ReLi3D: Relightable Multi-view 3D Reconstruction with Disentangled Illumination
Authors: Jan-Niklas Dihlmann
Mark Boss
Simon Donné
Andreas Engelhardt
Hendrik Lensch
Varun Jampani
Venue: ICLR 2026 Poster (2026)
Keywords: Material
Reconstruction
Large Reconstruction Model
Multi-View
Illumination
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Reconstructs geometry, PBR materials, and HDR environment lighting from a few views in sub-second time via cross-view fusion and a two-path illumination disentanglement design.

Abstract: Reconstructing 3D assets from images has long required separate pipelines for geometry reconstruction, material estimation, and illumination recovery, each with distinct limitations and computational overhead. We present MIDR-3D, the first unified end-to-end pipeline that simultaneously reconstructs complete 3D geometry, spatially-varying physically-based materials, and environment illumination
from sparse multi-view images in under one second. Our key insight is that multi-view constraints can dramatically improve material and illumination disentanglement, a problem that remains fundamentally ill-posed for single image methods. Key to our approach is the fusion of the multi-view input via a transformer cross-conditioning architecture, followed by a novel unified two path prediction strategy. The first path predicts the object’s structure and appearance, while the second path predicts the environment illumination from image background or object reflections. This combined with a differentiable Monte Carlo multiple importance sampling renderer, creates an optimal illumination disentanglement training pipeline. Further with our mixed-domain training protocol, combining synthetic PBR datasets with real-world RGB captures, we establish generalizable results across geometry, material accuracy, and illumination quality. By unifying previously separate reconstruction tasks into a single feed-forward pass, we enable near-instantaneous generation of complete, relightable 3D assets.

Published as a conference paper at ICLR 2026
RELI3D:
RELIGHTABLE WITH
MULTI-VIEW
3D DISENTANGLED
RECONSTRUCTION
ILLUMINATION
Jan-Niklas Dihlmann" * University of Tübingen
Mark Boss
Simon Donné
Andreas Engelhardt Stability Al
Stability AI
Stability AI
Hendrik P.A. Lenseh University of Tübingen
Varun Jampani Stability Al
ABSTRACT
Reconstructing. 3D) assets from images has long required separate pipelines for ge- ometry reconstruction, material estimation, and illumination recovery, each with distinct limitations and computational overhead. We present ReLi3D, the first uni- fied end-to-end pipeline that simultaneously reconstructs complete 3D geometry, spatially-varyie
eyysically-based materials, and environment illumination from sparse multi-view images in under one second. Our key insight is that multi-view constraints can dramatically improve material and illumination disentanglement, a problem that remains fundamentally ill-posed for single-image methods. Key to approach is the fusion of the multi-view input via transformer cross- conditioning architecture, followed by a novel unified two-path prediction strat- egy. The first path predicts the object's structure and appearance, while the sec- ond path predicts the environment illumination from image background or object reflections. This, combined with differentiable Monte Carlo multiple impor- tance sampling renderer, creates an optimal illumination disentanglement training pipeline. In addition, with mixed domain training protocol, which combines synthetic PBR datasets with real-world RGB captures, we establish generalizable results geometry, material accuracy, and illumination quality. By unifying pre- viously separate reconstruction tasks into single feed-forward pass, we enable near-instantaneous generation of complete, relightable 3D assets. Project Page: https::/relidd.jjdll waa.:iiidddddiimmmoooooooooo com/
ReLi3D
Pokymoite Jnst Poces receomnatien
Mort Viw spenett
Eraube
3D Sooemes
30 Solaeee Rir.
Figure 1: Fast, illumination disentangled reconstructions. ReLi3D reconstructs high-tuulity 3D meshes with physically based materials from sparse input images, while disentangling illumination effects; all in just 0.3s. is robustly trained on cross-domain datasets and excels in both single- and multi-view cases, synthetic data well real-world examples.
Alsso Stability AI.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Reconstructing production-ready 3D assets from images remains a challenging task with immense potential for industrial design, interactive media, robotics. Two lines of progress have emerged: (i) Generative models based on diffusion, which can achieve striking geometric fidelity, but with long inference times and hallucination, (ii) Large Reconstruction Models (LRMs) such as LRM (Hong et al. 2023), SF3D (Boss et al., 2024), and TripoSR (Tochilkin et al. (024a) that perform direct feed-forward inference from images to 3D. While LRMs are fast and practical, gap persists be- tween research prototypes and what artists require from 3D reconstruction, which is accurate reconstruction from multiple views and illumination disentanglement resulting in spatially varying Physically Based Rendering (PBR) materials that support relighting.
Unfortunately, many existing approaches optimize only for single-view reconstruction, which is inherently ill-posed. The same 2D appearance can arise from numerous combinations of surface reflectance and illumination. Regularization or learned priors help, but ambiguity remains, espe- cially in unobserved areas, leading to incomplete spatially varying material predictions, unreliable normals, and therefore limited relighting fidelity.
In our perspective, geometric consistency across multiple views provides the missing constraints to separate material properties from lighting effects. When multiple observations see the same surface point under a common illumination, cross- view agreement narrows the feasible solution space and turns an ill- ill-posed single-view problem into much better constrained one. To operationalize this, design architecture where multi-view fusion is not add-on for robustness, but the primary mechanism for material-lighting disentanglement.
In this paper, we present ReLi3D, unified ferd-forward system that turns variable number of posed images into a textured mesh with spatially varying PBR materials and a coherent HDR en- vironment in less than second. In order to allow for Multiview Illumination Disentanglement Reconstruction we utilize a two-path approach achieved through the following novel contributions:
Cross-view Fusion A shared cross cross-conditioning transformer ingests an arbitrary number of views and builds unified feature triplanes used by both paths, driving consistency across viewpoints.
Two-path Illumination Disentanglement. A tamnnetry+appparaae path yiclds mesh and from this unified triplane, while lighting
svBRDF (ubbedorougiines/meellliiooooo path fuses mask-aware tokens to predict efficient RENI+ (Gardner et al. 2023) latent code representing a coherent HDR environment.
Disentangled Training via MC+MIS. A differentiable physically-based Multiple Impor- tance Sampling (MIS) Monte Carlo (MC) renderer ties both paths together, enforcing phys- ically meaningful materials and illumination disentanglement.
Mixed-domain Training. We train on mixture of synthetic PBR-supervised data and real multi-view captures using image space self-supervision to bridge the gap and allow for real-world generalization.
Together, these pieces deliver the first feed-forwer forward pipeline that jointly reconstructs geometry, spa- tially varying materials, and HDR illumination at interactive speed. Our experiments show improved reconstruction, relighting fidelity and material realism over recent (i) generative and (ii) reconstruc- tion pipelines; will release code and weights to foster adoption and reproducibility.
2 RELATED WORK
ReLi3D lies at the intersection of 3D reconstruction, inverse rendering, and appearance estimation. The most closely aligned approaches are image-to-3D reconstruction and generation methods, and seek to clearly differentiate our feed-forward approach from optimization-batt reconstruction methods.
