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REVIEW 4 major objections 5 minor 58 references

MLI-NeRF: Multi-Light Intrinsic-Aware Neural Radiance Fields

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Multiple light positions teach a neural radiance field to separate reflectance from shading without ground-truth intrinsic images, using physics-based pseudo labels.

desk verdict Genuinely new multi-light pseudo-label approach with impressive synthetic results; the real-world claim rests on a direct-Lambertian assumption that is not yet tested quantitatively. read the letter →

arxiv 2411.17235 v1 pith:DCUIRPL5 submitted 2024-11-26 cs.CV

Yixiong Yang , Shilin Hu , Haoyu Wu , Ramon Baldrich , Dimitris Samaras , Maria Vanrell This is my paper · ORCID
classification cs.CV
keywords neuralradiancefieldsintrinsicimagedecompositionreflectanceshadingmulti-lightrelightingpseudo-labelgenerationrenderingsceneediting
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that the ill-posed problem of intrinsic image decomposition can be solved by using a neural radiance field that sees the scene under several known light positions. The core claim is that the extra lighting information substitutes for ground-truth intrinsic images: the method generates physics-based pseudo reflectance and shading labels from a relightable NeRF, then retrains the network with dedicated reflectance and shading modules supervised by those labels. If this works, intrinsic decomposition becomes feasible on real full scenes with backgrounds and cast shadows, not just isolated synthetic objects. The paper reports state-of-the-art results across synthetic, real-object, and real-scene datasets, and demonstrates editing applications such as relighting, reflectance editing, and shading editing.

What carries the argument

The central machinery is physics-based pseudo label generation: use the SDF geometry to compute normals and sphere-traced visibility, convert them into a Lambertian pseudo shading $S^* = (\max(\vec{N}\cdot\vec{L},0) \otimes V)^\gamma$, and then derive pseudo reflectance by dividing each observed image by its pseudo shading and merging multiple lighting observations with K-means. This replaces statistical priors with a direct geometric-optical constraint; the labels then supervise dedicated reflectance and shading MLPs in Stage 2.

What would settle it

A concrete test that would falsify the central claim: run MLI-NeRF on a real scene containing a visible glossy or mirror-like surface under the multi-light capture setup, and check whether the predicted reflectance contains specular highlights or color from the environment, and whether the residual term remains large. If the residual absorbs a large fraction of the image energy or the reflectance inherits lighting-dependent colors, the direct-Lambertian pseudo-shading assumption fails.

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Extended reading notes

Core claim

MLI-NeRF claims that multiple light positions, combined with multi-view geometry, provide enough information to disentangle reflectance and shading without ground-truth intrinsic data. In Stage 1, a Neuralangelo-style SDF network takes light position as an extra input, producing a relightable scene representation. Post-processing then derives pseudo shading from the SDF normal and sphere-traced light visibility as $S^* = (\max(\vec{N}\cdot\vec{L},0)\otimes V)^\gamma$, and pseudo reflectance by per-light division $R = I \oslash S$ followed by a K-means merge across lights with hole filling. In Stage 2, separate MLPs predict reflectance (independent of light and view) and shading (conditioned on light position), supervised by these labels with reliability weight maps, while the residual term $Re$ is regularized toward zero so the image is explained by reflectance and shading. The paper's experiments on synthetic, real-object, and ReNe full-scene datasets report that this approach outperforms prior intrinsic decomposition and inverse rendering methods in reflectance and shading metrics, and performs competitively in relighting.

Load-bearing premise

The load-bearing premise is that real scene shading can be modeled as direct Lambertian illumination, with all indirect light and specular effects relegated to a residual term that regularization can suppress.

