Light of Normals: Unified Feature Representation for Universal Photometric Stereo
Authors: Houyuan Chen
Hong Li
Chongjie Ye
Zhaoxi Chen
Bohan Li
Shaocong Xu
Xianda Guo
Xuhui Liu
Yikai Wang
Baochang Zhang
Satoshi Ikehata
Boxin Shi
Anyi Rao
Hao Zhao
Venue: ICLR 2026 Poster (2026)
Keywords: photometric stereo; normal estimation; register; VIT
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A novel photometric stereo framework that alleviates the coupling between illumination and normal features, enhances fine detail recovery using wavelet-based techniques, and achieves state-of-the-art performance with strong generalization.

Abstract: Universal photometric stereo (PS) is defined by two factors: it must (i) operate under arbitrary, unknown lighting conditions and (ii) avoid reliance on specific illumination models. Despite progress (e.g., SDM UniPS), two challenges remain. First, current encoders cannot guarantee that illumination and normal information are decoupled. To enforce decoupling, we introduce LINO UniPS with two key components: (i) Light Register Tokens with light alignment supervision to aggregate point, direction, and environment lights; (ii) Interleaved Attention Block featuring global cross-image attention that takes all lighting conditions together so the encoder can factor out lighting while retaining normal-related evidence. Second, high-frequency geometric details are easily lost. We address this with (i) a Wavelet-based Dual-branch Architecture and (ii) a Normal-gradient Perception Loss. These techniques yield a \textbf{unified} feature space in which lighting is explicitly represented by register tokens, while normal details are preserved via wavelet branch. We further introduce PS-Verse, a large-scale synthetic dataset graded by geometric complexity and lighting diversity, and adopt curriculum training from simple to complex scenes. Extensive experiments show new state-of-the-art results on public benchmarks (e.g., DiLiGenT, Luces), stronger generalization to real materials, and improved efficiency; ablations confirm that Light Register Tokens + Interleaved Attention Block drive better feature decoupling, while Wavelet-based Dual-branch Architecture + Normal-gradient Perception Loss recover finer details.

Published as a conference paper at ICLR 2026
LIGHT OF NORMALS: UNIFIED FEATURE REPRESENTA- TION FOR UNIVERSAL PHOTOMETRIC STEREO
Houyuan Chen
Hong L
Chongjie Yea.4t
Zhaoxi Chen
Bohan Li Baochang Zhang
Shaocong Xu
Xianda Guo
Xuhui Liu²
Yikai Wang?
Satoshi Ikehata
Boxin Shi
Anyi Rao!
Hao Z
HKUST
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Figure 1: (left) Given multi-light images from a fixed viewpoint, LINO UniPS recovers sharper, more faithful normals than UniPS/SDM UniPS and visually rivals 3D scanner. (right) On the DiLiGenT, clear correlation exists between consistency of encoder features (CSIM/SSIM) and final reconstruction accuracy (1/MAB).
ABSTRACT
Universal photometric stereo (PS) is defined by two factors: it must (i) operate under arbitrary, unknown lighting conditions and (ii) avoid reliance on specific illumination models. Despite progress (e.g.. SDM UniPS), two challenges remain. First, current encoders cannot guarantee that illumination and normal information are decoupled. To enforce decoupling, we introduce LINO UniPS with two key components: (i) Light Register Tokens with Light Alignment supervision to ag- gregate point, direction, and environment lights; (ii) Interlcaved Attention Block featuring global cross-image attention that takes all lighting conditions together so the encoder can factor out lighting while retaining normal-related evidence. Second, high-frequency geometric details are easily lost. We address this with (i) a Wavelet-based Dual-branch Architecture and (ii) a Normal-gradins Loss. These techniques yield unified feature space in which lighting is explicitly represented by register tokens, while normal details are preserved via wavelet branch. We further introduce PS-Verse, a large-scale synthetic dataset graded by geometric complexity and lighting diversity, and adopt curriculum training from simple complex scenes. Extensive experiments show new -fate-of-the-art results public benchmarks (e.g.. DiLiGen'T, Luces), stronger generalization to real materials, and improved efficiency; ablations confirm that Light Register Tokens + Interleaved Attention Block drive better feature decoupling, while Wavelet-bases Dual-branch Architecture + Normal-gradien Perception Loss recover finer details.
Perception
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Part of project lead.
i Cooresponning author.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Photometric stereo (PS) Woodham (980)) aims to recover surface normals from multiple images under varying lighting conditions. Traditional methods Ikehata et al. (2012) 2014); Ilehata (2018); Mo et al. (2018); ;); Haefner et al. (2019) rely on assumptions of specific light sources or physical models, making them difficult to adapt to complex natural scenes.
2023); Hardy et al. (2024) have alleviated this reliance by replacing explicit physical modeling with a learnable encoder-decode architecture. The encoder extracts representations of pixel-wise, spatially varying illumination from multiple input images and produces global lighting context, while the decoder, acting as a calibration network Ikehata (2021), transforms this context into normal predictions. This paradigm achieves robust normal estimation in diverse and spatially non-uniform lighting environments, substantially advancing the field. Yet, achieving reliable performance in this setting more challenging than it seems.
Recent Universal PS methods Ikehata 2022
The first challenge lies in the ineffective decoupling of illumination and normal cues. Existing encoders jointly process lighting and normal, but without explicit illumination representation the decoder inherits unstable features, producing inconsistent normal predictions. Interestingly, we observe that when encoders produce more similar normal features across different inputs, the accuracy of normal estimation improves (Fig. right), suggesting that explicitly factoring out illumination crucial for normal recovery. The second challenge the loss of geometry details. Conventional up/downsampling operations (e.g.. bilinear interpolation Ikehata (202)), pixel shuffle Ikehata (2023) either smooth fine details or disrupt high-frequency semantics CenaattcssOdeeaaa al. (2016), leading to degraded normal quality, especially in regions with complex variation (see Fig. Ileft).
In this paper, we propose LINO UniPS, a ViT-based framework that explicitly decouples illumination from surface cues while preserving geometric detail. First, we introduce learnable Light Register Tokens to capture illumination information. To handle diverse lights, we design three distinct types of tokens, each dedicated to a specific light source: point, directional lights, and environment (Env) lights. Our key innovation is an explicit Light Alignment supervision, which guides each register token to learn the characteristics of its corresponding illumination type. Second, we introduce an Interleaved Attention Block to process the image features alongside these specialized registers. This block employs a global cross-image attention mechanism that simultaneously attends to all tokens from all lighting conditions. The synergy between the Light Register Tokens and the Interleaved Attention Block enables the decoupling of lighting and normal cues, allowing our encoder to yield a unified feature representation. In parallel, we introduce two components to ensure the preservation of sine-scale geometry: a Wavelet-based Dual-branch Architecture, which introduces the wavelet domain to help the model better attend to high-frequency details, and Normal- Normal-gradient Perception Loss that more heavily penalizes errors in high- feggu-fryeency regions during training, thereby further refining local predictions. We further construct PS- Verse, new dataset graded by surface complexity and lighting diversity, which enhances robustness and generalization under challenging real-workd lighting. Together, these designs enable state-of-the-art accuracy, robustness, and generalization across synthetic and real benchmarks.
Overall, our contributions be summarized the following three points:
1. We introduce Light Register Tokens and an Interleaved Attention Block to explicitly decouple illumination from normal features, thereby yielding a unified feature representation.
2. We adopt a Wavelet- based Dual-branch Architecture and a Normal -gradient Perception Loss, which together substantially improve the reconstruction of fine- grained geometric details.
3. We build PS- Verse, a synthetic dataset graded by surface complexity and lighting diversity, and demonstrate superior accuracy and generalization through curriculum training.
RELATED WORK
Calibrated Photometric Stereo: Calibrated PS Woodham 1980) deduces surface normals by assuming meticulously pre-calibrated wtweeedddllllnmme illumination, often relying on precise light source parameters. This stringent requirement limits the applicability of early methods. Initial methods successfully addressed Lambertian surfaces under such known lighting Woodham (1980). Subsequent research
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extended capabilities to handle complex non-Lambertian reflectance. including varied BRDFs and specular highlights Grossberg & Nayar (2003); Chandraker et al. (2005): Goldman et al. (2005)
2010); Ikehata et al. (2012), albeit always presupposing perfectly known illumination conditions.
More recently, deep learning techniques Jung et al. (2015): : Santo et al. (2017) Chen et al. (018): 8): Ju
al. (202T) have significantly enhanced the ability model intricate materials within this calibrated framework. However, the reliance of Calibrated PS on precise light source parameters severely
restricts its applications to controlled laboratory environments, thereby limiting its real-world utility.
Uncalibrated Photometric Stereo: To alleviate photometric stereo's dependence on pre-calibrated light sources, the task of Uncalibrated Photometric Stereo (Uncalibrated PS) was introduced. Uncal- ibrated PS methods Hayakawa (1944; Belhumeur & Kriegman (1996); Mallick et al. (2005): [Chen et al. p020g; Taniai & Maehara (2018); Kaya et al. al. [ichy et al. (2022) commonly adopt a two- stage pipeline: an initial stage where network module estimates the unknown lighting parameters, followed by a second stage where these estimates are utilized as known inputs, like conventional Cali- brated PS frameworks. While such Uncalibrated PS techniques have demonstrated success in handling more complex scenarios [ichy et al. (2022), their application under unconstrained natural or ambient illumination encounters significant challenges. These challenges primarily stem from the inherent dif- ficulty in accurately modeling the physics of such arbitrary and often intricate lighting environments.
Universal Photometric Stereo: The Universal PS task, recently formulated by Ikehata in Ikehata 2022), leverages a purely data-driven approach to solve the photometric stereo problem. This task enables operation under arbitrary and unknown lighting environments, critically obviating the need for complex predefined assumptions regarding the illumination. Building on this foundation, methods such as SDM UniPS Ikehata (2023) further advanced Universal PS by employing an encoder to images; these features are then fused to create a global
extract features from lighting context, which a subsequent decoder utilizes in conjunction with pixel-level information to estimate surface normals. Uni MS-PS Hardy et al. (2024) has explored multi-scale strategies to address the Universal PS problem; however, such approaches often fall short of reconstructing surface normals that are simultaneously detailed and accurate. Our analysis of prior methods like UniPS and SDM UniPS indicates that an encoder's ' ability to extract features with greater consistency significantly facilitates the decoder's task of producing figher-fidelity normal maps. Motivated by this observation, we propose our method, LINO UniPS, which is designed to learn a superior Unified Feature Representation for Universal Photometric Stereo. Furthermore, adopt a Wavelet-base Dual-branch Architecture and a Normal-ge -anadl-gradient Perception Loss to address the challenge of detailed reconstruction in high-frequency regions.