Inverse Rendering Inverse rendering estimates shape, appearance, and environment lighting from image observations, an inherently ambiguous problem with many plausible material- material--lghting com- binations explaining identical observations. Modern methods leverage differentiable rendering (Li
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Published as a conference paper at ICLR 2026
et al. 2018a Liu al. 2019)
with scene representations such NeRF (Mildenhall al. 2021 or
Gaussian splats (Kerbl et al. 2023) to reconstruct scenes from dense RGB imagery (Zhang et al.
2021b; Boss et al.] 2021 2022 Engelhardt et al. 2024 Liang et al. 2024 Dihlmann et al. 2024]
Although regularization losses in shape, materials, or environment (Barron & 2013) Li et al,
2018b, (Gardnrr al. 2017) help reduce ambiguity, these optimization-based approaches require dense multi-view imagery and lengthy inference times. None manages to reconstruct 3D objects from sparse views, let alone single images. In contrast, ReLi3D performs feed-forward inference from sparse views while jointly estimating spatially varying materials and HDR environments via RENI+ (Gardner et al. 2023).
Image-to-3D Generation Score Distillation Sampling methods (Poole et al. 2023 Shi et al. 2023 Wang al. 2024b) optimize 3D representations using 2D diffusion priors but suffer from artifacts and impractically slow inference. Multi-view generation approaches (Liu et al. 2023 Long ctt al. 2024 Voleti et al. 2024, Tang et al. 2024) first generate consistent views and then apply reconstruction, but face view inconsistencies and inherit inverse rendering ambiguities.
Direct 3D diffusion methods model object distributions in triplane (Shue et al. 2023) Cheng al. 2023 Yariv et al., 2024) or compressed latent spaces (Zhao et al. 2025 Xiang et al. (024). SPAR3D (Huang et al., 2025) uniquely diffuses both geometry and PBR materials by first generat- ing sparse point clouds and then regressing detailed structure and appearance, but requires expensive probabilistic sampling. The lack of large-scale PBR data typically precludes joint geometry-material modeling in diffusion frameworks. Our feed-forward approach achieves comparable quality without the computational overhead of generative sampling, enabling end-to-end joint structure and appear- ance prediction.
Early regression approaches (Choy et al. [2016 Wang et al. 2018 Mescheder al. 2019) were limited by small datasets like ShapeNet (Chang et al. (015). restricting
Image-to-3D Reconstruction
generalization. Large Reconstruction Models (LRMs) (Hong et al. 2023 Tochilkin et al. 2024b
Boss al. 2024 now perform direct forward inference scale using transformer architectures
and large datasets (Deitke et al. 2022 Reizenstein et al.) (02T].
Although fast and practical, existing methods such as SF3D (Boss al. 2024) predict only sin- gle roughness/metallic values per object rather than spatially varying materials, and lack environ- ment estimation. Most critically, these approaches optimize for single-view reconstruction, leaving material-lighting disentanglement fundamentally ill-posed, and the same appearance arise from countless material-illuminationn combinations.
The parallel work LIRM (Li et al., 2025) addresses similar goals through progressive optimiza- tion but lacks illumination prediction and relies purely on synthetic supervision, limiting real-world applicabilitty. ReLi3D uniquely leverages multi-view constraints the primary mechanism for material-lighting disentanglement, enabling robust spatially varying PBR reconstruction with en- vironment estimation through mixed-domain training that bridges synthetic and real-world data.
PRELIMINARIES
Reconstructing 3D objects with realistic materials and lighting from images requires understand- ing how light interacts with surfaces and how efficiently represent 3D) information. This section introduces the key concepts underlying our approach: physically based material representations, environment illumination modeling, and neural 3D representations that enable feed-forward recon- struction.
3.1 PHYSICALLY BASED MATERIAL REPRESENTATION
An object's visual appearance results from how its surface reflects and refracts light, formally de- scribed by the bidirectional reflectance distribution function (BRDF) fr Wou)). This function models the fraction of light reflected into direction Wout given incoming light from direction win- When material properties vary across the surface, we have spatially varying BRDF (svBRDF).
In practice we parameterize materials using Disney's 's principled BRDF (Burley & Studios 2012) with metalnic-roughness representation: RGB albedo (base color) p. scalar roughness (controlling
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Geometry
Nuw FNVESIII www.NNRhea Abpooree
APp
Welgght
Geeerety
Moch
Appaarance
Protaro Tiglore
Vecellic
Sump Map
Ikaninttion
Endiremenn
Perdo Laterta
Figure 2: ReLi3D Overview. Multi-view input images fused by shared cross-conditioning transformer into two parallel paths: a Geometry & Appearance Path (blue) using a Triplane Trans- former to predict mesh geometry and PBR materials, and an Illumination Path (green) using Multi- View Illumination Transformer to estimate HDR environments. Both paths are unified through differentiable Monte Carlo Multiple Importance Sampling rendering learn to wproduce complete relightable 3D assets.
surface smoothness). and scalar metallic parameter m. Additionally, normal bump maps encode freeueryyyncy surface perturbations for fine geometric detail. For reconstruction scenarios without predefined UV mappings, we define the local tangent space with the surface normal as up-direction and align the tangent with the world coordinate system (Vainer al. 2024
3.2 ENVIRONMENT ILLUMINATION
Realistic rendering requires modeling the incoming illumination from all directions, typically repre- sented environment map (w) that depends only direction Traditional representations using spherical harmonics or spherical Gaussians are limited in capturing high-frequenne lighting details like sharp shadows or bright light sources. RENI++ (Gardner et al.) (202) provides a more condensed expressive representation by learning a compact latent space for realistic illumination patterns. Environment maps are decoded from latent codes z IR. Tp49%x as:
(wen((w) = exp(fo(z,, (ww))
(1)
where fe is the pre-trained decoder and 7(w) provides positional encoding. This enables low dimensional representation perfectly suited for fast feed-forward reconstruction.
3.3 LARGE RECONSTRUCTION MODELS AND TRIPLANE REPRESENTATIONS
Recent advances in feed-forward 3D reconstruction leverage large transformer models trained on extensive 3D datasets. Methods like LRM (Hong al. 2023) and TripoSR (Tochilkin et al. 2024a)
demonstrate that direct image-to-3D reconstruction is feasible without per-object optimization.
These approaches typically use triplane representations to efficiently encode 3D information. A triplane T E R33x'xH consists of three orthogonal 2D feature planes. For any 3D point (x, y, z), features are extracted by projecting onto each plane:
f(p) = concat (2 v), Ty (v. z). (2,
These concatenated features are then decoded through MLPs to predict geometric and appearance properties. SFFD (Boss et al. (2244) exemplifies this paradigm, it encodes input images with DI- NOv2 (Oquab et al.) 2023), processes them through transformer with camera conditioning, and outputs triplane features. These are decoded into geometry via DMTet (Shen et al. 2021 and tex- tured using fast UV unwrapping. However, SF3D is limited to single- input, global material properties, and lacks environment estimation. Limitations our approach addresses through multi- view fusion and spatially varying material prediction.