Editorial extensions

If this is right

  • Intrinsic decomposition can be performed on real full scenes with backgrounds and cast shadows, where statistical-prior methods and inverse rendering methods struggle.
  • Relighting and intrinsic decomposition can be learned in a single NeRF pipeline from multi-light captures without requiring ground-truth reflectance maps.
  • A small number of light positions, around four, is sufficient to approach the quality achieved with many random light positions in the tested scenes.
  • The learned decomposition enables editing applications such as reflectance editing, relighting, and shading editing on scenes from diverse datasets.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The pseudo-shading formula assumes direct Lambertian illumination only; real interreflections and specular effects are pushed into the residual term, which the Stage 2 regularization tries to minimize, potentially corrupting estimated reflectance in scenes with significant indirect light. This is an inference about the method's behavior beyond the paper's own claims.
  • Because Stage 1 is trained on known light positions, the same pseudo-label generation could in principle be applied to any relightable NeRF backbone, not just the Neuralangelo base; the paper notes the method is applicable to 'different similar baselines'.
  • A testable extension would be to replace the K-means merge of pseudo reflectances with a learned fusion or a robust median that accounts for specular highlights, which might improve performance on scenes with glossy materials.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes MLI-NeRF, a two-stage neural radiance field method for intrinsic image decomposition (reflectance, shading, residual) that uses images captured under multiple known light positions. Stage 1 trains a relightable Neuralangelo-style SDF network conditioned on light position. Post-processing derives pseudo-shading from normals, light visibility (via sphere tracing), and a gamma-corrected Lambertian term, and pseudo-reflectance by dividing the input image by that pseudo-shading and merging across lights with K-means and hole-filling. Stage 2 freezes the geometry and adds reflectance and shading MLPs, supervised by the pseudo-labels with weight maps and a residual regularization term. The authors evaluate on a synthetic Blender dataset, a real object relighting dataset, and the real ReNe dataset, reporting strong quantitative results on synthetic intrinsic decomposition and NVS/relighting, and qualitative results on real scenes, plus editing applications.

Significance. If the pseudo-label strategy is robust, MLI-NeRF would be a meaningful advance: it replaces statistical priors for intrinsic decomposition with multi-light physics-based supervision inside a NeRF, and it targets full scenes rather than isolated objects. The paper includes a public code release, re-rendered synthetic data with ground truth intrinsic images, and comparisons against several baselines across multiple light settings. The main significance is therefore conditional on whether the direct-Lambertian pseudo-shading model is accurate enough for real scenes and whether the reported synthetic gains transfer beyond the same rendering protocol.

major comments (4)
  1. [§3.3, Eq. (5), Step C] The pseudo-reflectance label is computed as R* = I ⊘ S* while explicitly ignoring the residual term in Eq. (1). Under the paper's own model I = R⊗S + Re, this yields R* = R + Re/S*, so any specular highlight, interreflection, or penumbra that the direct-Lambertian shading model (Eq. 5) does not capture is baked into the pseudo-reflectance label. The synthetic evaluation in Table 1 uses scenes rendered under the same direct-light, gamma-corrected protocol, so it does not test this contamination, and the ReNe experiments in Sec. 4.6 report only qualitative intrinsic results. To support the claimed robustness on real-world data, the authors should provide either quantitative intrinsic evaluation on real scenes with measured or known albedo (e.g., OpenIllumination-style objects) or a controlled synthetic experiment with indirect illumination and specular materials, showing that the pseudo-labels and final decomposition remain accurate when Re is nonzero.
  2. [§3.3, Step C and §3.4, Eq. (7)] The pseudo-label generation pipeline relies on several under-specified heuristics: the gamma exponent γ in Eq. (5), the number of K-means clusters and initialization used for the pixel-level merge, the distance weights in the hole-filling strategy, and the construction of the weight maps W_R and W_S. These are load-bearing because they directly determine the supervision targets in Eq. (7), yet no values, sensitivity analysis, or ablation are reported. The authors should state all hyperparameters and provide an ablation showing how pseudo-label quality and final decomposition metrics vary with these choices, at least for the number of lights used in the merge and the gamma value.
  3. [§3.4, Stage 2] There is a self-distillation aspect to the training: the pseudo-shading S* is computed from the Stage-1 SDF normals and visibility, and Stage 2 trains a shading MLP on the same frozen geometry to reproduce S*. The pseudo-reflectance is then I ⊘ S*, so Stage 2 largely learns a function of Stage 1's geometry rather than being independently supervised. The paper does not report how accurate the pseudo-labels themselves are on the synthetic dataset (e.g., PSNR/LPIPS of S* and R* against the re-rendered ground truth), nor whether Stage 2 actually improves over the pseudo-labels. Adding these numbers would clarify how much of the final performance comes from the pseudo-label generation versus the Stage-2 network and would strengthen the claim that the decomposition is genuinely learned rather than inherited from the geometry.
  4. [§4.6, Fig. 6 and Table 3] The ReNe experiments are the primary evidence for real-world full-scene performance, but the intrinsic decomposition results there are qualitative only; Table 3 reports NVS metrics and does not include reflectance or shading error. Since the test set is not public and the validation set is used for inference, the authors should at least provide a quantitative intrinsic evaluation on a publicly available real multi-light dataset or on a held-out portion of ReNe with manual annotation, if available. As written, the claim of 'superior performance in intrinsic decomposition on real scenes' is not backed by numbers.
minor comments (5)
  1. [§4.2, 'Additional Lighting Setups'] In the multiple-light track, TensoIR is run with three lights while MLI-NeRF uses four, because TensoIR 'occasionally failed' with four; this should be stated as a limitation in the comparison fairness rather than only in the text.
  2. [§3.3, Eq. (5)] The paper calls γ 'gamma correction' but does not specify whether it is a fixed constant (e.g., 2.2) or a learned/estimated value; please clarify and justify the choice.
  3. [§4.3 and §3.2] The loss weights weik, wcurv, wintrinsic, wreg are listed with values, but there is no discussion of how they were chosen or whether results are sensitive to them; a brief note or supplementary ablation would help.
  4. [Fig. 2 and Fig. 6 captions] Some labels are inconsistent: 'NRHInts' appears in Fig. 5 and the supplementary, and 'MLPref lectance' appears in Sec. 3.4; please unify the notation.
  5. [Table 2] The NVS/relighting comparison in the Random setting reports slightly lower PSNR/SSIM for MLI-NeRF than NRHints but better LPIPS; the text says 'comparable performance,' which is fair, but the shading and reflectance errors are not reported there, so the intrinsic decomposition contribution to relighting quality is not visible.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: pseudo-labels are constructed from a forward physics model and the method is validated against external ground-truth benchmarks.