METHOD
The task of Universal PS is accurately recover the surface normal map N E Hx of object or scene from set of F images {1/}==> where L E R Hxxxx3 x3, captured under single viewpoint but multiple unknown lighting conditions {L, } Ikehata 2022).
As illustrated in Fig. 2 our method processes multi-light images through an encoder-decoder archi- tecture. Within the encoder, the pipeline consists of three main stages: First, the input images undergo processing by the (I) Wavelet Feature Extractor. This module simultaneously generates downsample components and low- and high-frequency wavelet components via wavelet decomposi- tion Doubechiiss (1990); Finder et al. (2024), and then converts both components into independent token sequences through the backbone Dosovitskiy al. (202T). 2) These sequences then enter the (II) Light Registered Attention Module, where they are prepended with specialized Light Register Tokens (Point, Direction, Env) aggregate global illumination, before being processed by cascaded Interleaved Attention Blocks. 3) Next, these tokens, excluding the Light Register Tokens, are fed into the (III) Wavelet Aggregator. Here, features from the downsample and wavelet branches are fused to produce the unified feature representation, which is finally passed to decoder for normal prediction.
The training is guided by two supervisory signals. First, a Light Alignment loss ensures that the register tokens capture the characteristics of their corresponding illumination sources. Second, a Normal Gradient Perception Loss enhances the model's ability reconstruct ainennrieedd geometric details. Furthermore, this section only provides a high-level overview; please refer to appendix for details.
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Warebet DownSample
Prezse
Light
Giebal
Light
Vissle
Hornaigndder Low
Peature Extrastor
Decoder
Feature Extractor
(1I) Light Registered
Module
(III) Agpregator
Figure 2:
Overview the LINO UniPS architecture. The encoder includes (I) Wavelet Feature Extractor, (II)
Light Registered Attention Module and (III) Wavelet Aggregator, which together fuse wavelet and downsample domain features for obtaining unified feature.
3.1 LINO-UNIPS
Despite recent progress in this field, prior methods still suffer from two primary challenges.
Challenge I: The encoder fails to effectively decouple illumination and normal information.
This failure forces the decoder to perform this disentanglement in addition to its primary prediction task. This creates paradoxical workflow, as the more powerful encoder partially offloads the most difficult disentanglement to the weaker decoder. Motivated by this, our approach empowers the encoder to generate consistent, disentangled normal features. This simplifies the decoder's role to that of simple refiner, easing the learning process. To achieve this, method introduces two components for this purpose: Light Register Tokens and Interleaved Attention Block.
Light Register Tokens: Inspired by the register mechanism in DINO Dareet et al. (2024), introduce Light Register Tokens to aggregate global illumination information. To account for varied illumination types, design distinet register tokens for point, direction, and environment (Env) lights. Our key innovation, and a crucial departure from DINO's unsupervised registers, is the introduction of an explicit Light Alignment supervision.
Drawing on the feature alignment methods used in VAVAE Yao et al. (0025) and REPA Yu et al. (2025) accelerate generative model training, we encode the three types of Light Register Tokens align with information about the light sources in the training dataset. including the point, direction upper right corner), and supervise each of them separately with cosine similarity loss. We summarize the form of the loss functions as follows:
and environment lights, (see Fig. 2
Light Cenv X2 X Edirection
(1)
where λς A2, and Ag are hyperparamettrs..The settings for these hyperparameters and the detailed formulations of the Len., Lponnt. and Ldirection are provided in appendix AA.11. and appendix A1.1.
Through Light Alignment supervision, these tokens learn to capture and encode their respective illumination information, thereby achieving decoupling of lighting from normal features. This is demonstrated by the phenomena observed in Fig. 3 The attention maps show that the register tokens have learned specialized focus. Specifically, the Point register tokens exhibit a sparse and sharp attentional focus, concentrating on high-intensity regions, which strongly aligns with the high- frequency characteristics of point light sources that produce distinct specular highlights. The Direction and Env register tokens attend to broad, spatially-diffiie regions across the object, aligning perfectly with the expected fowffrequnncy characteristies of directional and environmental illumination that govern global shading and softer shadows. This clear division of labor confirms the effectiveness of register tokens disentangling the different physical components of the illumination.
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Phirt Dirrttin
Figure 3: Attention maps for our different Light Register Tokens, derived from the encoder's final-layer feature maps for both real (left) and synthetic (right) data. These maps visually confirm that the tokens learn to focus on distinct distributions of illumination information.
Interleaved Attention Block: Previous methods like UniPS and SDM UniPS rely solely Frame Attention (Frame) Dosovitskiy et al. (202T) and Light Axis Attention (Light) Ikehata (2023). While these mechanisms facilitate local information flow, they insufficient for effectively decoupling lighting variations and normal features. Inspired by VGGT [Wang et al. (2025), our Interleaved Attention block introduces more powerful global tross-image attention mechanism (Global) that attends to all input image tokens simultaneously. Specifically, our block applies four attention layers an interleaved sequence: Frame + Light Global + Light (see Fig. 2 middle).
The key advantage of this design is its ability to aggregate and fuse information across multiple hierarchical levels. Light-axis attention operates at the patch level, frame attention captures intra- image context, and our global attention integrates information at the cross-image level. This multi- level awareness enables the model to build a holistic understanding of the global illumination from local to global scales, thereby achieving better decoupling from the intrinsic normal features.
Challenge 2: The model fails to preserve high-frequency geometric details.
Accurate normal reconstruction is highly dependent on preserving high-frequency geometric details, yet current feature extraction techniques often discard them. Although prior methods recognize this issue, their solutions are inadequate. UniPS Ikchata (2022) extracts features from downsampled images, which inherently introduces blurring. While SDM UniPS Ikehata (2023) attempts to mitigate this with "split-and- merge" operation, this process can disrupt frequenyy semantics Odena
let al. (2016). To overcome these limitations, we propose a two-pronged approach: a Wavelet- based Dual-branch Architecture preserve information during feature extraction, and a Normal Gradient Perception Loss to guide the model's focus towards these fine details during training.
Wavelet-based Dual-branch Architecture: To mitigate information loss during downsampling. our dual-branch uses the discrete wavelet transform Daubechies/ (1990). This allows us to decompose multi-light images into their high- and fwwffrey components. At the same time, retain global image-domain semantic information, we maintain a parallel branch that performs downsampling, During feature upsampling. an inverse wavelet transform reconstructs features from the wavelet domain, ensuring detail preservation throughout the network.
To further guide the network's 's focus towards complex geometric and richly textured regions, we introduce Normal Gradient Perception Loss (Cn). Instead of treating all pixels equally, this loss uses the predicted normal gradient (G) to generate confidence map
Normal Gradient Perception Loss:
(C = の that amplifies the error signal in high-frequency areas. The loss is a weighted sum of this confidence-weighted reconstruction error and gradient supervision term:
C. = N - - AS (G-
(2)
Here, the first term penalizes the difference between the predicted normal (Ñ) and ground truth (N), weighted by the confidence map C. The second term directly supervises the predicted gradient (G)
against the ground truth gradient (G VN). The coefficients x and Àş balance these two objectives. This design makes the network explicitly sensitive to fine surface details, significantly improving reconstruction quality in challenging regions.
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UnFS
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Focae taas tiigg zxaps Lyltan)
Input 10.55
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Figure 4: Features from different methods' encoders; rightmost column is variance.
Figure 5: Results of object inference with masks in Luces and DiLiGenTT. Using 16 input images.
Table I: Comparison PS- Verse with Other Photometric Sterco Datasets. The terms 'Intrinsic., 'Normal', and 'Lightt" indicate the availability of material parameters (albedo, etc.), the use of normal maps for rendering detail. and the presence of light type annotations. 'Complexity' quantifies the average magnitude of surface normal gradients, where higher values denote greater geometric intricacy. 'Shapes', 'Env', and 'Scenes' the respective counts 3D models, HDRI environment maps, and rendered scenes.
Dataset
Intrinsic
Normal
Light
Complexity
Shapes
Env
# Scenes
CyclesPS-Traie
Ikehata
2018
X
V
4.9
15
0
45
PS-Wild Wild Ikchata 2022
X
x
X
3.5
410
31
10,099
PS-Mix Lichata 2023
X
X
11.5
410
31
34,927
PS-Uni MS-PS Hardy et 2024
v
8.6
11,000
1100
100,000
PS-Verse
26.7
17,805
2423
100,000
3.2 PERVERSE DATASET
Prior large-scale synthetic datasets, such as PS-Wild Ikehata (2022) and PS-Mix Ikehata 2023)) have advanced the field by enabling data-driven Universal PS. However, they remain limited by either overly simplistic lighting (e.g., lacking high-frequency point sources) or geometrically simple objects that fail to produce complex lighting variations and seaf-shhd effects.
Leveraging publicly available large-scale 3D asset datasets Deitke al. (2022 2023 Vecchio & Deschaintre (202)) we propose the PS- Verse dataset (see Tab, for a detailed comparison with other Photometric Stereo datasets). To increase the lighting complexity in rendered scenes, we carefully select 17,805 textured 3D models with UV coordinates and PBR materials from the Objaverse dataset Deitke al. (2022), which contains nearly 800,000 models. Following Dora Chen al. (2024), which quantifies geometric complexity based on the density of sharp edges, we categorize the objects into four complexity levels. We then construct scenes by recursively selecting 4 to 6 objects from each level, and classify scenes into four corresponding complexity tiers.
Regarding materials, textures are randomly sampled from the MatSynth large-scale PBR material database Vecchio & Deschaintre 2024), consisting of 806 metallic, 1,226 specular, and 3,321 diffuse material groups. To balance the distribution of specular and diffuse lighting effects, we assign object materials according to the ratio of 1:4:2.5:2.5 across original object textures, diffuse, specular, and metallic materials. To more realistically simulate fine surface detail interactions with light, we introduce normal mapping for objects for the first time in such data, defining this as a fifth complexity level that injects rich high-frequeen lighting details.
In terms of lighting setup,
uniform environment light simulate non-dark ambient conditions. Detailed lighting configurations are provided in the appendix A.3333 Overall, the PS-Vers Verse dataset consists of 100,000 scenes generated with Blender Foundation Each scene includes two rendering outputs: with and without normal mapping. We render 20 images at a resolution of 512 per scene.
In addition to providing ground truth for surface normals, the Pe-Verse dataset also offers ground truth for albedo, roughness, and metallic, to support PBR prediction tasks. The PBR prediction results and visual showcase of the PS-Verse dataset can be found in the appendix A1.2 and A1.3

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Published as a conference paper at ICLR 2026
Ground Truth
Baseline
+ +light register tokens
+global attention
+light alignment
+wavelet branch
+grad perception loss
+curriculum learning
Figure 6: Qualitative results for the main ablation study. The visual comparisons are shown on samples from our PS-Verse Testdata. We recommend zooming in to observe fine-grained details.
