[1]). Have the authors tried using single-view inputs as conditions for generation? If so, what were the results? I think it would be interesting to see how the model performs with single-view inputs compared to multi-view inputs.\n- When using VGGT as the feature extractor for multi-view inputs, if the generated multi-view images are inconsistent, how much does this affect the quality of the final 3D model?\n\n[1] Direct3D‑S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The paper provides clear and detailed descriptions of the methodology and experimental design, making it easy to understand and replicate. \n\n- The proposed ReconViaGen framework effectively addresses the limitations of pure reconstruction and pure generative methods by combining their strengths. The coarse-to-fine strategy, utilizing Global Geometric Conditions (GGC) for coarse structure and Per-View Conditions (PVC) for fine details, allows for a more accurate and consistent 3D reconstruction from single images. The results on Dora-Bench and OmniObject3D datasets demonstrate significant improvements in both completeness and consistency compared to state-of-the-art methods.", "weaknesses": "- The requirements for multi-view inputs may limit the applicability of the method in scenarios where only single-view inputs are available. In practical applications, users often can only provide a single view. It is unclear how the method would perform with single-view inputs, and the paper does not discuss or analyze this aspect.\n- In Section 4.3, the ablation study does not provide visual comparisons to illustrate the effects of different modules, relying solely on numerical results to demonstrate their effectiveness. This makes it difficult for readers to intuitively understand the impact of each module on the final results.", "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": "SF9yMHMywJ", "reviewer": "Reviewer_eFoP", "summary": "The paper proposes ReconViaGen, a coarse-to-fine framework that integrates pose-free multi-view reconstruction priors (VGGT) with diffusion-based 3D generative priors (TRELLIS) for accurate and complete 3D object reconstruction from sparse, uncalibrated views. The method: (1) builds multi-view-aware conditions from VGGT via a ConditionNet, forming a Global Geometry Condition (GGC) for coarse Structure (SS) Flow and Per-View Conditions (PVC) for fine SLAT Flow; (2) introduces a rendering-aware velocity compensation (RVC) that, during inference, adjusts rectified-flow velocities using SSIM/LPIPS/DreamSim losses between rendered views and inputs; (3) refines camera poses by registering inputs into the generator’s space through rendering, VGGT estimation, feature matching, and PnP-RANSAC. Experiments on Dora-Bench and OmniObject3D show consistent SOTA improvements on image consistency (PSNR/SSIM/LPIPS), geometry (CD), and completeness (F-score), with ablations isolating the benefits of GGC/PVC/RVC and robustness to number/quality of views.", "questions": "1.\tProvide attention visualizations and cross-view token similarity to show GGC’s global aggregation in SS and PVC’s local correspondence in SLAT?\n2.\tConduct “Information bottleneck” experiments in SS (vary GGC token capacity) to study structure convergence and downstream SLAT performance?\n3.\tProvide sensitivity studies for RVC: α, timestep scheduling (t threshold), loss weights, and the 0.8 outlier discard rule? How do these affect convergence, runtime, and stability?\n4.\tWhether RVC, being inference-only, creates a potential train–inference gap. Why not introduce a lightweight RVC-like self-distillation during training to reduce distribution shift?\n5.\tReport performance on real multi-view captures (e.g., DTU object subset or an in-house dataset) to validate real-world generalization?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tInjects reconstruction priors (VGGT) into a 3D diffusion generator, addressing a well-known failure mode of existing generative methods—global structure drift & local inconsistency.\n2.\tThe clear division of labor and strong motivation: GGC for structural cues and PVC for fine details match information granularity.\n3.\tRVC corrects the diffusion velocity field with differentiable rendering feedback at inference, simple to implement yet clearly improves input consistency.\n4.\tBroad evaluation with both image-consistency and geometry metrics plus pose accuracy; ablations are comprehensive and convincing.", "weaknesses": "1.\tWhile results support “GGC is more suitable for SS and PVC is more suitable for SLAT,” deeper interpretability is missing.\n2.\tRVC hyperparameter/stability analysis is light: sensitivity to α, timestep threshold (t < 0.5), and the outlier rejection threshold (0.8) is not quantified; inference-time overhead (decoding/rendering per step) is not reported.\n3.\tPose refinement pipeline is engineering-heavy.\n4.\tRVC is inference-only, creating a potential train–inference gap.\n5.\tSome theoretical clarity on how RVC interacts with rectified flow objectives is missing; decoding to different 3D representations (RF/3DGS/Mesh) and their effect on metrics could be further analyzed.", "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": "QBZHeGIfy6", "reviewer": "Reviewer_LmLx", "summary": "This paper proposes to incorporate VGGT [2] features into Trellis [1] generation process via simple cross attention in order to improve the adherence of the Trellis Generators (SS and SLAT) to the provided input views.\n\nThis paper demonstrates an interesting insight towards how the 3D global information aggregated by VGGT is more meaningful to the Trellis denoisers compared to providing the raw unprocessed RGB images directly, however it falls short of shedding a scientific light onto why this is the case. Moreover, the important high-frequency details in their results actually come from a highly empirical inference-time iterative refining step (RVC) which almost doubles the generation time (at the least, details missing), and not from the featurewise cross connection of VGGT and Trellis. Thus this research raises concerns about the actual utility of the proposed mutli-step pipeline. More detailed concerns in the Weakness section. \n\n\n\n[1] Xiang, J., Lv, Z., Xu, S., Deng, Y., Wang, R., Zhang, B., ... & Yang, J. (2025). Structured 3d latents for scalable and versatile 3d generation. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 21469-21480).\n\n[2] Wang, J., Chen, M., Karaev, N., Vedaldi, A., Rupprecht, C., & Novotny, D. (2025). Vggt: Visual geometry grounded transformer. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 5294-5306).", "questions": "Responses to the Major Concerns 1 and 2 would be most informative to me in making the final decision in the discussion phase. \n\n**Minor Suggestions**:\n1. The visual metrics (PSNR, LPIPS and SSIM) for the VGGT baseline, however low, will be helpful for completeness of the scientific study. They have been possibly left out because of the low numbers coming from the holes in the renders of VGGT, but not detailed why exactly they have been left out. Clarification appreciated.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The authors combine two very strong recent works VGGT and TRELLIS with a clean Cross-Attention module into an E2E pipeline which yields decent improvements in the results. \n\n2. The paper is presented in a clear and lucid manner.", "weaknesses": "The paper however raises the following concerns:\n\n**Major Concerns**:\n1. The RVC step contributes the most amount of visual details to the generated results, and as such, this step lacks sufficient details and discussion in the paper. Most importantly, how does an RVC only baseline fare against the `(d)-full` version of the method. For the Rendering aware correction, something simple as the predicted point-clouds of VGGT or even sparse reconstructions from COLMAP are meaningful because in this version no training is needed. Moreover, it would be quite important for this paper to present details about how the hyperparameters of the final step are tuned, and whether per-asset generation tuning is needed of if some fixed values could be set in the pipeline and then generalised for inference. Lastly, how much generation latency does the RVC step add to the full pipeline? \n\n2. As alluded to earlier in the summary, imho, a discussion about why the global features from VGGT help the Trellis generation so much is imperative for this paper. Of course we can say that the learned features aggregated by VGGT through the regressive prior capture much more 3D structural information than just the 2D multi-views, but then the question that rises is how come the generative 3D prior implicitly learned by Trellis is not enough? If it's possible to entirely use Trellis (through finetuning), then the solution becomes even more cleaner than the current one which integrates two different large-scale foundation models. \n\n**Minor concerns**:\n1. Are the two ConditionNets for the global condition as well as the local per-view condition tokens the same? This is not clarified properly in the draft as the Mathematical notation is the same for the two, and this is not clarified explicitly. \n\n2. What is the intuition behind the weighted fusion of the per-view features cross-attention conditioning? Just for computational efficiency? Details are missing for this particular operation. How can the MLP predict from just the current' view's features which of them are important and which aren't when fusing multiple views? Why not just add all the features together and them apply a linear layer/MLP? Ideally, the simplest operation should be applying a cross-attention with the KV cache coming from all the views, but might cause OOM. Clarification from the authors would be appreciated. An ablation on this would make the paper more well rounded.\n\n3. How is the SLAT-FLow transformer trained? Similar to the SS-FLow? Implementation Details paragraph lines 350-359 do not cover this explicitly.\n\n4. What is the total time E2E time required for the 3D model generation starting from Multi-view inputs to receiving the final 3D model output? How much the RCV contribute to the generation latency? A detailed latency table would be useful for demonstrating the utility of the final proposed pipeline. \n\n5. I think it would be very beneficial to include more qualitative examples for the (c) v/s (d) ablation study. It will be very insightful to understand where exactly the ~1.5 dB improvement is coming from.", "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": "X98hxQE9Fx", "reviewer": "Reviewer_3uhU", "summary": "1. **Originality-wise**: The paper introduces a novel framework that uses implicit, 3D-aware features from a reconstruction network (VGGT) to condition a diffusion generator (TRELLIS) , and a rendering-aware feedback loop (RVC), correcting the denoising trajectory during inference. It represents a new and sophisticated approach to solving the trade-off between reconstruction completeness and generative accuracy.\n\n2. **Quality-wise**: The claims are substantiated with a robust and comprehensive evaluation on challenging benchmarks like Dora-Bench and OmniObject3D.\n\n3. **Clarity-wise**: The manuscript is clearly written, with well-structured methodology, detailed explanations, and intuitive visualizations that enhance understanding.", "questions": "Please refer to Weaknesses part.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. Clear problem formulation and strong motivation: The paper accurately identifies the central conflict in modern 3D reconstruction between the \"incompleteness of reconstruction\" and the \"inaccuracy of generation.\" The analysis of the root causes—such as insufficient cross-view correlation and poor controllability of the generation process—provides a solid foundation for the proposed method.\n\n2. Innovative and well-Designed framework: ReconViaGen is more than a simple cascade of reconstruction and generation models. It achieves a deep fusion of the two priors by integrating the feature-level output of the reconstructor directly into the conditioning mechanism of the diffusion generator. The coarse-to-fine strategy, guided by separate global and local conditions, is a logical and effective way to leverage multi-level reconstruction cues.", "weaknesses": "This paper is good writing and well visualized, but still with some minor weakness:\n\n1. Concern about computational cost: While highly effective, the RVC mechanism requires 3D decoding, rendering, loss computation, and gradient calculation at multiple steps of the diffusion inference process. This will undoubtedly add significant computational overhead. The paper does not provide a detailed analysis of this overhead, which could be a potential bottleneck for applications requiring fast inference. A discussion on the trade-off between speed and accuracy would be beneficial.\n\n2. The effectiveness of the RVC mechanism is critically dependent on the accuracy of the estimated camera poses. In challenging sparse-view scenarios where pose estimation is inherently less reliable, any inaccuracies will directly corrupt the rendering-based loss, providing erroneous guidance to the diffusion model. This risks actively misleading the denoising trajectory rather than correcting it. The paper lacks a crucial robustness analysis to quantify the sensitivity of the final reconstruction quality to errors in camera pose estimation.\n\n---\n\nI have listed my concerns, and the score will be adjusted based on the author's response.", "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)