full rationale

The claimed derivation chain is feed-forward rather than circular. Stage 1 fits a multi-light neural scene representation from images; post-processing then evaluates explicit, non-learned formulas: Eq. (5) computes pseudo-shading S* from Stage-1 normals, light direction, visibility, and gamma correction, while Step C computes pseudo-reflectance R* = I / S* from the image and shading. These labels are not defined in terms of the Stage-2 networks, and Stage 2's reflectance and shading MLPs are trained to match them through Eq. (7), with the geometry network frozen. The fact that the Stage-2 shading MLP shares the frozen SDF features used to generate S* is a self-distillation property, not a circular reduction: neither equation sets the prediction equal to its own input, and the label construction includes an independent forward visibility/shading computation constrained by the multi-light images. The central claim is also externally falsifiable: the synthetic experiments compare against ground truth rendered in Blender and against multiple prior methods (PIE-Net, Ordinal, InvRender, TensoIR, IntrinsicNeRF), so the reported improvement is not forced by construction. The concern that indirect/specular light may be absorbed into the residual term is a modeling-validity risk for real scenes, not a circularity in the derivation, and the paper's own limitation section acknowledges the reliance on known light positions rather than concealing it.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The pipeline rests on several domain assumptions about the illumination model and the reliability of pseudo labels, plus hand-chosen hyperparameters. No new physical entities are introduced.

free parameters (4)
  • Loss weights (weik, wcurv, wintrinsic, wreg) = 0.1, 5e-4, 1.0, 1.0
    Chosen by hand; no sensitivity analysis is provided.
  • Gamma correction exponent in pseudo-shading (Eq. 5) = Not stated
    Applied to adapt pseudo-shading to sensor gamma; the value is not reported, making the pseudo-shading pipeline under-specified.
  • Number of light positions sampled for pseudo-reflectance merge = 4 for non-grid-sampled setups
    The paper states 'four light positions are randomly selected, which our experiments indicate is sufficient' but provides no ablation.
  • Weight-map parameters for W_R and W_S = Not stated
    Defined via pseudo-shading edges and visibility, but the functional form and thresholds are not specified.
assumptions (5)
  • domain assumption Lambert's cosine law (S = N·L) as the shading model
    Used in Eq. (2)/(5); assumes pure diffuse reflectance with no specular or indirect contributions in the shading term.
  • domain assumption Binary light visibility V from sphere tracing
    Assumes hard shadows, no penumbra or diffuse interreflections, and that sphere tracing gives accurate visibility.
  • domain assumption Scene geometry from Stage 1 (Neuralangelo) is accurate enough for pseudo-label generation
    The pseudo-labels are generated from the Stage 1 SDF normals and depth; errors in geometry propagate to labels.
  • domain assumption The residual term Re captures all non-diffuse and indirect effects
    Eq. (1) defines I = R*S + Re; the paper trains Re with L1 regularization to be small, effectively forcing most of the image into R and S.
  • domain assumption Images follow the same gamma correction as assumed in Eq. (5)
    Sensor gamma is assumed known and uniform; no calibration is described.