EXPERIMENTS
Implementation Details: To effectively enhance method's capability for reconstructing fine- grained surface normals, we employ curriculum learning strategy that progresses from low to high geometric complexity. We start training PS-Verse Verse Level data, adding higher levels every 10 epochs up to Level 4, for about 150 epochs. Then, we finetune on Level 5 data with ground truth normals until 200 epochs to improve surface detail reconstruction. We use AdamW Optimizer with le-4 initial learning rate, 0.05 weight decay and a step decay of 0.8 every ten epochs. Input images per batch vary randomly from 6. The total loss L combines multiple components:
L = Light + Ln
(3)
Training runs on NVIDIA H100 GPUs for roughly days. Inference takes around 1.5 seconds for 16 input images at 12 H100.
Evaluation Metric: We evaluate our results using three metrics. For surface normal accuracy, we measure the Mean Angular Error (MAE, in degrees, J). To assess feature similarity, utilize Cosine Similarity (CSIM T) on normalized features and the Structural Similarity (SSIM t) Wang et al. (2004) their 3D PCA projections. For both metrics, the final score is obtained by averaging the similarity values computed between all possible feature pairs. For instance, given a set of 6 features, calculate all C9 pairwise values to derive this mean.
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Evaluation Dataset: For evaluation, comparison experiments and ablation studies, we employ two public benchmarks, DiLiGenT Shi et al.] (2018) and Luces Logothetis et al. (2022), and our synthetic Pe-Verse Testdata. This set contains 441 scenes held out from the training data, with each seene featuring single object to allow for clearer qualitative evaluation. To verify the model's generalization capabilities, we test on real-world images from SDM UniPS Tkehata (2023).
4.1 ABLATION STUDY AND COMPARISON EXPERIMENT
The main ablation quantitative results are presented in Tab. 2 and the qualitative re- sults are in Fig. 6 First, demonstrate the superiority of our PS-Verse dataset, we retrain the Uni MS-PS Hardy et al. (2024) on both the PS-Mix Ikehata (2023) and our PS- We then compare these models against the officially released Uni MS-PS, which was trained on its native data (PS-Uni MS-PS). The results show that the model trained on PS- Verse yields substantially better feature similarity scores (CSIM and SSIM) and a lower MAE for normal reconstruction, which strongly indicates that PS-Verse is more effective for training high-performance models.
Ablation:
conduct ab- lations of the various mod- ules within LINO UniPS method, starting from a base- line model where all our pro- posed enhancements are re- moved. Our initial finding is that by merely incorporating unsupervised Light Register
Second,
Table 2: Main ablation with 20 multi-lights input images. The evaluation
metries were measured on our PS- 3e-Verse Testdata.
Method
Dataset
CSIM
SSIMI
Avg. MAE
Uni MS-PS Uni MS-PS
PS. PS-Uni MS-PS
0.72
0.70
9.02
PS-Mix
0.63
0.66
10.02
Uni MS-PS
PS- PS-Verse
0.75
0.73
7.82
Baseline
PS- -S-Veree
0.71
0.69
8,73
+ light register tokens
PS. Verse
0.74
0.73
8.13 (0.60 J)
+ global attention
PS. -eree
0.80
0.78
6.44 (2.29 J)
Tokens, both the feature sim- ilarity metrics and the normal
+ light alignment
PS- Verse
0.86
0.82
5.58 (3.15 J)
+ wavelet branch
PS- -S-Verce
0.85
0.82
5.15 (3.58 L)
reconstruction results show an improvement. This suggests that these additional tokens can often capture global lighting information even without direct supervi- sion, thereby aiding the disentanglement of normals from illumination. Progressively incorporating our global cross-image attention mechanism to our interleaved Attention Block and light alignment su- pervision leads to more significant improvements in both feature similarity and normal reconstruction performance. This further validates our central conclusion: that more effectively decoupling lighting and normal features enhances the quality of normal reconstruction. Next, we integrate the wavelet branch form our Wavelet-based Dual-branch Architecture and the Normal-gradient Perception Loss for extracting fine-gu tne-grained context. While these two modules have a modest impact on feature similarity metrics, they substantially improve performance on data with complex geometries as intended. Finally, from the last row, adopting curriculum learning boosts all metrics, improving feature similarity while also reducing MAE, which confirms the effectiveness this training strategy.
grad perception loss
PS PS-Verse
0.86
0.83
4.84 (3.89
curriculum learning
PS- PS-Verse
0.88
0.86
4.51 (4.22 ))
Comparison 1: To isolate the architectural ad udvatages of LINO UniPS from the ef- fects of training data, we re- train it alongside Uni MS-
Table 3: Architectural comparison on the PS-Mix dataset Ikchata| (2023). The consistency of the encoder features is measured by Cosine Similarity (CSIM) and Structural Similarity (SSIM), while the final normal reconstruction accuracy is evaluated by Mean Angular Error (MAE).
PS Hardy et al. (0224) on the PS-Mix dataset Ikehata
MAE!
Method
CSIM
SSIM
(2023 to convergence 100 epochs). As presented in Tab. our LINO UniPS sur- passes SDM UniPS and Uni MS-PS across both evalu- ated public datasets. Notably, LINO UniPS yields higher CSIM and SSIM scores, quantitatively demonstrating that features extracted by our encoder exhibit greater similarity compared to those from SDM UniPS Ikehata (2023) and Uni MS-PS. It is noteworthy that our LINO UniPS utilizes a decoder architecture identical to that of SDM UniPS Ikehata (2023). This architectural commonality strongly suggests that the observed performance improvements are primarily attributable to our en- coder's enhanced capability to more effectively decouple illumination from geometry. Such effective
DiLiGenT
Luces
SDM UniPS Ikehata (2023)
0.84
0.72
5.80
13.50
Uni MS-PS Hardy at. 2024)
0.82
0.76
5.75
13.71
Ours
0.90
0.91
5.60
12.70
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Table 4: Evaluation on DiLiGenTT Shi et al. (2018). Uses all images unless otherwise noted (K). Normal reconstruction accuracy is evaluated by Mean Angular Error (MAE).
Ruddha
Cow
Gobllt
Harvest
Reading
Avg. MAE
UniPS Bichuta
2022
4,90
9.10
19.40
13.00
11.60
24.20
25.20
10.80
9.50
18.80
14,70
3.60
7.50
10.20
Uni MS- PS Frmnyy er 2014
1.92
3.14
6.16
3.60
4.04
6.35
8.84
4.08
4.88
7.09
5.01
Ours w/ mlp
1.21
3.62
7.36
4.83
4.94
6.11
10.71
5.37
5.23
7.54
5.69
5,17
Ours(K-32)
1.75
2,66
6.26
3.49
4.06
5.25
8.71
4.12
4,26
6.75
4.73
Ours(K=16)
1.92
2.74
6.40
3.51
4.25
5.41
8.81
4.14
4.45
7.12
4.88
Table 5: Evaluation on Luces Mecca et al. (2021). Uses 52 images unless otherwise noted (K). Normal reconstruction accuracy is evaluated by Mcan Angular Error (MAE).
Method
Bdll
Bell
Buddho
House
Jur
Dwl
Tool
UnaPS Bichu RON
11.01
24.12
23.84
27.50
23.51
28.64
16.24
21.41
35.53
14.53
32.57
28.36
25.36
19.03
23.77
S0M Iomornaeee pact
13.90
17:76
3:44
18:58
8.55
19.67
7.25
8.86
36.07
8:90
13.67
15.97
16.01
17:56
13:40
Uni MS Melarryraa soale 2024
10.20
10.52
6.98
12.83
9:60
13.65
6.19
8.33
25.29
6.30
11.47
12.45
11.36
11.79
11.21
9,99
0:57
7.65
11:17
19.2
10.66
6.89
22.91
6.24
9.58
9.54
Oues (K-15)
10.7
23,00
0.39
10.48
9.95
10.29
decoupling fosters stronger normal consistency within the learned features, consequently boosting the accuracy and capability of the final normal reconstruction.
Comparison 2: We present a comprehensive analysis of the parameter count in Tab. To investigate the impact of model size, we finetune two smaller variants PS-Verse, Ours-Si (73.2M) and Ours-S2 (60,4M), by reducing the number of layers in Feature Extractor and Interleaved Attention Blocks. These models are designed have parameter counts comparable Uni MS-PS (75.5M) and SDM UniPS (59.9M). The results clearly show that our models consistently outperform their counterparts at similar parameter scales, confirming that the superiority of our method stems from its architectural design, not merely from a larger parameter count.
Furthermore, the table compares inference times. On both high-resolution (4000x and standard- resolution (512x612) images, our method runs slightly faster than SDM UniPS and is substantially more efficient than Uni MS-PS, which requires approximately 35 times more computation time. This highlights the efficiency of our approach, particularly for high-resolution processing.
4.2 QUANTITATIVE RESULTS
As observed in Tab.
the DiLiGenT Shi et al. (2018). method achieves SOTA performance with an average MAE of 4.65%, outperforming the previous best method. Similar results can also be information, our method obtains an average MAE of 9.43 This represents a substantial improvement over the prior best performance (1 1.21%). Some qualitative results are presented in Fig.
in Tab. On Luces Mecca al. (02T), dataset featuring high-frequency
To further underscore the efficacy of our encoder, we conduct an additional experiment. In this setup, replace the standard decoder in our LINO UniPS with simpler Multi-Layer Perceptron (MLP) and then finetune this variant until convergence. Our findings are twofold: First, the consistency of the extracted features is further enhanced, as detailed in Fig. Second, although the reconstruction performance sees a slight degradation compared to LINO UniPS with its original, more sophisticated decoder, this MI.P MI.P-decoder variant (w/mlp) still outperforms SDM UniPS. These results align with hypothesis that the superior performance of LINO UniPS is primarily driven by its encoder's advanced capability to generate highly consistent and well-disentangled features.
Moreover, as detailed in Tab. and Tab. 5) our method achieves SOTA all scenes except for the 'Ball' object in both benchmarks. demonstrating the general superiority of our approach. Interestingly, variant. We hypothesize
on the 'Ball' scenes, the best performance is achieved by our that this is because a simpler MLP decoder is better suited for recovering geometrically simple primitives like the 'Ball."

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Published as a conference paper at ICLR 2026
Table 6: Ablation study on parameter count. Inference time is measured 16 images (K=16). The 4000 4000
and x 612 resolution images are sourced from the SDM UniPS real data [kehata (023) and the DiLiGen'T et al. 2018).