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4 METHOD
Our core insight is that multi-view constraints provide the missing information to disentangle mate- rial properties from lighting effects, problem that remains fundamentally ill-posed for single-view methods. We achieve this through a unified two-path architecture that jointly predicts object struc- ture with spatially varying materials and environment illumination from arbitrary numbers of input views. Figure illustrates our complete pipeline.
4.1 VULWI-VIEW ILLUMINATION DISENTANGLEMEN ARCHITECTURE
Our approach centers on novel two-path prediction strategy enabled by multi-view fusion. The geometry+appperrnne path predicts mesh structure and spatially varying BRDF parameters from unified triplane features, while the illumination path estimates HDR environment maps via our multi-view RENI+ extension. Both paths are driven by shared cross-conditioning transformer that fuses arbitrary numbers of input views, creating consistent feature representations that enable robust material-lighting disentanglement.
4.1.1 CROSS-VIEN FEATURE FUSION
Let the input be set of N masked images with cameras {L,, M,, C.)) We first form per-view tokens with DINOv2 and camera modulation:
Ti DINOv2(I, M.), e, fam
Toomd
T ; ei]
We designate one view as the hero view h and its tokens are concatenated to the learned triplane token bank T and drive the query stream of the transformer:
Qo T T
(4)
The hero view serves the query stream for cross- conditiondi and is selected uniformly random during training and evaluation, ensuring robust performance independent of viewpoint choice.
To make cross-view context compact yet expressive, we employ latent mixing. A bank learnable latent tokens Lg E IR LxD is mixed with the projected cross-view tokens (all non-hero views) to form a memory M that the query stream will attend
H, = Ps (
Veress:
(5)
L SelfAtt((ayerNoom(LL.))
(6)
M Interleave (L, TokenConcat( (H.))
Here Pt projects tokens to the latent dimensionality D, and Interleave denotes the two-stream in- terleaved transformer, which alternates blocks that (i) update Q with cross-attention to M and (ii)
refine M via httl//croos-aatentionn The main transformer thus computes:
T TwoStream Qo. M).
(8)
which yields triplane-conditioned features that are consistent across an arbitrary number of input views while preserving dedicated hero view pathway for stable geometry/appearance alignment. In implementation, we use pixel-shuffle upsampling to obtain higher-resolution triplanes from raw predictions.
4.1.2 SPATIALLY VARYING MATERIAL PREDICTION
Our snomeery+appearante path operates on the unified triplane representation to predict spatially varying material properties and mesh structure. The transformer output tokens Tout are directly interpreted as triplane pixels, forming our unified 3D representation T E TR3x 43331x333388 For any 3D point p. we extract features via triplane projection as established Equation (2)
Crucially, we predict all material and geometric properties from this single shared triplane embed- ding using task-specific MLP heads:
MLPrough, a. P. m. Dhump) } (p) (MI.P density, MI.P MLPalbeddy MLP rough; MLP metal, MLP normall (f(p))
Pabbeda
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where a is density, is albedo, is roughness, is metallic, and Duump represents normal perturba- tions. This unified approach eliminates the need for separate material tokens and enables complex multi-material object support.
Geometry is extracted using Flexicubes (Shen et al. 2023) for superior mesh quality, and the result- ing mesh is textured with spatially varying PBR parameters via fast UV unwrapping.
4.1.3 -IEW ENVIRONMENT ESTIMATION
We introduce novel multi-view illumination inference approach that fundamentally differs from existing methods. While prior work typically predicts environment maps using simple MLPs from triplane features or single-view observations, we present the first method to leverage multi-view reasoning with adaptive background masking robust environment estimation.
Our illumination path operates in parallel to the geometry reconstruction, enabling dual-mode op- cration where our method can robustly recover HDR environments from either direct background observations or indirect material reflectance cues across multiple viewpoints. We utilize REN++ as an efficient illumination representation, however this approach could be easily extended to other lighting representations.
We encode mask image pairs (M,, Ie) via a trainable DINOv2-small with two extra input channels to obtain mask-aware tokens
T = fmukk (M,, L.),
I.
(10)
These tokens are concatenated with the object-transformer outputs to form the environment context
Tew-etx concat concat((TTqua) ((TTusk) N
T
(11)
dedicated ID transformer maps learned environment tokens to a RENI+ latent and global rotation (6D) via cross-attention:
Zeney FED] Env Baw/Trasfforme// Tenk-bunk Tenwect). Zenv ER
FED ER
(12)
where K xxd d matches the RENI+ latent grid dimensionality. The final HDR environment is decoded
as established in Equation |I.
Critically, training employs stochastic background masking, randomly occluding background pixels subset of views during training. This forces the network to solve two complementary tasks: when background pixels are visible, it can read lighting directly from the environment; when they are masked, it must infer lighting from indirect cues in object reflections and shading. This dual mode training enables robust illumination inference in real-world scenes where backgrounds are often partially cropped, saturated, or noisy.
4.2 DISENTANGLED TRAINING VIA MC+MIS
Our differentiable physically based Monte Carlo (MC) renderer with Multiple Importance Sampling (MIS) ties both reconstruction paths together, enforcing physically meaningful material-illuminatoon disentanglement while enabling mixed-domain training. We found that utilizing VNDF sam- pling (Heitz 2018) with spherical caps (Dupuy & Benyoub 2023) and antithetic sampling (Zhang et al. 202 la helps stabilize the training. This MC+MIS approach enables the following capabilities:
Physical disentanglementt The renderer enforces that predicted materials f, and illumi- nation Len must jointly explain observed images through physically based light transport.
Mixed supervision: When PBR ground truth exists, we additionally use direct material su- pervision; otherwise, the renderer ensures material and lighting consistency purely through image reconstruction.
Domain bridging:
This allows seamless training across synthetic PBR data, synthetic RGB-only renders, and most importantly real-world captures, dramatically improving gen- eralization and robustness.
The result is the first system capable of learning spatially varying material reconstruction from mixed-domain data without supervision collapse, enabling robust performance on real-world inputs while maintaining physical plausibility.

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Published as a conference paper at ICLR 2026
Inpit Itages
Basncolor
Roughnees
Metailic
Normal (Wordd)
Reconemaction
Bellt Vitu
Rell
Figure 3: PBR & Relighting Results. We show that our spatially varying PBR prediction is faithful to the ground truth and therefore produces highly detailed and realistic relightings.
5 EXPERIMENTS
We evaluate ReLi3D across three dimensions that validate central thesis: multi-view straints enable superior material and lighting disentanglement for fast, production ready 3D asset creation. Our experiments demonstrate that while achieve competitive geometry reconstruction at interactive speeds, our primary contribution lies in illumination disentanglement, delivering spa- tially varying PBR materials and coherent HDR environments that enable high-fidelity relighting.