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Cite this review

Pith. "Pith review of MLI-NeRF: Multi-Light Intrinsic-Aware Neural Radiance Fields." pith.science (2026). https://pith.science/paper/DCUIRPL5

@misc{pith2026241117235,
  author       = {Pith},
  title        = {Pith review of: MLI-NeRF: Multi-Light Intrinsic-Aware Neural Radiance Fields},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DCUIRPL5}},
  note         = {Machine review of arXiv:2411.17235}
}
read the original abstract

Current methods for extracting intrinsic image components, such as reflectance and shading, primarily rely on statistical priors. These methods focus mainly on simple synthetic scenes and isolated objects and struggle to perform well on challenging real-world data. To address this issue, we propose MLI-NeRF, which integrates \textbf{M}ultiple \textbf{L}ight information in \textbf{I}ntrinsic-aware \textbf{Ne}ural \textbf{R}adiance \textbf{F}ields. By leveraging scene information provided by different light source positions complementing the multi-view information, we generate pseudo-label images for reflectance and shading to guide intrinsic image decomposition without the need for ground truth data. Our method introduces straightforward supervision for intrinsic component separation and ensures robustness across diverse scene types. We validate our approach on both synthetic and real-world datasets, outperforming existing state-of-the-art methods. Additionally, we demonstrate its applicability to various image editing tasks. The code and data are publicly available.

Figures

Figures reproduced from arXiv: 2411.17235 by the authors.

Figure 1
Figure 1. Given real-world images from ReNe dataset [ [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Illustration of the Framework. In Stage 1, we introduce light position as input to extend NeRF for multi-light implicit representation (top left). Following Stage 1, three post-processing steps are applied to generate pseudo labels for reflectance and shading using the proposed physics-based pipeline (right). In Stage 2, we train the intrinsic-aware NeRF based on the model from Stage 1 and the pseudo labels from pos… view at source ↗
Figure 3
Figure 3. Illustration of the pseudo reflectance generation process in the post-processing. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (15 more)
Figure 4
Figure 4. Figure 4: Qualitative Results on the Synthetic Dataset with all settings. The same GT reflectance applies across all settings, but GT shading differs due to varying light positions. Here for brevity, only the shading under the Random Lights setting is shown. Compared to other me…
Figure 5
Figure 5. Figure 5: Qualitative Results on the Real Object Dataset. (a) Our method compared with NRHints for the rendered image, and PIE-Net and Ordinal for intrinsic decomposition. (b) Our reflectance estimation for two different scenes, with zoomed-in views on the object hole and cast s…
Figure 6
Figure 6. Figure 6: Qualitative Results on the ReNe Dataset. We show the reflectance estimation for a reference view across all settings. mance; however, our method produces meaningful results in the first and third scenes. The other neural render￾ing methods, TensoIR[18] and IntrinsicNeR…
Figure 7
Figure 7. Figure 7: Additional Qualitative Results on the Synthetic Dataset. (Hotdog) [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Additional Qualitative Results on the Synthetic Dataset (FurBall). However, it is worth noting that in the GT, the shading of the [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: Additional Qualitative Results on the Synthetic Dataset. (Drums) [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: Additional Qualitative Results on the Synthetic Dataset. (Lego) [PITH_FULL_IMAGE:figures/full_fig_p016_10.png]
Figure 11
Figure 11. Figure 11: Additional Qualitative Results on the Real Object Dataset. [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: Reflectance and Shading estimation by our method for different points of view of the same scene on the Real Object Dataset. [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]
Figure 13
Figure 13. Figure 13: Reflectance Estimation details for different Scenes of the Real Object Dataset. [PITH_FULL_IMAGE:figures/full_fig_p019_13.png]
Figure 14
Figure 14. Figure 14: Shading Estimation details for different Scenes of the Real Object Dataset. [PITH_FULL_IMAGE:figures/full_fig_p019_14.png]
Figure 15
Figure 15. Figure 15: Additional Qualitative Results on the ReNe dataset. (Apple). [PITH_FULL_IMAGE:figures/full_fig_p020_15.png]
Figure 16
Figure 16. Figure 16: Additional Qualitative Results on the ReNe dataset. (Cube). [PITH_FULL_IMAGE:figures/full_fig_p021_16.png]
Figure 17
Figure 17. Figure 17: Additional Qualitative Results on the ReNe dataset. (Garden). [PITH_FULL_IMAGE:figures/full_fig_p022_17.png]
Figure 18
Figure 18. Figure 18: Additional Qualitative Results on the ReNe dataset. (savannah). [PITH_FULL_IMAGE:figures/full_fig_p023_18.png]

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Reference graph

Works this paper leans on

58 extracted references · 48 canonical work pages

  1. [1]