Inference Time (s)
MAE (9)
Method
Params (M)
4000
4000
512
612
DiLiGenT
LUCES
Ours
84.2
85.1
1.7
4,65
9.43
Ours-S1
73.2
82.9
4.83
10.05
Ours-S2 S2
60.4
81.0
1.5
4.95
10.89
SDM UniPS
59.9
92.7
5.83
13.52
Uni MS-PS
75.5
3012.2
35.3
5.01
11.21
Inpul RGB
OurS
Uni MS-PS
SDM UniPS
UniPS
Figure
Qualitative comparison high-resolution (4K) 'Coins and keyboard' scene from UniPS Ike
hata 2023
with 8 input images. Our method recovers more intricate details than the baseline.
4.3
QUALITATIVE RESULTS
Fig. 1 and Fig. 7 showcase our method's ability to reconstruct highly detailed and accurate surface normals for real-world objects and scenes, highlighting strong generalization capabilities. shown in Fig. [ when processing high-resolution images, close inspection of detail-rich regions like the rabbit's cars and abdomen reveals a clear distinction. While UniPS and SDM UniPS produce over-smoothed results and fail capture the intricate surface geometry, our LINO UniPS successfully reconstructs fine-grained details in these challenging areas. Fig. |7 further demonstrates our method's performance more complex, high-resolution (4K) real-world scenes. Our method consistently produces more physically plausible and detailed results. For instance, key differentiator is revealed the background textile, as shown in the magnified view of the top row. Here, Uni MS-PS, SDM UniPS and UniPS fail to reconstruct the complex texture of the tablecloth, whereas our method demonstrates stronger generalization by accurately recovering its intricate fabric pattern. Additional reconstruction results real- world scenarios can be found in appendix A1.5.2
5 CONCLUSION
In this paper, we propose LINO UniPS, novel framework for Universal Photometric Stereo that addresses two core challenges. The first is the failure to disentangle Illuminatooninvann invariant surface normals from spatially-varyine lighting. To this end, we employ Light Register Tokens with an explicit light alignment and an Interleaved Attention Block with global cross tross-image attention mechanism. These components work in concert to capture the global lighting context and enable better separation of lighting and normal features. The second challenge is the loss of fine-grained detail. To address this, we integrate a wavelet branch to form Wavelet-based
Qual-branch Architecture and introduce Normal- Normal-gradient Perception Loss, which heightens the model's sensitivity to intricate geometry. Finally, we contribute a complex and large-scale photometric stereo dataset, hoping to provide valuable reference for future research.
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ETHICS STATEMENT.
This work does not involve human subjects, personally identifiable information, or sensitive data. The datasets used in this study publicly available and widely adopted in the machine learning community. All experiments were conducted using standard computational resources without en- vironmental societal harm. The methodology does not introduce discriminatory biases, and the model's ss potential applications are aligned with responsible Al principles. The authors have reviewed the ICLR Code of Ethics and confirm that this submission adheres to its guidelines.
REPRODUCIBILITY STATEMENT.
To support reproducibility, provide complete description of our model architecture, training procedures, hyperparameters, and evaluation protocols in the main paper. Additional implementation details are included in the appendix. We have strived to document all necessary components with sufficient clarity to enable independent replication of our results.
ACKNOWLEDGEMENTS.
This work was partially supported by a grant from the NSFC/RGC Collaborative Research Scheme sponsored by the Research Grants Council of the Hong Kong Special Administrative Region, China and National Natural Science Foundation of China (Project No. CRS_ URR_HKUST605/25)
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A1 APPENDIX
This supplementary document offers further technical details, demonstrations of the datasets em- ployed, additional insights, and comprehensive results pertaining to our LINO UniPS.
A1.1 NETWORK ARCHITECTURE DETAILS
Interkenee Attention Hlock
PHW3
Fuw
DownStmppt
Extractor T.r.
Light Arts Attirtttttt
Light Attt-tttttio
Kuddo Black
4
(9) Waveler UpSample
Filc
Globat Attrass
Decoder
Foame
Wavele
Muin light
(b)
Images
(43.igh Racisdedd Attredion Mickde
Encoder
Figure AI:
overview our network architecture, illustrating the corresponding tensor shape transformations. Black solid arrows denote the forward pass of the downsample branch, while purple dashed arrows denote the forward pass of the wavelet branch.
To provide readers with a more in-depth understanding of our LINO UniPS network architecture, in the following, we detail the architecture of our LINO UniPS, breaking it down into several key modules: (a) Wavelet DownSample Module, (b) Feature Extractor, (c) Light Registered Attention Module, (d) Fusion Block, (e) Wavelet UpSample Module, (f) Decoder and the training loss. An overview of our network architecture is presented in Fig. AT] In the subsequent sections, we will provide detailed account of the network's structural components and the specific transformations of tensor shapes as data progresses through the model.
A1.I1 WAVELET DOWNL MODULE:
To enable our encoder to extract more fine- one-grained contexts, we first incorporate the wavelet trans- form Daubechies (1990), chosen for its ability separate image's high- and low-frequency /Fnnre components Finder et al. 2024); Huang et al. (2017) while concurrently mitigating losses typically
incurred during downsampling Peng
20244).
Initially, the input to our network is batch of multi-light image sets, represented by tensor In this notation, B denotes the batch size, F is the number of images captured
I E BxFxxxx under different illumination conditions for each scene instance, H and W represent the spatial dimensions (height and width), and the final dimension 3 corresponds to the RGB color channels. To simplify the subsequent exposition, we will assume batch size of B = 1 unless otherwise specified, effectively considering the processing of single multi-illumination image set at time. As part of the preprocessing, ensure that image pixel values lie within comparable range, each of the F images within given scene instance normalized by a random sealar sampled uniformly between its maximum and mean values. Following this preprocessing, for the purpose of subsequent discussion (effectively assuming B 1), we obtain a set of images [ where each I E R !I XXx
Following SDM UniPS Ikehata 2023), for each pre-processed input image I, E Hx we perform two separate transformations: naive downsampling to obtain 15 E # x3 in the image domain, and a wavelet transform yield its corresponding wavelet domain components 1 R4 W x3 namely 14 E # 1 = 1pd E x and Ihh E x
A1.12 FEATURE EXTRACTOR:
Subsequently, both the downsample image representation 1 E R 4 x3 and the set of wavelet components I (comprising 17, 17 11 Ihh, each R x " x3 are individually processed. First, each of these input components is partitioned into sequence of basch-based tokens. These token sequences are then fed into our Feature Extractor backbone. This backbone trained during our training procedure to extract rich visual representations from these diverse inputs. In specific

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Published as a conference paper at ICLR 2026
implementation, set the patch size to P 8. Consequently, for each input component with spatial dimensions H/2 W/2, the resulting sequence length L (H/2)x(W/2) tokens. The feature embedding dimension is D = 384. Following processing by this backbone, we respectively obtain shallow visual feature representations F R from the downsample image stream (derived from 14) and F E R4xLXD from the wavelet components stream (derived from ff").
A1.1.3 LIGHT REGISTERED ATTENTION MODULE:
To achieve a more effective decoupling of lighting and normal features that are subsequently processed by the decoder, we introduce our Light Registered Attention Module.
Firstly, we design the Light Register Tokens to improve the handling of global illumination. While lighting information predominantly exhibits global characteristics across multi-light inputs [kehata (2018) 2022 2023). traditional attention mechanisms in existing Universal PS methods often fail to fully leverage this distributed information. This deficiency can hinder effective illumination- geometry separation, motivating our specialized token-based strategy. Drawing inspiration from advancements like Darcet et al. (2024), and further considering the inherent illumination-depeddenn of the Universal PS task, we introduce these Light Register Tokens to explicitly capture and represent decoupled global lighting information within our framework.
To facilitate the perception of distinct lighting components, we additionally introduce three specialized Light Register Tokens: Fenv R xxI designated for perceiving environment light; pooit E 1x tailored for point lights (which often contribute high-frrquue high-frequency illumination effects); and Edirection E RIIDD for directional light (typically representing low-frequency illumination sources). Subsequently, this set of three specialized light tokens is prepended to the token sequences derived from F (features from the downsample image stream) and 5.f (features from the wavelet components stream), respectively, leading to:Fs a.f.r E LR and *.f E B4XL'xD, where L' = L +
Then Fd. and F are fed into our Interleaved Attention Block Wang et al. 2025 to enhance inter- intra feature communication. Specifically, our Interleaved Attention Block contains four attention layers, which can be represented as: Frame + Light -> Global - Light.
Previous work has found that feature communication within the encoder is very important (apphraa Ikehata (2022) 2023); Hardy et al. (2024), but their methods are often limited to patch-level local light-axis attention. Our Interleaved Attention Block, however, breaks such limitations. On the one hand, incorporates Frame attention to enhance intra-image communication. On the other hand, we have added Global attention, a more comprehensive global operation, allowing inter-image features to also be extended from the patch level the image level. Upon processing by these four cascaded Interleaved Attention Blocks (the number chosen to maintain a manageable parameter count, although more blocks could potentially be employed), we obtain the deep feature representations denoted as d IXXx''D It is worth noting that the attention operations inherent in these blocks do not alter the fundamental shapes of these tensor sequences.
Fdl E RR' xD and For
To ensure the Light Register Tokens Tenvs Tpoints Edirection effectively capture global illumination, as direct supervision of the decoded light map is challenging, we introduce a light- feature aligmment strategy during training Yu al. (2023); Yao et al. (2025) : Song et al. (002). Since we train LINO UniPS on our own rendered synthetic dataset PS- Verse, for every scene within this training dataset, we have access to its corresponding lighting information from the rendering process. This includes: the HDRI environment map, the positions, the distance to camera and intensities of point lights, and the positions, the distance to camera, intensities and areas of directional lights. Mathematically, these are denoted as Lenv E H x3 Loinn E R and Ldirection E R A52
respectively. For Lppiin E R x5 where M1 is the number of point lights, its first three dimensions denote position, the fourth denotes the distance and the fifth denotes the intensity. For the directional light component Ldirectinn E M2 where M2 denotes the number of directional lights, its first three dimensions specify position, the fourth denotes the distance, the fifth denotes the directional light size, and the sixth indicates intensity. Subsequently, Lpoint. and Ldirectinn by projecting them into a D-dimensional feature space, thereby obtaining their 1/4 dirrection E DR Similarly, the light tokens Denvs I poinns and Edrrectonn after being processed by the cascaded Interleaved Attention Blocks, are projected into the same D-dimensional feature space. This yields their respective high-dimensional
encode the lighting components Trane
respective representations 1/ E IR I E RDD, and
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Table Al: Comparison of PBR material prediction performance. A dash (-) indicates the method does not provide the corresponding output.