5.1 IMPLEMENTATION AND EVALUATION SETUP
We train on 174k objects total: 42k synthetic PBR (full material supervision), 70k synthetic RGB- only, and 62k real-world captures from UCO3D (Liu al. 2024). For evaluation, we test on
out-of-distribution datasets including Google Scanned Objects (GSO) (Downs et al.) 2022). Poly-
haven (Haven 2024) objects rendered with HDRI-Skies (IHRRI 2024), Stanford ORB Kuang
iet al. (024).. and challenging real-world UCO3D captures with motion blur and imperfect masks. We compare against recent feed-forward and generative methods: SF3D (Boss et al. SPAR3D (Huang et al., 2025), 3DToppa-XL. (Chen et al.) (2024), and Hunyuan3I (Zhao et al. All experiments run on single H100 GPU, including mesh extraction and texture baking. To en- sure fair comparison, we apply rigid ICP alignment to ground truth meshes before evaluating image metrics, as baselines often produce meshes in arbitrary canonical spaces. ReLi3D predictions naturally aligned, highlighting useful feature for practical applications. For more details, please refer the appendix Appendix
2024
2025
5.2 MATERIAL- LIGHTING DISENTANGLEMENT OUR CORE CONTRIBUTION
While overall 3D reconstruction is important, we are particularly interested in the quality of material estimation and illumination disentanglement.

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with DMA wi
Figure 4: Illumination Comparison. (Left) Single view, illumination prediction results compared to ground truth and SPAR3D, which also predicts RENI+ latents, indicating our severely improved method. (Right) Influence of increasing numbers of views and background information. Notice how well can predict the illumination in the top rows with background information locate light sources correctly, whereas the bottom row more spread out as it is inferred from diffuse surface reflections only.
Spatially Varying Material Prediction. For PBR results in Figure 3 and Table [. we demonstrate that ReLi3D predicts fully spatially varying PBR materials that improve significantly with additional views (e.g., where the base of the bed is corrected in Figure 5). Our method ranks first across all material metrics: albedo reconstruction achieves 25.00 dB PSNR SF3D's 18.42 dB), roughness reaches 22.69 dB PSNR, and metallic prediction achieves 32.73 dB. Multi-view input further en- hances these results, demonstrating that cress-view constraints successfully resolve material-lighting ambiguities.
Relighting Performance. The ultimate test of material- lighttinlliiii disentanglement is relighting under novel environments. For quantitative relighting evaluation, rendered each reconstruction in novel out-of-distribution HDR environment. Even when competing methods receive ground-truth environment maps as input, ReLi3D ranks first across all relighting metrics in Table Visually, Fig- shows that our material estimation so accurate that the relit reconstructions closely resemble the ground truth, confirming that material decomposition generalizes well to novel lighting ditions.
Environment Estimation. Figure compares predicted HDR environment maps with ground truth. Even a single view suffices to recover the correct sky color and sun direction. We also show how background information helps recover correct light sources, and utilizing multiple views helps recover correct light directions, even in dark environments. In contrast, SPAR3D often predicts over-smoothed, low-contrast maps with no clear light sources.
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0.815
0.133
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0.531
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0.137
36.37
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0.836
0.154
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0.325
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30.44
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21.17
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Table Relighting & Image & PBR Metrics Comparison. (Left) Relighting performance. (Mid- dle) Image reconstruction performance. (Right) PBR material reconstruction performance. While most methods produce only global PBR parameters, ours produce spatially varying material maps which increase in quality with more views.
5.3 OVERALL RECONSTRUCTION QUALITY
While geometry reconstruction is not our primary focus, ReLi3D achieves competitive results at un- precedented speed. Our model achieves quantitative and qualitative state-of saate-of-hhe-r single-vier view re- construction results out of distribution synthetic (GSO, Stanford ORB) and real-world (UCO3D) data in Table the multi-view setting, ReLi3D permorms well on geometric and outperforms on all image metrics while running in avg. 0.31s. Supplying just four views improves CD by 27% and
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pushes the F-score @0.5 to 0.993, showeasing the effectiveness of our multi vielw eross- coos-conditioning at virtually unchanged cost. Performance saturation beyond 4-8 views stems from coverage satura- tion: once surface coverage is sufficient, additional random views often provide redundant informa- tion rather than new constraints, leading to marginal gains.
Figure 6 offers end-to-end comparison across all datasets and methods. Competing techniques frequently fail or output planar artifacts, while our multi-view fusion reconstructs complete assets, including hidden backsides, with better ground truth lighting and shadowing. For real-world cap- tures, ReLi3D remains robust, and our method improves with multi-view input while others do not (e.g., the face of the teddy bear in Figure
We acknowledge that specialized high-resolution diffusion methods may achieve superior geometric detail through longer optimization. However, our contribution lies in the speed-quality trade-off for material-awan reconstruction: we deliver complete, relightable assets in under second while running 100x faster than generative approaches like Hunyuan3D.
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0.312
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0.250
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0.723
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38.09
0,900
0.251
0.139
0.355
0.13
0.752
0.273
BaL330 (Oung)
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0.32
0.511
19:37
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0.200
0.243
0.309
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0,839
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0.38
0,081
0.787
0.526
0.593
21.45
0.894
0.080
0.185
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0,088
Table 3D and Image Metries. ReLi3D clearly achieves SOTA single and sparse multi-view re- construction while also achieving great speeds. It is worth noting that that TripoSG and Hunyuan3D also produce signficantly higher vertex counts (100k+ vs 4.5k for ours).
5.4 CROSS-DOMAIN TRAINING EFFICIENCY
Our mixed-domain training protocol enables robust real-world performance Figure F despite training on only 174k objects 10-50x less data than recent large-scale methods. The key insight is that multi- view constraints provide stronger supervision signals than massive single-view datasets, enabling efficient learning of material-lighting disentanglement.
We evaluate on real-world Stanford ORB dataset (Kuang et al. 2024) to demonstrate generalization (Table 3). ReLi3D outperforms all baselines across 3D reconstruction, image quality, and material prediction metrics. Multi-view input further improves performance.
Stanford ORB
3D
Image
Basecolor
Method
CD1
FS
FS80.21
FS@0.51
PSNRT
SSIMt
LPIPS]
PSNR1
SSIM
SF3D
0.152
0.512
0.769
0.954
17.75
0.891
0.111
18.52
0.865
SPAR3D
0.165
0.488
0.751
0.940
17.10
0.886
0.113
17.80
0.857
Trellis
0.152
0.561
0.782
0.948
17.13
0.888
0.112
Hunyuan3D
0.141
0.571
0.801
0.960
16.96
0.877
0.110
21.37
0.872
ReLi3D (Ours)
0.116
0.608
0,856
0.980
18.68
0.907
0.098
24.21
0.891
Hunyuan3D (2 Views)
0.134
0.588
0.809
0.967
16.91
0.876
0.108
21.42
0.872
Hunywan3D (4 Views)
0.136
0.579
0.810
0.966
16.83
0.877
0.108
21.46
0.873
ReLi3D (Ours) (2 Views)
0.104
0.654
0.888
0.986
19.74
0.913
0.089
25.01
0.896
ReLi3D (Ours)
Views)
0.094
0.718
0.906
0.082
25.33
0.900
ReLi3D (Ours) (8 Views)
0.089
0.745
0.914
0.991
21.21
0.921
0.080
25.50
0.901
ReLi3D (Ours) (16 Views)
0.089
0.749
0.914
0.990
21.29
0.921
0,080
25,58
0.902
Table 3: Real-world Evaluation on Stanford ORB. Quantitative evaluation on Stanford ORB dataset showing 3D reconstruction, image quality, and basecolor material prediction performance. Our method outperforms baselines across all metrics and improves with more input views.