Normal
Albedo
Metallic
Roughness
Method
MAEL
PSNR1
SSIM
PSNR1
SSIM
PSNR1
SSIM
UniPS Ikehata 2022
24.67
IDarb Lt al 2025
29.51
25.11 24.04
0.9271
25.62
0.9148 0.9060
24.85 23.87
0,9342
SDM Um Ikchata
2023
10.25
0.9076
23.68
0,9289
Uni MS-PS PS Hardy et al 2024
8.92
Ours
4.51
30.49
0.9529
32.99
0.9357
31.32
0.9670
Input RGB
Ground Truth
Our
Uni MS-PS
SDM UmiPS
UniPS
Input RGB
Ground Truth
Oun
SDM UnIPS
IDA
Neddlc
Figure A2:
Qualitative comparison on PS-Verse Verse Testdata. LINO UniPS demonstrates superior PBR estimation compared to all other methods.
representations: A x paint and xh ddrecction all in R.D. Specifically, this projection is realized using three structurally similar two-layer Multi-Layer Perceptrons (MLPs). Within this common embedding space, we employ cosine similarity to supervise and align their respective feature distributions. Cosine similarity is chosen as it effectively measures the directional concordance between feature vectors, making it suitable for aligning representations of different lighting characteristics. This supervision translates into three distinct loss functions, denoted as Lenvs Lpiints and Ldirection- Their respective mathematical formulations are:
Lenv =
Σ
(1h
x
(Ala)
Epoint Σ
(1 (Point xhi xpoint)
(A1b)
Ldirection
Σ
(frrecttoon direcction)
(A1c)
A1.1.4 FUSION BLOCK:
The subsequent discussion details the Fusion Block applied to features extracted during the initial stages of our Encoder. The overall feature fusion process is DPT-based Ranfil Ranftl al. (202)) For clarity,

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we will first illustrate this process using the features derived from the downsample image features Fd f. as the primary example. Within each Interleaved Attention Block of the encoder, features Light Axis, Global, and Light Axis are first concatenated along the feature dimension. This operation yields an aggregated feature set for each block, denoted as Fdd(() E RIx44, where i E 2,, 3, 4) represents the index of the i-th attention block. It is crucial to note that the three additional Light Register Tokens (introduced previously) do not participate in this specific feature aggregation (concatenation) process. Therefore, the feature dimension of Fd.(() is 4D, not 4D'.
obtained from its four internal attentioon-Fratt
Then, to effectively fuse features from different depths within the encoder, our Fusion Block employs top-down, multi-scale fusion strategy. This process begins by selecting the aggregated output Fdi(() from four different stages of the Interleaved Attention Blocks. These features are initially ID token sequences. First, these token sequences are reshaped from their ID sequence format back into 2D spatial feature maps. Next, these four distinct spatial maps (which come from different depths of the Interleaved Attention Blocks) undergo a series of projection and downsampling operations to transform them into a standardized, four-level hierarchical feature pyramid, denoted H1, H2, H3, and H4. These operations typically consist of 1x1 convolutions to project the features to their target channel dimensions 2C, 4C, 4C') and downsampling (2x2 convolutions with a stride of 2) to match the target resolutions. This pyramid captures multi-scale information, ranging from high-resolution features at H2 (shape F x C x H/2 X W/2) now-resolutiionhhih--emmma features at H4 (shape F x 4C' x H/16 x W/16), where F is the number of multi-light images, C is 256, and H. W refer to the spatial resolution of the original, full-sized input images. We then employ progressive, top-down fusion path. This fusion is implemented using residual convolutional blocks He et al. (2015) and upsampling operations (2x2 transposed convolution with a stride of 2). Specifically, the deepest feature H4 is first upsampled and then fused with H3 via residual block; this result is then upsampled and fused with H2, and so on. The final output of this progressive fusion is a single, information-rich fused feature map, F'used: with a shape of fx x 7 x yff xC which is
features, fd
then passed to the Wavelet UpSample module. A similar fusion process is applied to the features derived from the wavelet components path (FX,,, ), yielding a corresponding fused representation,
Friied E R4XFX x y xc It is noteworthy that this strategy of selecting four feature levels for hierarchical fusion can be adapted if more than four Interleaved Attention Blocks employed in the encoder's 's initial stages. For instance, if six such blocks are utilized, features from blocks indexed 1, 2, 4, and 6 might be selected to form the pyramid. Similarly, for an eight-block configuration, features from blocks 1, 3, 5, and 7 could be chosen as inputs to the hierarchical fusion pathway.
A1.15 WAVELET UPSAMPLE MODULE:
To obtain the final encoder output Fene E XX the features derived from the downsample image path Fiised and those from the wavelet components path Fiised should be integrated. The process is as follows: first, the feature map Fid Fiused is upsampled. yielding a representation fused E
FuP
RFxHxx Concurrently, for 17 Fusedd which originates from the wavelet-transfoomeed inputs, an inverse wavelet transform is applied to convert it back to the spatial domain, resulting in / E RxxxxxxC xC Finally, these two processed feature sets, Fixed and Edut are element-wise summed. A Gaussian blur is subsequently applied to this sum to promote a smoother and more effective fusion of these potentially cross-domain features, ultimately producing the final encoder representation Fenc E IR x
A1.16 DECODER:
The decoder architecture in our LINO UniPS is largely identical to that of SDM UniPS Ikehata 20023); for clarity, we briefly outline its key components and rationale here.
A common initial step in PS for surface normal estimation is the pixel-wise aggregation of spatial-light features along the illumination axis, effectively reducing F light channels to a single representation per pixel using input images I and their corresponding encoded features F We introduce an approach, termed the pixel-sapplig Transformer Wu et al. (2024b); Contributors (2023): Wu et al. (2022) 2024a), which uniquely operates on a fixed count (m, e.g., m = 2048) of randomly chosen pixel locations. This strategy offers distinct advantages: it maintains constant memory footprint
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Dir Back
Point Dir Back
Point Dir + Eny
Point +Drr Back
Point Eny
Point +En
Point +Di Back
Point Back
Dr
Point Dir + Ery
Point Baac
Point +ir
+ Back
Point + Back
Poirt
Ero
Point.
Point +Dr
Dir
Point Eny
Figure A3: 20 multi-light images of one scene and their respective lighting configurations.
sample set regardless of image dimensions, thus ensuring excellent scalability; furthermore, by processing a sparse, randomly distributed set points, it substantially curtails werrsmoothing artifacts often prevalent in dense convolutional operations. The practical implementation of the pixel-sampling Transformer involves selecting m random pixels, denoted { from the valid (masked) region of the input image. For each sampled pixel T;, its associated features File (z:) e RC are obtained. These features Fire(z; are then combined through element- addition with (x,) E RC, The term I2( (2;) represents (x,) E R33 and this projection is performed by two-layer MLP with the objective of enhancing the representational power of these raw observations by mapping them to this tigher-dimensione space. Notably, our strategy of first projecting the raw observations to RC and then performing addition differs from SDM UniPS Ikehata (2023), which typically employs direct concatenation of features and the raw observations. These added per-pixel features Ff F are subsequently condensed into compact descriptors A(x;) by employing Pooling by Multi-head Attention (PMA) Lee al. (019)). The resulting collection of m descriptors, A(x;) is then fed into Transformer network. The processing for each of the yn sampled points involves applying frame attention and light-axis attention to aggregate non-local context and cross- information. Following this, two-layer MLP is utilized to predict the surface normal vector for each location. These sparsely predicted normals are then systematically merged- -for instance, through spatial interpolation dedicated upsampling module-ta reconstruct the full-resolution surface normal map corresponding to the original input image dimensions. In essence, the pixel-sampling Transformer facilitates the modeling of robust non-local dependencies with notable computational efficiency, while concurrently preserving fine details in the output normal map. This makes the approach particularly well-suited for physics-based vision tasks Chen et al. 2025 that involve high-resolution imagery.
dige-dimensional projection the raw pixel observations
A1.17 TRAINING LOSS:
In this part, elaborate the composition of our total training loss and the design of the respective weights for its constituent components. Based on the definitions provided in Eq. Eq. 2 and Eq Al the overall training loss L. for our LINO UniPS method be expressed
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L= Xzeav + Xp + A Addrrtctinn + 1 INN DGG-G)
(A2)
Let us define the confidence-weiihteed normal reconstruction loss as Coont and the normal gradient supervision loss as Lg. The overall training loss C can then be expressed as:
= Cenv + X2 + Cd + >g A5
(A3)
Since our primary objective is surface normal reconstruction, Lcont (our confidence-weighted recon- struction loss) is established as the principal component of our total loss function. First, we provide a detailed explanation for our selection of Lcont as the primary loss function. We elaborate on why this specific formulation was chosen over other potential candidates, such as direct MSE, CNN-NV
or alternative loss weighted by e G VN,, namely (N N22 e
primary motivation for our LINO UniPS framework to advance beyond prior Universal PS methods by specifically improving the handling of challenging high-frequency regions. We identify these regions based on large magnitudes of the surface normal gradients, as these directly reflect geometric complexity. We deliberately avoid using gradients derived from the input RGB multi-ligh light images as the primary criterion for this identification. The rationale is that while RGB gradients are indeed large in areas of intricate geometric detail, they can also exhibit high magnitudes in regions with significant basecolor variations, which do not necessarily correspond to the geometric high- frequency features we aim emphasize and reconstruct accurately. Consequently, our methodology incorporates a loss function that is directly informed by surface normal gradients, rather than relying on a naive MSE.
A crucial aspect of this gradient-informet loss strategy concerns the source of the gradients utilized for weighting or guidance. We opt to utilize network-estimate normal gradients G for this purpose, rather than directly employing ground truth normal gradients G. This design choice is primarily motivated by two factors: Firstly, it compels the network to intrinsically estimate high-frequncc components from the input, thereby fostering its inherent capability to process and represent fine- grained details. Secondly, refraining from direct weighting by ground truth normal gradients typically leads to more stable and manageable training process, especially during the initial stages when network predictions may significantly deviate from the ground truth.
Our design for the loss weights as follows:
0.1
0.1
A (Erm Lecont )sg
A2 (Epoimm / Loant log
(A4)
0.1
0.1
A LLirection fecet)
1,
(Le// Lecnt)
where the subscript 'sg" denotes that the term within the parenthesis is treated as a constant (i.c., its gradient is not computed during backpropagation for the purpose of this scaling factor, akin to detach () in PyTorch).
Our decision set a to 1 is because Lconf serves as the principal component in overall loss function. The remaining auxiliary losses are then scaled using the adaptive weighting mechanism detailed in Eq. A4 This mechanism constrains their magnitudes to 0.1 times that of the primary loss's detached value, (Zeunt)yg. while still allowing their gradients to backpropagate fully. Such a strategy effectively positions these auxiliary losses to act as regularizers to the main learning objective, rather than allowing disparate loss magnitudes to vie for dominance and potentially destabilize training. This controlled weighting is crucial for ensuring stable and efficient training LINO UniPS, mitigating issues such as excessively slow convergence or even training failure that can arise from an unbalanced multi-term loss function.
A1.2 PBR MATERIALS PREDICTION
The intrinsic properties of surface (albedo, metallic, normal, and roughness) are fundamentally entangled, as they are all governed by the underlying surface geometry and material composition. Consequently, our LINO UniPS architecture, designed for normal recovery, be naturally extended to jointly estimate a full set of PBR material parameters. This extension requires only finetuning on an appropriate dataset with modified loss function.

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A1.2.1 IMPLEMENTATION DETAILS
Our PS-Verse dataset was created with this purpose in mind; alongside surface normals, we also rendered ground truth maps for albedo (3-channel RGB), metallic (1-channel), and roughness (1- channel). sample of this data shown in Sec. A1.3
Starting with our LINO UniPS model pre-trained for normal estimation, we finetune it on this complete PBR dataset for approximately 10 epochs until convergence. This process enables the model to simultaneously predict all four properties from the same input images. For the loss function, we augment the original loss L with weighted MSE term for the material maps. The new loss is defined as:LpBR = L λBLa + A7hm + λghr, where La, Lm; & are the MSE between ground truth and predicted albedo, metallic, and roughness maps, respectively. The coefficients λe, A7, Às are their corresponding loss weights, which are defined as:
A6
X7
As
(AS)
(Lm/ Leont)
Scontt
A1.2.2 EXPERIMENTAL SETUP
Baselines: Besides the Universal PS methods mentioned in the main paper (UniPS Ikehata (2022).
SDM UniPS Ikehata 2023), Uni MS-PS Hardy et al. (2024), we added one more baseline, IDArb Li al. 2025). It should noted that we chose the BRDF version of SDM UniPS, which can predict PBR materials. And IDArb can also predict PBR materials. In contrast, UniPS and Uni MS-PS can only predict normals.
We evaluate our results using three metrics. For surface normal accuracy, we measure the Mean Angular Error (MAF, degrees, 4), consistent with our main experiments. For the predicted albedo, metallic, and roughness maps, assess their quality via the Peak Signal-to-Noise Ratio (PSNR, in dB, 1) and the Structural Similarity Index (SSIM t).
Evaluation Metries:
Evaluation Dataset:
The evaluation is conducted on our PS- Verse Testdata, which contains ground truth for all four intrinsic properties.
A1.2.3 EXPERIMENT RESULTS
The quantitative results for PBR material estimation are presented Tab. AT The results clearly show that our PBR-version LINO UniPS achieves SOTA performance across all predicted maps, including normal, albedo, metallic, and roughness. This comprehensive superiority further validates the effectiveness of our proposed architecture. Furthermore, observe interesting trade-offs among the baseline methods. While IDArb underperforms all other baselines on the normal prediction task, it surpasses SDM UniPS in estimating the other PBR properties (albedo, metallic, and roughness).
Fig. A2 presents the qualitative results on our Verse Testdata, where our LINO UniPS predictions demonstrate the highest visual fidelity to the ground truth for all properties. In contrast, the baselines exhibit noticeable artifacts. For complex normal estimation, most methods struggle, and IDArb fails completely by predicting flat surface. For the other material maps, SDM UniPS tends to bake in RGB texture details (e.g., the barrel pattern), while IDArb fails to disentangle complex shading, particularly in shadowed regions like on the soundbox. These visual comparisons further highlight the robustness and superior disentanglement capabilities of our approach.
A1.3 DATASET ANALYSIS AND PRESENTATION
A1.3.1 CATEGORIZATION METHODDLLOGY
To rigorously evaluate and enhance the capability of our LINO UniPS method for reconstructing surface normals of objects that feature high-frequency geometric details on complex surfaces, we curated a dedicated set of objects exhibiting diverse geometric complexities. These objects were subsequently graded by difficulty into five distinct levels, designated Level I to Level 5. Specifically, Levels 1-4 are classified following Dora Chen et al. (2024) criterion, based on the number of salient edges Nr. Level 5, in contrast, is distinguished by the use of normal mapping in its rendering. The specific criteria for this classification
follows:

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Leve
Figure A4: The objects displayed sequentially from left right. representing Level to Level 5. Across Levels through 5, there a progressive rise geometric complexity. Specifically, Level features exceptionally complex surface geometry due to the utilization normal mapping its rendering process.
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Level 1 (Less Detail): 0 < Nr 5000;
Level 2 (Moderate Detail); 5000 < Nr S 20000;
Level 3 (Rich Detail): 20000 < Nr - 50000;
Level (Very Rich Detail): Nr 50000.
Level 5 With Normal Mapping
shows representative cases from the different defined levels. Pe-Verse comprises 100,000 scenes. For each of these scenes, two distinct renderings are typically generated: one that utilizes normal mapping incorporate fine geometric details, and another rendered without this normal mapping. Levels 1-4 consist exclusively scenes rendered without normal mapping, with each of these four levels containing 25,000 scenes. Level 5 is composed entirely of the 100,000 scenes rendered with normal mapping enhancement.
Fig. A4
A1.3.2 THE USE OF NORMAL MAPPING
To enhance PS normal recon- struction for objects character- ized by intricate, higu-freyuency details, training data rich in such geometric features is essen- tial. However, 3D models gen- uinely possessing fine-grained geometric intricacies are often scarce and prohibitively expen- sive, which impedes the creation of diverse, large-scale. high- fidelity datasets. To overcome this limitation within the Univer- sal PS framework, our work pi- oneers the integration of normal mapping directly into the dataset generation process. Normal map- ping, 3D computer graphics technique, imbues low-polygon models with the visual appear- ance of figh-frequency geomet- ric details by applying a special- ized texture, which encodes fine- scale perturbations of the surface normals. During rendering, these stored normal variations are then utilized to simulate intricate sur- face details without actually in- creasing the underlying geomet- ric complexity polygon count of the model.
Figure A5: Effect of normal mapping on rendered surface detail. The top two rows display renderings without normal mapping, while the bottom rows showcase the is evident that employing normal mapping (bottom rows) results in significantly more siequnryy surface normal detail compared to renderings without (top rows).
rendered with normal mapping.
A visual comparison of render- ings with and without the use of normal mapping is presented in Fig. A5 the figure that employing normal mapping during the rendering process yields significantly higher level of detail in the resulting surface normals.
is clearly evident from
A1.3.3 LIGHTING SETUP
When rendering PS- Verse, we use four types of light sources; (a) environment lighting, (b) directional lighting, (c) point lighting, (d) uniform background lighting.

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Tydns
WSdd
PS-Mxx
PS Uii MS-PS
OurS
Resolution: 256x256
Resolution: 512x512
Resolution: 512x512
Resolution: 128x 128
Resolution: 512x512
Figure A6: Visual comparison of different datasets. The spatial resolution of the images corresponding each column is indicated beneath
During rendering, we generate ten distinct lighting configurations by combining several base lighting components (conceptually denoted here as (a), (b), (c), and (d)). These specific configurations are as
follows: (1) Component (a), (2) Component (b), (3) Component (c), (4) Components (a) (b), (5) Components (a) + (c), (6) Components (b) + (c), (7) Components (a) + (b) + (c), (8) Components (a) (d), (9) Components (b) (d), (10) Components (a) (b) (d). The lighting setup includes: directional light, point light and uniform background lighting, which is introduced to better simulate realistic global illumination. Every scene in PS-Verse rendered as 20 images, each employing lighting setup randomly chosen from our ten predefined lighting configurations. An example such
an image set for single scene illustrated in Fig. A3
A1.3.4 COMPARISON
OTHER DATASETS
Here, we primarily compare several training datasets: CyclesPS-TI Train Ikehata (2018), PS- AS-Wild Ike hata 2022). PS-Mix Ikehata (2023), PS-Uni MS-PS Hardy al. (2024), and Perve.... For
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RGB
A Albedo
Metallic
Normal
Roughness
Figure A7: Our PS- Verse dataset contains ground truth albedo, metallic, normal, roughness.
quantitative comparison these datasets, please refer to Tab. T We now present illustrative qualitative comparisons in Fig. A6 Our comprehensive evaluation, encompassing both qualitative and quantitative aspects, leads us conclude that PS- Verse the premier training dataset in terms of quality for the Universal PS task. This emphasis on large-scale, high-quality data curation is also consistent with recent trends in dense correspondence and stereo matching Guo al, 20055 a)
A1.3.5 PBR MATERIALS
In addition to ground truth normals, our PS-Verse dataset also provides ground truth maps for albedo, metallic, and roughness, shown in Fig. A7
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nnco on
Figure A8: Post-PCA visualization features extracted by different method encoders for the CowPNG from the DiLiGennT [Shi et al. (2018). Metrics displayed to the right of each row are (CSIM/SSIM), higher values indicate higher feature similarity.
A1.4 ADDITIONAL DISCUSSION
A1.4.1 WHY FEATURE CONSISTENCY MATTERS
Universal PS aims to solve an inverse problem: recovering the intrinsic surface normal field N from a set of observations { formed under varying, unknown illumination conditions {L, / The imaging process be formally described as I J(N, p. Ly), where F represents the rendering equation and the surface reflectance.
An ideal encoder seeks to extract a unified feature representation Fenc E(14))==1) that is strictly illumination-invarian effectively marginalizing out the extrinsic variable [L/} this physical context, feature similarity (CSIM; SSIM) serves direct quantitative metric of this invariance; a low CSIM/SSIM score implies that the feature Feme retains significant dependency on (L indicating failure decouple extrinsic illumination from intrinsic geometry.
The decoder D is tasked with learning the mapping Ñ D((Z)) When features are not decoupled, the decoder confronts highly ill-posed problem: receives highly variable inputs Fene for the exact same physical geometry N, which inevitably introduces ambiguity and variance into the estimator, manifesting as higher reconstruction error (MAE).
Therefore, primary driver for our LINO UniPS the introduction of an improved encoder E. By explicitly employing Light Register Tokens supervised by Light Alignment to physically isolate variant illumination components [ our encoder ensures that the feature representation passed to the decoder remains pure and consistent. This effectively mitigates the ill ill-posed nature of the decoding task, naturally yielding higher accuracy.
While the decoder's capabilities represent non-negligible factor in overall performance, the decoders utilized in UniPS, SDM UniPS, and our LINO UniPS are architecturally similar, all adhering to the pixel-sampling paradigm. Consequently, the direct correlation between greater feature consistency and superior normal reconstruction is not an oversimpiificaton; thus, when analyzing this relationship in Fig. [ we group these three methods together to facilitate direct comparison of the impact of their respective encoder-derived features.