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5.5 LIMITATIONS
Although rare, failure cases occur where the decomposition fails to disentangle lighting and mate- rials, resulting in baked-in lighting affecting the material maps. This seems occur when environ- ment lighting is not in domain for the RENI++ prior, most notably when multiple very strong light sources are present.
The largest remaining weakness is the relatively limited resolution of the triplane, limiting texture and geometry resolution in practice, also visible reconstruction examples against Hunyuan3D. While we do not claim to have the best geometry prediction, other methods spend more time with high-quality diffusion processes, we are confident that our illumination disentanglement structure is contribution that, with sufficient resources, could help larger methods.
6 CONCLUSION
We have enhanced the fundamental challenge of illumination disentanglement in feed-forward 3D reconstruction, enabling the first method jointly predict -varyig PBR materials and coherent HDR environments from sparse image inputs. Through our novel two-path architecture and differentiable Monte Carlo training, we demonstrate that proper material- lathrial-lighting separation achievable at interactive speeds, delivering production-quality relightable assets in under one sec- ond.
This development in illumination disentanglement opens exciting avenues for future research and applications. The ability to rapidly generate physically accurate 3D assets from casual captures could transform content creation workflows, enabling real-time asset digitization. More broadly, our disentanglement framework could extend beyond reconstruction to enable in-the-wild material understanding; imagine training objects captured under varying real-world illumination to learn material priors that generalize across lighting conditions.
We release all code, pretrained weights, and dataset generation scripts to accelerate adoption and enable the community to build upon this foundation for the next generation of 3D-aware vision systems.
ACKNOWLEDGEMENTS
The authors thank Stability AI for hosting Jan-Niklas Dihlmann as intern during this work. This work was funded by the Deutsche Forschungsgemmiinscaaft (DFG, German Research Foundation) under Germany's 's Excellence Strategy-EXC number work was supported by the German Research Foundation (DFG): SFB 1233, Robust Vision: In- ference Principles and Neural Mechanisms, TP 02, project number: 276693517. This work was supported by the Tübingen AI Center. The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting Jan-Niklas Dihlmann.
Project number 390727645. This
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APPENDIX
This appendix provides technical details and additional experimental validation for our multi-view illumination disentanglement approach. We organize the material as follows: Appendix [ presents extended experimental results including detailed PBR comparisons, real-world and synthetic recon- struction examples, and an ablation study that validate our architectural choices. Appendix offers implementation specifies including loss formulations for mixed-domain training, the progressive training protocol that bridges volumetric and mesh-based rendering. Finally, Appendix [ details curated training data composition, covering both synthetic dataset construction with full PBR supervision and the extensive preprocessing pipeline required to integrate challenging real-world UCO3D captures for robust domain generalization.
A FURTHER EXPERIMENTS
This section extends our experimental validation of our multi-view illumination disentanglement approach, including detailed visual analysis of PBR decomposition quality, comprehensive recon- struction comparisons across synthetic and real-world datasets, and critical ablation studies that validate our architectural design choices.
A.1 COMPARISON
Figure demonstrates the superior quality our spatially varying material predictions compared to existing methods. Unlike previous approaches that predict global material properties fail to achieve proper material-lighting separation, our method produces detailed albedo, roughness, and metallic maps that exhibit realistic spatial variation. Particularly noteworthy method's ability to handle mixed-material objects.
Figure 5: PBR Decomposition Results. Our method capable of producing highly detailed tex- tures and geometries even from a single view. It is also the only method capable of reproducing accurate spatially varying PBR parameters, which are essential for relighting.
Our method's generalization capabilities extensively validated across diverse synthetic and real- world scenarios. validation real-world captures, where imperfect masks, camera estimation errors, and challenging lighting conditions test the robustness of our approach.
reconstruction quality on synthetic objects. Figure provides
Real-world Material Prediction We demonstrate real-world performance on challenging UCO3D captures with motion blur and cluttered backgrounds (Figure g) These examples show the benefit of our multi-view setting (e.g.. recovering the front of objects given additional views) and improved material prediction as lighting aligns with ground truth. Our method successfully
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Groudd Trzhh
Oue' 12 vove
Our view
Horpan sowi
Hanpard2- (tre
Spard
1
l
i
f
Figure 6: Reconstruction Results (Synthetie). Our method performs well across synthetic data and shows accurate reconstructions from single view. Other methods show collaps with bend or predictions.
Ground
Our 12
30m
SF3D
Figure 7: Reconstruction Results (Real World), Our method produces accurate reconstructions for real-world data, although challenging. Incorporating multiple views improves the performance further by clearing up uncertainties in unseen areas.
separates metallic and non-metallic materials even in challenging real-world settings with strong reflections and blur.
Complex Multi-material Objects We evaluate on complex, multi-material objects from the Blender Shiny dataset (Figure ( demonstrating that our spatially varying PBR prediction gener- alizes to complex geometries and real materials. The figure shows predicted basecolor, roughness, metallic, and normal maps, along with relit renderings in novel environments, confirming robust material decomposition across diverse object types.
Illumination Disentanglement Quality Figure 10 provides detailed qualitative comparison of illumination prediction results between DiffusionLight, SPAR3D, and our method (ReLi3D). While DiffusionLight hallucinates completely different environments (c.g., predicting indoor scenes for outdoor inputs), and SPAR3D fails to recover meaningful illumination, ReLi3D accurately mimics the ground truth shape and color of the environment maps. This demonstrates the effectiveness of dedicated illumination branch and multi-view reasoning for robust environment estimation.
Quantitative Evaluation of Illumination Disentanglement Table 4 provides quantitative results comparing ReLi3D, SPAR3D, and DiffusionLLggh Phongthawee et al. (2023) baseline on the Polyhaven+HDRI dataset. ReLi3D achieves comparable relighting PSNR DiffusionLight (20.88 vs 20.93 dB) while being significantly faster (0.34s vs 21, 21.46s) and supporting multi-view input. SPAR3D achieves similar speed but significantly lower quality (17.10 dB PSNR), confirming the importance of our dedicated illumination branch architecture.
A.2 ABLATION
Table 5 shows validation of our key architectural choices, with particular emphasis the critical role of Monte Carlo rendering in achieving high-quality material-lightnng disentanglement. The ablation reveals that removing the Monte Carlo renderer (- MC-Render) significantly degrades image
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Ppst Irages
Dxwwcoter
Rougimns
Metilfc
Nomal (Wirk)
Potk Viow
View
Prodecte
Freskdton
Predection
Figure 8: Real-worke material prediction. Material maps (albedo, roughness, metallic, normal) for real-world objects from UCO3D dataset very challenging settings, strong reflections and blur. Our method still able to make a rough prediction and faithfully separates metallic and non- metallic materials.