Fig. A8 presents Principal Component Analysis (PCA) visualizations of features extracted by the encoders of various methods, alongside their corresponding feature similarity metrics (CSIM/SSIM). In the following discussion, focus analysis Ours w/mlp variant and Uni MS-PS.
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Ours w/MLP refers to configuration of our LINO UniPS where the standard decoder is replaced by simple two-layer MLP. As illustrated in Fig.[AS A8 this variant gives the highest feature similarity. Visual inspection of the PCA plots further reveals that its extracted features have effectively disentangled lighting information. We hypothesize that the superior feature similarity of Ours w/mlp compared to the full LINO UniPS (with its original decoder) stems from the constraints imposed by the weaker MLP decoder; this limited decoder capacity compels the encoder to learn more consistent features to facilitate accurate normal reconstruction. While this enhanced feature consistency from the encoder may not entirely compensate for the reduced representational power of the simpler decoder in terms of absolute normal reconstruction quality (when compared to the full LINO UniPS), Ours w/MLP variant nevertheless significantly outperforms SDM UniPS. This finding strongly corroborates our central hypothesis regarding the critical role of powerful and well-regularized encoder in achieving effective feature disentanglement and consistency.
Uni MS-PS also demonstrates high feature similarity. However, visual analysis of its features (Fig. A8) suggests that they remain considerably entangled with lighting information. Consequently, we infer that its high reported feature similarity may be more attributable to geometric self-consistency within its representations rather than successful illumination decoupling. Despite this apparent lack of complete feature decoupling. Uni MS-PS often achieves commendable reconstruction results. We attribute this primarily to its multi-scale architecture: beyond the initial stage, each subsequent network stage in Uni MS-PS incorporates predicted normals from the preceding stage as an additional input, effectively leveraging them strong geometric prior. This iterative refinement, guided by intermediate normal predictions, places Uni MS-PS in a distinct operational paradigm compared to methods like UniPS, SDM UniPS, and our LINO UniPS.
Furthermore, we need to figure out that Uni MS-PS exhibits certain practical limitations. (a) Its multi-scale nature leads to considerable inference latency, particularly when processing multiple high-resolution input images (e.g., handling 16 images at 4K resolution can extend to about an hour). In contrast, our LINO UniPS method typically completes inference within tens of seconds for similar inputs (see Tab. 6). (b) While Uni MS-PS can reconstruct detailed surface normals, its reliance on potentially lower-resolution training datasets and its patch-based inference mechanism can lead to a loss of global contextual information, sometimes resulting in reconstructions that are locally detailed but globally inconsistent or erroneous (see Fig. and Fig.
A 11. 1.4.2 LIMITATIONS
Despite the commendable performance demonstrated by LINO UniPS, certain limitations remain, offering avenues for future research.
Firstly, the incorporation of global attention within our en- coder, while designed to enhance inter-image feature inter- action for more effective comminationnnnrmaan decoupling and successfully improving disentanglement, inevitably in- troduces additional computational burden. Consequently, key direction for future work is to explore more computa- tionally efficient mechanisms that can achieve comparable decoupling efficacy at a reduced operational cost.
Secondly, despite its strong generalization capabilities, it is important to acknowledge that the Universal PS paradigm exhibits certain inherent drawbacks compared to tradi- tional paradigms.
EXPLORE
CENPLORE
For example, traditional Calibrated PS methods benefit from explicit, known light source parameters. In con- trast, Universal PS lacks any explicit light source input (Although we employ Light Register Tokens to mitigate this, our network operates without explicit light source input during inference. However, Calibrated PS methods do utilize). Consequently, when handling near- plarrrlanar objects, the Universal PS method struggles to unambiguously distinguish whether light originates from "above" "below", resulting in the
Figure A9: For near-planar objects possess- ing intricate concave and convex surface de- tails, our LINO UniPS tends to invert the predicted surface normals. The objects are from DiLiGenT-III Wang et al. (2023)
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Figure A10: Top row: Example from the input multi-light images. reconstructed by our LINO UniPS.
Bottom row: Surface normal map
Figure A11: Results the Dil 2 dataset Ren al. 2022 matrix comparing performance where rows/columns corre- spond shapes/materials, For enhanced detail visibility, viewing electronic version in color is recommended.
reconstructed surface normals being inverted (see Fig. relatively well.
A9 whereas Calibrated PS handles this
Furthermore, the Universal PS method is data-driven, inherently relying on massive amounts of data. Conversely, traditional Calibrated and Uncalibrated PS approaches do not rely heavily large-scale training datasets.
A1.4.3 THE USE OF LARGE LANGUAGE MODELS (LLMs)
During the preparation of this manuscript, we utilized Large Language Models (LLMs), including Google's Gemini, writing assistant. The primary application of these models was for language enhancement tasks, such as improving grammar, refining phrasing for clarity, and ensuring stylistic consistency throughout the paper. It is important to note that the core scientifie contributions, experimental design, results, and analyses presented herein were conceived and executed solely by the authors. The LLMs' role was strictly limited that of sophisticated tool for polishing the language and presentation of our work.
A1.5 EXTENDED RESULTS
In this section, we present additional results to further demonstrate the capabilities of our LINO UniPS.
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Input
Predict
Input
Predict
Input
Predict
Figure A12: Real-world data with masks and corresponding LINO UniPS reconstruction results; data sourced from UniPS Ikehata (2022) and SDM UniPSakehatal (2023).
A1.5.1 PUBLIC BENCHMARKS
In this section, we present additional evaluation results on the DiLiGenT10 benchmark Ren et al. (2022). which shown in Fig. AIl For comprehensive comparison, evaluate against diverse set of five baselines spanning three categories: representative Calibrated PS method (PS-FCN Chen et al. (2018)), representative Uncalibrated PS approach (SDPS Chen al. (2019)) and three
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Universal PS methods (UniPS Ikehata (022)) SDM UniPS Ikehata (2033 and Uni MS-PS Hardy et al. (2024)). The results demonstrate that our LINO UniPS not only achieves the best performance among all Universal PS methods but also significantly surpasses the specialized Calibrated and Uncalibrated approaches. This superiority particularly pronounced for objects with challenging material properties (e.g., ACRYLIC) complex geometries (e.g., PENTAGON). In these demanding scenarios, our method's s advantage over other contemporary Universal PS techniques becomes even more evident, highlighting its robustness.
Ground Truth
Ous
Uni MS-PS
SDM UniPS
UniPS
Input
20.5
41.0"
des JArEE
Ground Truth
Ours
MS-PS
SDM CniPS
UniPS
Ingut
9.3
14.7"
20.3
38.4
Ground Truth
Ours
Uni MS-PS
SDM UniPS
UniPS
Input
20.8
Eater Magp
Figure A13: Comparison of different Universal PS methods PS-Verse Testdata, showcasing ground truth normals, reconstruction normals, and corresponding error maps. error maps depict the Mean Angular Error (MAE), measured in degrees; lower MAE values signify a more accurate reconstruction.
A1.5.2
REAL DATA SYNTHETIC BENCHMARK
Fig. A10 showcases our method's performance on challenging real-world data. To demonstrate its robustness, the figure includes mask-free examples from two distinct sources: the leftmost
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Ground Truth
Dinction
(b) Env
Point
Direction & Env Point
Predictel Nomel
Fomr Map
Figure A 14: Qualitative results of the effect of each Light Register Token type. The full three-token model (right) achieves higher visual fidelity than any single-token variant, highlighting the need for all three.
columns show 4K resolution images from the SDM UniPS real data Ikehata (2023). while the rightmost column displays our own 960 960 captures using a mobile phone.
These results demonstrate that our LINO UniPS robust variations in scale and mask-free inputs, while consistently reconstructing fine-graine grained details.
Fig. A12 presents examples of real-world captured objects, the overall quality of these reconstructions underscores our approach's 's strong generalization capabilities.
Fig. A13 presents a comparison of various Universal PS methods on our PS- Verse Testdata. Given that PS- Verse Testdata is a synthetic dataset, ground truth is readily available, facilitating precise quantitative evaluation. The results clearly demonstrate that our LINO UniPS significantly outper- forms contemporary approaches, including Uni MS-PS Hardy et al. (2024), SDM UniPS Ikehata (2023), and UniPS [ikehata 2022).
A1.6
ADDITIONAL ABLATIONS
A1.6.1 EFFECT EACH LIGHT REGISTER TOKEN TYPE
To analyze the independent contributions of each proposed Light Register Token, we designed fine-grained ablation study. this experiment, presented in the last row of Tab. 2 as our baseline. The variable is the specific Light Register Token used, along with their cor- responding light alignment losses. We eval- uated the following four setups: (a) Using Direction Tokens, (b) Using Eny Tokens, (c) Using Point Tokens. (d) Using Point & Direction & Eny Tokens (final model).
Table A2: Quantitative comparison of single-token (Direc- tion, Env, Point) performance against the full three-token combination, highlighting the Point token's 's contribution and the critical synergy of all three.
the final model
CSIMI
SSIM
Avg. MAEJ
Direction Env
0.84
0.81
5.32
0.83
0.82
5.30
Point
0.86
0.84
4.98
Direction & Env & Point
0.88
0.86
4.51
The total number of register tokens was kept at three for all setups (consistent with the final model).
All other experimental settings were identical.
Quantitative results are shown in Tab. A2 and qualitative results are in Fig. A14 We observe that: among all single-token configurations, using only the Point Tokens yields the most significant performance improvement, while the effects of the Direction and Env tokens are similar. This because the Point Tokens represent gighfrreuency illumination information (such as specular highlights), which provides the most critical and informative Ikehatal (2023) 2022). Conversely, the Direction and Env tokens correspond to low-frequency light sources, providing relatively fewer geometric cues.
for resolving surface normals
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Ground Truth
Generic
Specialized
Predicted Nomal
Figure A15: Qualitative comparison of Specialized vs. Generic Light Register Tokens. Our specialized token approach (right) exhibits superior visual fidelity.
Furthermore, we observe that a significant performance gap still exists between any single-token variant (configurations (a), (b), (c)) and the full model using all three tokens (configuration (d)). This strongly demonstrates that our design is not redundant: real-world illumination is a complex mixture, and the synergistic effect of all three tokens enables the model to handle more complex lighting conditions, thus most thoroughly decoupling the complex illumination information from the normal features.