Inpat Images
Desecaior
Roughnass
Meislic
Narmal (Word)
Reconemuction
Bel Viee
Pet Vee
Figure 9: Varying materials and complex objects. Results on the Blender Shiny dataset showing spatially varying PBR material prediction on complex multi-material objects. The figure shows predicted basecolor, roughness, metallic, and normal maps, along with relit renderings in novel environments.
reconstruction quality (19.92 17.54 dB PSNR). This finding underscores crucial insight: the Monte Carlo renderer with Multiple Importance Sampling is not merely an optimization detail but fundamental component that enables proper physical disentanglement.
Training Stage Contributions Our progressive training pipeline transitions from volumetric ren- dering through spherical Gaussian approximation stages (128 -> 256 - 512 Gaussians) full Monte Carlo integration, as detailed in Appendix [B2 Tebles the share of the total improve- ment (Phase + Full MC) contributed by each intermediate stage. The Gaussian stages with larger batch sizes explain 70-80% of the 3D coverage gains (CD/FS), confirming they mainly stabilize geometry before expensive rendering. The Monte Carlo stage accounts for the majority of remain- ing improvements in material disentanglement (basecolor, roughness, metallic). The 512-Gaa Gaussian stage provides the sweet spot for -runteey- while the final MC finetuning sharpens material maps and relighting without regressing 3DD accuracy.
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Input
Ground Truth
Ours
DiffusionL.ght
Spar3D
Figure 10: Illumination Comparison. Comparison of illumination prediction results between Dif- figh, SPAR3D, and our method (ReLi3D). Predicted environmens vary vastly while ours mimics the ground truth shape and color, DiffusionLight hallucinates a completely different envi- ronment, SPAR3D fails.
Time (s)
PSNR1
Difffusion Light
ReLi3D
SPAR3D
Diffussin Light ReLi3D
SPAR3D
21.46
0.34
0.33
20.93
20.88
17.10
Table 4:
Quantitative evaluation of illumination disentanglement. Comparison of environment map prediction and relighting quality on Polyhaven+HDRI dataset.
3D
Image
Method
CDJ
FS@0.11
FS@0.21
FS@0.51
PSNR1
ReLi3D
0.110
0.676
0.870
0.975
19.92
MCC-ender
0.114
0.668
0.865
0.971
17.54
Table 5: Ablation study. Impact of removing the differentiable Monte-Carlo renderer (- MC- Render).
B IMPLEMENTATION DETAILS
This section provides comprehensive implementation details for our multi-view illumination dis- entanglement architecture, including loss formulations, training protocols, architectural design choices.
B. Loss FUNCTIONS
Our training objective combines physically-based image reconstruction with material and illumi- nation supervision, designed to handle mixed-doman datasets with varying levels of ground truth availability.
Image Reconstruction Loss The primary training signal compares rendered reconstructions against ground truth images not used as input:
Ling 10.00MsEam 2.0CLPIPS.m
(13)
This combination ensures both pixel-level accuracy and perceptual quality.
Geometry and Mask Supervision During volumetric training stages, employ mask binary cross-entropy loss 10.00mus for foreground segmentation. Geometry losses Cgemm follow the Flexi- cubes implementation and weighting scheme for robust mesh extraction.
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Coverage
Image Quality Share (1)
Share
Roughness (38)
Phase (256) vs Phase (122))
20.2
22.6
6.3
Pluse 3 (512) vs Phase 2 (256)
70.3
50.0
23.9
31,3
41.0
MC (Full) Phase (512)
43.2
53.5
62.4
51.3
Table 6: Training stage contribution analysis. Average share of the total improvement from Phase (128 Gaussians) to the full Monte Carlo stage that is attributable each intermediate stage.
Columns aggregate the metrics shown in Table I: (1) 3D coverage (CD and FS FS@0.05-0.5), (2) im- age quality (PSNR, SSIM, LPIPS). (3) basecolor (PSNR, SSIM. LSSIMSE), (4) roughness (PSNR, SSIM, RMSE), and (5) metallic (PSNR, SSIM, RMSE). Early Gaussian stages mainly expand 3D coverage, while the Monte Carlo refinement sharpens PBR material disentanglement.
Material Property Supervision Given the mixed nature of our training data, material supervision adapts to ground truth availability:
Las = 10.0CMSEPPRR + 4.0C 0.055
(14)
where basecolor, roughness, and metallic parameters use MSE supervision when available, surface normals employ cosine similarity loss, and bump maps regularized toward flatness using local normal direction Dup (0, 1)
Environment Supervision
Direct RENI+ latent supervision provides illumination guidance:
Lew 0. LMSE.RENT 0.02
(15)
When RENI+ ground truth is unavailable, demodulation regularization biases the environment toward neutral white lighting.
B.2 TRAINING PROTOCOL
Our multi-stage training protocol progressively transitions from volumetric mesh-based render- ing, culminating in full Monte Carlo integration.
Multi-stage Rendering Pipeline
We execute three distinct training phases:
1. Volumetric rendering of the implicit field using NeRFAcc for initial shape learning
2. Mesh rendering with spherical Gaussian approximation, progressively increasing im- age resolution (128- 256 + 512) for efficient lighting approximation
3. Full Monte Carlo integration with VNDF sampling, spherical caps, and antithetic sam- pling for physically accurate shading
Each stage spans 60,000 training steps. This progressive approach ensures stable convergence while gradually increasing rendering fidelity.
Stage-specific Losses and Training All stages employ the same loss formulation combining im- age reconstruction (Ling), material supervision (Lmm)), geometry regularization (Cgeom), and envi- ronment supervision (Cew) as detailed in Appendix B.1 Stages 1-3 use spherical Gaussian approx- imation for lighting, while stage 4 employs full Monte Carlo integration. All network components remain trainable throughout all stages no modules are frozen. The background module is excluded from weight loading when transitioning between stages to allow adaptation to new rendering con- figurations.
Training Configuration We utilize 512 512 input resolution and randomly sample 1-4 con- ditioning views training iteration. The entire pipeline trains end-to-end with learning rate of x 10 Batch sizes adapt to computational demands: during volumetric rendering, 192 during spherical Gaussian stages, and 32 during Monte Carlo integration.
B.3 ARCHITECTURAL DESIGN CHOICES
Hero View Selection and Sensitivity The hero view serves as the query stream for the cross- conditioning transformer, providing stable reference for geometry and appearance alignment. In
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reported metrics (Tables 1 and 2), the hero view is selected uniformly at random, ensuring our results reflect robust performance independent of viewpoint choice, unlike methods relying on canonical frootal views. To test sensitivity, compared random selection against fixed frontal-view selection (Table Results show only marginal differences, with slight perceptual gains for random views likely due parallax information from side views.
Polyhaven Subset
3D
Image
Method
CDJ
FS
FS@0.21
FS@0.51
PSNR1
SSIM
LPIPS!
ReLi3D (Random Hero)
0.102
0.697
0.883
0.982
20.25
0.919
0.083
ReLi3D (Frontal Hero) 0.123
0.641
0.840
0.965
19.06
0.909
0.095
Table 7: Hero view selection sensitivity. Comparison of metrics using random hero view selection versus always selecting the most frontal view on the Polyhaven dataset.
Illumination Prior and Alternative Representations Our framework compatible with alterna- tive lighting representations: we use spherical Gaussian approximations in the intermediate training stage (Appendix (B.)) before switching to Monte Carlo rendering with RENI+ envmaps. In those stages, we train with low frequency Gaussian representation and observe that it fails capture sharp highlights and directional suns, leading to worse relighting metrics as shown Table 3.
RENI++ provides a compact but high-frequency representation critical for photorealistic relighting and accurate material and lighting separation. While nothing in our architecture prevents using SH or Gaussians, found RENI+ to be the best trade-off between expressiveness and efficiency. We choose this compact representation to our memory limitations; expanding into a more memory in- tensive representation (e.g., ENV Map HDR prediction) would not be possible with our constraints.
C DATASETS
Our training leverages carefully curated mix of synthetic and real-world data achieve robust gen- eralization while maintaining physical plausibility. This mixed-domain approach enables learning from both controlled synthetic environments with full material supervision and challenging real- world captures that provide crucial domain adaptation.
C.I SYNTHETIC DATA COMPOSITION
Following established protocols while extending coverage, we combine multiple synthetic datasets maximize training diversity. Our synthetic corpus extends the TripoSR dataset protocol with Amazon Berkeley Objects (ABO) Collins et al. (2022) and ARIA Pan et al. (2023), providing com- prehensive material and geometric variation.
AABO)CCollins
Rendering Protocol Each object is rendered under three distinct illumination environments, domly rotated around the vertical axis to prevent lighting bias. Camera focal lengths are sampled from scaled normal distribution between 22" and 37 to match real-world capture conditions. Objects are normalized to unit scale and centered, with cameras positioned to fill the frame with appropriate padding, followed by slight positional augmentation.
We render significantly more views for objects with PBR ground truth (100 images) compared to RGB-only objects (30 images), providing richer supervision where material information is available. This asymmetric sampling strategy maximizes learning efficiency while accommodating varying supervision levels.
Illumination Environments Our synthetic rendering employs 1000 HDRI environments sourced from iHDRI IHDRI (2024) and Polyhaven Haven (2024) datasets. These environments are pre- processed to extract RENI+ latent codes, enabling direct illumination supervision during training. This diverse illumination set ensures robust material- hithtial-lighting disentanglement across varied lighting conditions.
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Input
Rendering
Basecolor
Views
Ground Truth
Ours
Hunyuan
Ground Truth
Ours
Hunyuan 2
Figure 11: Failure cases. Failure cases showing challenges with baked in lighting for objects with strong self shadowing (fin shark) and basecolor prediction difficulties dark scenes (Rhino). Comparison includes results from Hunyuan3D and our method.
C.2 REAL-WORLD DATA PREPARATION
The unCommon Objects in 3D (UCO3D) Liu et al. (2024) dataset provides real-world training data, requires extensive preprocessing to achieve training compatibility with our synthetic data pipeline.
Quality Filtering UCO3D contains numerous challenging samples including motion blur, inaceu- rate masks, and poor camera estimates. We apply strict quality filtering based reconstruction and camera estimation scores provided by the dataset's Gaussian Splatting optimization, retaining only objects with scores 1.0. This filtering dramatically reduces the dataset size but ensures training stability and prevents degraded supervision signals.
Data Preprocessing Pipeline Our preprocessing pipeline, illustrated in, applies several critical transformations:
1. Square cropping and centering: Objects consistently cropped to square aspect ratios and centered within frames
2. Intrinsic calibration: Camera intrinsics are carefully adjusted account for cropping transformations
3. Valid region tracking: Due to square cropping, we maintain masks for valid view regions and foreground objects
4. Surface normal estimation: Monocular normal estimation provides additional geometric supervision
5. Scale normalization:
Scene boundaries are rescaled align with synthetic example scales
This comprehensive preprocessing ensures seamless integration with synthetic training data while preserving the challenging real-world characteristies that drive domain generalization.
Training Integration The processed UCO3D data provides RGB-only supervision without mate-
rial or illumination ground truth. Our mixed-domain training protocol accommodates this through
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image-space reconstruction losses while synthetic data provides direct material supervision. This combination enables robust real-world generalization while maintaining physical material proper- ties learned from synthetic supervision.
D LIMITATIONS AND FAILURE CASES
Although rare, failure cases occur where the decomposition fails to disentangle lighting and mate- rials, resulting in baked-in lighting affecting the material maps (Figure T1.. This primarily occurs when (i) environment lighting falls outside the RENI+ prior distribution, especially with multiple extremely bright, localized light sources, or (ii) strong self-shadowing leads to baked-in lighting in material maps, (iii) dark scenes make basecolor prediction challenging. However, even these challenging cases, ReLi3D still outperforms strong baselines like Hunyuan3D Zhhoo et al. (2025)
The largest remaining weakness is the relatively limited resolution of the triplane x3 40 x 384 x 3844, limiting texture and geometry resolution in practice, also visible in reconstruction examples against Hunyuan3I.. Current blur results primarily stem from this resolution constraint and the DINOv2 fine-tuning bottleneck, not the disentanglement framework itself.
Transparent objects present another limitation: while our density- based NeRF pre-training handles transparency, explicit mesh reconstruction of transparent surfaces remains an open research chal- lenge outside our current scope.
ReLi3D assumes known camera poses and physically plausible materials, which are often violated by generated images from diffusion models. While single-image inputs generally work well when pose estimation is accurate (e.g., from DUST3R Wang et al. (2024a)), severely bad pose estima- tion leads to blur artifacts. Multi-view generations sometimes degrade performance due to pose and appearance inconsistencies, though pairing generated multi-vies images with proxy 3D reconstruc- tions could enable adaptation to this regime future work.