A1.6.2 WHY SPECIALIZED TOKENS INSTEAD OF GENERIC TOKENS
We introduce three specialized Light Reg- ister Tokens to correspond to three distinct illumination types (Point, Direction, and Env). A natural question arises: why not use generic tokens instead of this special- ized design? To answer this, we conduct an ablation study. For this experiment, we use our final model (last row of Tab. (2) as the baseline. All other experimental settings re- main identical. The only change we make is to replace our three specialized Light Register Tokens with three generic tokens. These generic tokens are simultancously supervised by all three light alignment losses (Lenv. Cpoints and Ldirection)
Table A3: Quantitative comparison of Specialized Generic Light Register Tokens. Our specialized token de- sign outperforms the generic token method.
CSIM
SSIM
Avg. MAE!
Generic Specialized
0.84
0.84
5.07
0.88
0.86
4.51
Quantitative results are shown in Tab. A3 and qualitative results are in Fig. A15 We find that the performance of the generic token approach is lower than our specialized token approach. We analyze that this is because the generic tokens are forced to entangle physically disparate lighting cues (c.g., high-frreqency and low-frequency light), which consequently degrades the accuracy of the subsequent normal prediction.
A1.6.3 WIYY USING DUAL-BRANCI ARCHITECTURE
This section analyzes the advantages of our Dual-branch architecture (parallel wavelet and naive downsample branches). Quantitative results are in Tab. A4 and qualitative results are in Fig. A16 For this experiment, identical; we only modify the encoder's s feature extraction architecture to create three variants: (1) Downsample-only (2) Wavelet-only (3) Dual-branch (final model).
final model (last row of Tab. 2 as the baseline. All other settings
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Published as a conference paper at ICLR 2026
Ground Truth
Downsample-only
Wavelet-only
Dual-branch
Predicted Nammal
Entor Map
Figure A16: Qualitative comparison Dual-branch model against its bingle-branch variants, confirming the complementary effect.
From both quantitative and qualitative results, find that Wavelet-only outperforms Downsample- only. For normal reconstruction, naive downsample irreversibly discards critical geometric information. In contrast, the wavelet transform, by explicitly preserving these high- frequency components, captures finer surface details and yields performance gain.
Furthermore, Dual-branch model outper- forms all single-branch variants. Our analysis is follows: The two branches achieve functional specialization. The wavelet branch focuses on explicitly pre- serving high-frequency, fine-grat fine-grained lo- cal geometric details. The naive down- sample branch, while lossy in detail, pro- vides a smoother, more anti-aliased low- frequency representation, offering robust global image-domane
Table A4: Quantitative comparison Dual-branch model against its single-branch variants, demonstrating the quantita- tive advantage of the Dual-branch design.
CSIM
SSIM
Avg. MAE,
Downsample-only
0.80
0.79
5.98
Wavelet-only
0.85
0.84
4.97
Dual-brance
0.88
0.86
4.51
semantic informa-
tion Glasner et al. (2009); Zhang (2019); Liu al. (2024); Yang et al. (2024). The advantages
of both branches are complementary Guo et al. (2017 7): [Zengg et (2023). and their fusion allows the model to achieve superior accuracy; therefore, despite the low- freeuueyyncy component already included the wavelet transform, we additionally retain the parallel naive downsample branch.
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Reviews:
[{"review_id": "HkPMrYpgsf", "reviewer": "Reviewer_N2Ck", "summary": "In this paper, the authors introduce a ViT-based framework, named LINO UniPS, for universal photometric stereo. They target at tackling two major challeneges in universal photometric stereo, namely 1) ineffective decoupling of illumination and normal cues and 2) loss of geometry details. Specifically, they introduce Light Register Tokens and an Interleaved Attention Block to explicitly decouple illumination from normal features, yielding a unified feature representation. They also adopt a Wavelet-based Dual-branch Architecture and a Normal-gradient Perception Loss that substantially improve the reconstruction of fine-grained geometric details. Further, they build a synthetic dataset, named PS-Verse, graded with surface complexity and lighting diversity to support similar research.", "questions": "- Theoretically, the wavelet transform already includes a downsampled version of the image. Why is it necessary to include a naive downsampling branch in parallel?  \n- Three Light Register Tokens are introduced specifically for three illumination types, namely point, direction, and environment lights. Have the authors consider using a single generic light register token instead? Does this design generalize well to other light representations?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "+ The introduction of three types of Light Register Tokens for aggregration of illumination information of three different illumination types sounds logical and novel. It helps improve the decoupling of illumination from normal features. Its effectiveness has been demonstrarted in ablation study.\n+ The proposed light alignment supervision sounds logical and novel. It helps Light Register Tokens learn to capture the respective illumination information. Its effectiveness has been demonstrated in ablation study.\n+ The Interleaved Attention Block introduces a global cross-image attention mechanism. Although global attention is not a novel idea, but the four interleaved attention layers allow aggregrating information across multiple hierarchical levels and help better decoupling illumination and normal features. The effectiveness of the Interleaved Attention Block has been demonstrated in ablation study.\n+ The Wavelet-based Dual-Branch Architecture sounds logical and novel. It helps preserve details throughout the network. Its effectiveness has been demonstrated in ablation study.\n+ The Normal Gradient Perception Loss sounds logical and novel. It helps to enhance high-frequency areas. Its effectiveness has been demonstrated in ablation study.\n+ The large-scale synthetic dataset PS-Verse can benefit further research in universal photometric stereo.\n+ SOTA results have been reported on DiLiGenT and Luces datasets.", "weaknesses": "- The figures and captions need further improvement. For instance, the pipeline in fig. 2 is rather complicated and difficult to understand. It does not match well with the detailed description of the modules. What do the different colors represent? In fig. 3, it is not clear how to interpret the attention maps for the different Light Register Tokens. More detailed discussions are needed to better understand how the figures demonstrate the effectiveness of the different Light Register Tokens.    \n- The concept of inter-image and intra-image context in this paper is rather confusing. In lines 228-229, it mentioned that frame attention captures inter-image context. Should the frame attention capture intra-image (within-image) context while the global attention capture inter-image (between-images) context instead? Discussions in the Appendix also show the same confusion.\n- The ablation study has demonstrated the effectiveness of each proposed component quantitatively. It would, however, be also important to show the corresponding qualitative results to enable readers to visually perceive the effecti of each component.\n- Even with the details provided in the Appendix, it is not sufficient to reproduce the results. For instance, in lines 707-714, the Light Register Tokens have a dimension of C, but they are concatenated with the wavelet/downsample tokens with a dimension of D. In lines 751-755, should the \"1 - \" goes inside the summation sign? Otherwsie, what is the purpose of including \"1\" in the loss? In lines 808-814, the steps in transformating the aggregated features into the four-level feature pyramid is not very clear.", "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": "SJSn7dH0wh", "reviewer": "Reviewer_7ywv", "summary": "The paper proposes a new encoder to address two issues in Photometric Stereo (PS): (1) the coupling between normal and lighting information, and (2) the loss of high-frequency details. To tackle the first problem, the authors introduce Light Register Tokens and an Interleaved Attention Block to decouple normal and lighting features. Furthermore, they employ a wavelet-based dual-branch structure combined with specific loss to preserve high-frequency details.", "questions": "See above", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The proposed method demonstrates solid performance on both real and synthetic datasets. The ablation studies are comprehensive and well-conducted.", "weaknesses": "- Line 64 seems quite contradictory, should it be normal features instead?\n- Could the authors provide a more physically grounded explanation for explaining the effect of feature similarity? The current discussion mainly relies on experiments illustration.\n- Could the Light Token also be used for light source estimation? If so, how accurate would it be?\n- It would be much better to discuss and acknowledge PS work under general setup.", "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": "mnvr8qBVKN", "reviewer": "Reviewer_zyYx", "summary": "The paper proposes LINO UniPS, a method for universal photometric stereo. It first introduces light register tokens for different types of lightings and interleaved attention block to enable better separation of lighting and lighting-invariant normal in the encoder. Then it uses a Wavelet-based Dual-branch architecture and a normal-gradient perception loss to recover the the fine details of the scenes. Furthermore, it introduces a new dataset, PS-Verse, with more complicated surface and lightings for the photometric stereo task.", "questions": "1. The training starts from PS-Verse Level 1 data to level 4 data, which don't have ground-truth normal maps, I wonder how do you compute $L_n$ for these data?\n\n2. For the feature similarity metric (CISM), what are the two features to compare with?\n\n3. In table 4, for POT2, SDM-UniPS seems to achieve better results?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The proposed method achieves good results on two benchmark datasets, DiLiGenT and LUCES.\n\n2. The paper introduces a new dataset, PS-Verse, which is proved to be able to help achieve better performance for the same method from Table 2.", "weaknesses": "1. The clarity of the paper could be further improved. In general I can understand the idea of the paper but there are several key aspects that I am confused with. Please see Questions section below. Also there are not metrics introduction for table 4 and table 5 in their table descriptions.\n\n2. The test scenes only have one object per-scene. I wonder if the method can handle more complicated scenes?\n\n3. My biggest concern is the comparison results with other methods. \n\n(1) In Uni MS-PS (https://hal.science/hal-04431103v2/file/main_hal.pdf) Table 3, Uni MS-PS can achieve 6.04 when using 30 images for Buddha, which is better than LINO UniPS and not shown in Table 4. For POT1, Uni MS-PS can achieve 4.08 which is very similar to LINO UniPS. For COW, best Uni MS-PS is the same as LINO UniPS and also not shown in table 4.\n\n(2) The same is for LUCES dataset, not the best results of Uni MS-PS are reported in table 5, e.g. for BOWL, BUDDHA.", "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": "GQx8XqeQkk", "reviewer": "Reviewer_nvYr", "summary": "This paper proposes LINO UniPS, a transformer-based framework for Universal Photometric Stereo. It recovers surface normal maps from multiple images under unknown lighting conditions. They propose the Light Register Tokens and the Interleaved Attention Block to allow the model to understand normal and light conditions separately. Also, they propose the Wavelet-based Dual-branch Architecture and a normal-gradient perception loss to preserve high-frequency geometric details. They demonstrated their methods on PS-Verse, a new, high-quality, large-scale synthetic dataset they created.", "questions": "- Looking at the Light Registered Attention module in Figure 2, it seems like frame tokens are copied and utilized for each of the three light registers. Am I understanding this correctly? This part seems like a huge computational burden. Why did you bother calculating the three separately?\n- You claim that each of your methods solves the decomposing and high-frequency problems, but can you show qualitative ablation, not just performance improvement?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The explicit light-feature decoupling through LRTs and interleaved attention is well justified and ablated.\n- Consistent performance improvements have been achieved, including in the benchmarks presented.\n- The paper is well-organized.", "weaknesses": "- Some ablations (e.g., the effect of each light type token separately) could be presented in more detail. \n- There are no qualitative results for ablation studies.", "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)