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Reviews:
[{"review_id": "xufdgJy7as", "reviewer": "Reviewer_7UYe", "summary": "ReLi3D presents a feed-forward framework that reconstructs 3D geometry, spatially varying PBR materials, and HDR illumination from sparse multi-view images. It employs a two-path transformer architecture for joint material–lighting disentanglement, guided by a differentiable Monte Carlo renderer. Trained on mixed synthetic and real data, the model achieves fast (<1 s) and high-quality relightable 3D reconstruction.", "questions": "1. How is the hero view (line 231) chosen in multi-view fusion? Is it random or learned, and how sensitive is the model to this choice?\n\n2. Have the authors tested failure cases such as baked-in lighting or specular highlight misinterpretation? Could you show such examples or briefly discuss possible solutions and limitations?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The two-path feed-forward framework jointly reconstructs geometry, materials, and illumination under multi-view constraints, showing a clear and coherent design.\n\n2. Uses Monte Carlo integration with MIS for training supervision, leading to more consistent and realistic reconstructions.\n\n3. The model runs efficiently and shows some degree of cross-domain generalization with mixed synthetic–real training.", "weaknesses": "1. Limited evaluation diversity. The test data mostly covers diffuse or moderately lit objects. The paper lacks challenging cases such as metallic, transparent materials, or strong HDR illumination, where disentanglement performance would be most critical.\n\n2. Lack of illumination disentanglement evaluation. The paper does not provide quantitative evaluation of the predicted lighting quality (e.g., comparison against SPAR3D or DiffusionLight) or at least sufficient qualitative examples demonstrating the accuracy of recovered illumination.\n\n3. Potential dataset-specific bias. Since the real-world training data (UCO3D) includes only RGB supervision, the model might have learned dataset-specific entangled appearance cues rather than true material–lighting disentanglement on real data. More real-scene examples and an ablation showing the individual contribution of synthetic vs. real data would strengthen the claim of cross-domain generalization.\n\n4. Missing ablation for cross-view fusion. Cross-view fusion is one of the key claimed contributions, but there is no explicit ablation showing how it improves view consistency compared to models without this fusion module.", "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": "OvAWs7schd", "reviewer": "Reviewer_BeZw", "summary": "ReLi3D is the first unified end-to-end system that reconstructs complete 3D geometry, spatially-varying materials, and environment illumination from sparse multi-view images in under one second. By leveraging a transformer-based multi-view fusion and dual-path prediction architecture with differentiable rendering, it achieves fast, generalizable, and high-quality relightable 3D asset reconstruction.", "questions": "1. Regarding mesh reconstruction alignment:\n\nBased on my understanding, the meshes reconstructed by Huanyuan3D are represented in their own coordinate systems. Consequently, it may not be straightforward to obtain a direct correspondence with the input view — that is, the pose in which the input RGB view can be rendered. Under such a situation, it is unclear how the authors performed the image-based metric comparison in Table 2. The authors should clarify how they ensured pose alignment or view consistency before evaluating these metrics.\n\n2. Regarding the training protocol (Appendix B.2):\n\nAccording to Section B.2, the training process is divided into several stages. However, the current version of the paper does not clearly explain the details of each stage — for instance, which losses are used in each stage, which network components are trained or frozen. These details are crucial for reproducibility but are currently missing from both the main paper and the supplementary material. The authors should elaborate on these aspects and consider including a concise description of each training stage (objectives, losses, and training parts) in the main paper to facilitate re-implementation by readers.\n\n3. Regarding ablation studies:\n\nThe current version lacks sufficient ablation experiments. At present, only a single experiment in the supplementary material ablates the MC-Render component. A more comprehensive ablation study is needed to strengthen the paper. Specifically, the authors are encouraged to include:\n\nPerformance comparison after each training stage;\n\nAblation of the main architectural modules;\n\nAnalysis of how performance changes if geometry&materials and environment illumination are predicted using two independent networks rather than a unified one.\n\nIf these necessary ablation results can be provided in the rebuttal, I would be inclined to reconsider and potentially raise my rating.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The writing is clear and easy to follow.\n2.The proposed pipeline is new and makes a meaningful contribution to the field.", "weaknesses": "please see weakness for detail", "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": "4n2DU8dslS", "reviewer": "Reviewer_FiCS", "summary": "The paper introduces a method to reconstruct 3D assets with PBR materials + HDR envmaps from posed 2D images.\n\nMain components of the method include:\n- Efficient fusion of input views with cross-view feature fusion\n- spatially-varying material prediction - a common approach to predict materials with MLP, given triplane features (inspired by LGM)\n- separate branches for svBRDF (mesh + PBR) and HDR prediction\n- Training on mix of gt PBR materials and envmaps (where available) and in \"self-supervised\" manner with MC renderer and 2D supervision", "questions": "1. \"Although rare, failure cases occur where the decomposition fails to disentangle lighting and materials, resulting in baked-in lighting affecting the material maps.\" - please provide visuals with failure cases\n2. See weakness 1 - please provide ablation on using multiple paths vs single path.\n3. Please clarify how the hero view is selected.\n4. L292 \"Critically, out training employs... background masking\" - please provide more details on this approach. Theoretically, this should work similarly to taking masked / not masked render with some probability, not sure why custom occlusion logic is needed here.\n5. See weakness 2 - although it's not critical please discuss or show at least a few 3D samples generated from images from diffusion / flow models.\n6. See weakness 3 - please consider improving the visuals in Table 2. to better evaluate the quality of this method.\n7. On choice of HDR prior (RENI++) - do you think the same framework could plug in alternative HDR priors (spherical harmonics / gaussians), did you experiment with any?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "To my knowledge, the suggested approach is the first which jointly reconstructs mesh, PBR, **and** environment (HDR), all in a feedforward manner and at impressive speeds. To me, this constitutes a significant contribution.\nAdditionally, the paper contains novel ideas (more on that below) and is clear and well-written.\n\n1. The idea of fusing arbitrary number of views with one \"hero\" view and other views with latent mixing (what the authors call \"cross-view feature fusion\") is novel and insightful.\n2. Authors' choice of decoding svBRDF with an MLP -> Flexicubes from triplane features is very common in industry, which I think is a well-suited and efficient selection for 3D representation.\n3. Both illumination reconstruction (Fig 4), PBR & relighting metrics (Table 1.) look convincing.", "weaknesses": "1. The most important ablation is missing on whether to use multiple paths (one for geometry & appearance, another for illumination path) or compute them all in a single path. \n2. While the method was trained and inferred either on real-world or synthetic data, it would be valuable to see how it generalizes to generated (e.g. with diffusion / flow models) images. This might improve practicality of this approach.\n3. While 3D+Image metrics (Table 2) look convincing at first, qualitative results in Fig. 6 are hard to inspect.", "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": "yKj3j2QfWV", "reviewer": "Reviewer_7ZmD", "summary": "ReLi3D is a feed-forward framework that takes a set of variably posed images as input and outputs a textured 3D mesh with PBR materials and an environment map. The model adopts a two-path architecture that separately learns geometry–material and environment–lighting representations, which are then fused through cross-attention layers. A differentiable renderer jointly renders outputs from both paths, enforcing material–illumination disentanglement and enabling image-space self-supervision. Extensive experiments demonstrate the superior performance of the proposed method.", "questions": "How does the choice of the hero view $h$ affect the final reconstruction quality? What is the selection strategy for $h$ ?\n\nIn Table 2, why doesn’t the performance consistently improve as the number of input views increases, given that additional views should generally reduce the task difficulty?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Fast relightable 3D generation. The method can produce relightable 3D assets in under a second, which significantly enhances the practicality and usability of 3D generation systems.\n\n2. Self-supervised learning on real data. The two-path architecture, combined with a differentiable renderer, allows the model to be trained on real-world images via self-supervision—an important advancement over prior works (e.g., Hunyuan, Trellis) that rely mainly on synthetic datasets.\n\n3. Strong quantitative results. The approach achieves large improvements over baseline methods across multiple benchmarks.", "weaknesses": "1. No comprehensive visual results are provided for real-world data beyond the single example shown in the teaser. Also, including quantitative material evaluations on Stanford ORB would help substantiate the method’s real-world performance.\n\n2. In the real-world example (the backpack in the teaser), noticeable artifacts appear in roughness and metallic predictions. Similarly, in Figure 2 (lamp case), the predicted high-frequency details appear blurry.\n\n3. The presented examples mostly involve objects with simple geometry and uniform materials. It would strengthen the paper to include results on more complex, multi-material objects with intricate geometry.\n\nI am happy to increase my score if the authors address these concerns.", "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)