    Shape, illumination, and reflectance from shading

    Jonathan T Barron and Jitendra Malik. Shape, illumination, and reflectance from shading. IEEE transactions on pattern analysis and machine intelligence , 37(8):1670–1687, 2014. 2

    2014

  2. [2]

    Re- covering intrinsic scene characteristics

    Harry Barrow, J Tenenbaum, A Hanson, and E Riseman. Re- covering intrinsic scene characteristics. Comput. Vis. Syst, 2 (3-26):2, 1978. 1, 2, 3

    work page 1978

  3. [3]

    Nerd: Neural reflectance decomposition from image collections

    Mark Boss, Raphael Braun, Varun Jampani, Jonathan T Bar- ron, Ce Liu, and Hendrik Lensch. Nerd: Neural reflectance decomposition from image collections. In Proceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 12684–12694, 2021. 2

    2021

  4. [4]

    Barron, Hendrik P.A

    Mark Boss, Andreas Engelhardt, Abhishek Kar, Yuanzhen Li, Deqing Sun, Jonathan T. Barron, Hendrik P.A. Lensch, and Varun Jampani. SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collec- tions. In Advances in Neural Information Processing Sys- tems (NeurIPS), 2022. 2

    work page 2022

  5. [5]

    Physically-based shading at disney

    Brent Burley and Walt Disney Animation Studios. Physically-based shading at disney. In Acm Siggraph , pages 1–7. vol. 2012, 2012. 1

    work page 2012

  6. [6]

    Intrinsic image decomposi- tion via ordinal shading

    Chris Careaga and Ya ˘gız Aksoy. Intrinsic image decomposi- tion via ordinal shading. ACM Trans. Graph., 2023. 1, 2, 3, 5, 7, 8

    work page 2023

  7. [7]

    Fast sun-aligned outdoor scene relight- ing based on tensorf

    Yeonjin Chang, Yearim Kim, Seunghyeon Seo, Jung Yi, and Nojun Kwak. Fast sun-aligned outdoor scene relight- ing based on tensorf. In Proceedings of the IEEE/CVF Win- ter Conference on Applications of Computer Vision , pages 3626–3636, 2024. 2, 8

    work page 2024

  8. [8]

    L-tracing: Fast light visibility estimation on neural surfaces by sphere tracing

    Ziyu Chen, Chenjing Ding, Jianfei Guo, Dongliang Wang, Yikang Li, Xuan Xiao, Wei Wu, and Li Song. L-tracing: Fast light visibility estimation on neural surfaces by sphere tracing. In Proceedings of the European Conference on Com- puter Vision (ECCV), 2022. 2, 3

    work page 2022

Show all 58 references
  1. [9]

    Pie-net: Pho- tometric invariant edge guided network for intrinsic image decomposition

    Partha Das, Sezer Karaoglu, and Theo Gevers. Pie-net: Pho- tometric invariant edge guided network for intrinsic image decomposition. In IEEE Conference on Computer Vision and Pattern Recognition, (CVPR), 2022. 1, 2, 5, 7, 8

    2022

  2. [10]

    Deep neural models for illumination estimation and relight- ing: A survey

    Farshad Einabadi, Jean-Yves Guillemaut, and Adrian Hilton. Deep neural models for illumination estimation and relight- ing: A survey. In Computer Graphics Forum, pages 315–

  3. [11]

    Ntire 2021 depth guided image relighting chal- lenge

    Majed El Helou, Ruofan Zhou, Sabine Susstrunk, and Radu Timofte. Ntire 2021 depth guided image relighting chal- lenge. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 566–577, 2021. 2

    2021

  4. [12]

    Revisiting deep intrinsic image decompositions

    Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, and David Wipf. Revisiting deep intrinsic image decompositions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8944–8952, 2018. 3

    2018

  5. [13]

    Deferred neural lighting: free-viewpoint re- lighting from unstructured photographs

    Duan Gao, Guojun Chen, Yue Dong, Pieter Peers, Kun Xu, and Xin Tong. Deferred neural lighting: free-viewpoint re- lighting from unstructured photographs. ACM Transactions on Graphics (TOG), 39(6):258, 2020. 2, 5, 1

    2020

  6. [14]

    A survey on intrinsic images: Delv- ing deep into lambert and beyond

    Elena Garces, Carlos Rodriguez-Pardo, Dan Casas, and Jorge Lopez-Moreno. A survey on intrinsic images: Delv- ing deep into lambert and beyond. International Journal of Computer Vision, 130(3):836–868, 2022. 1, 2, 3

    2022

  7. [15]

    Implicit geometric regularization for learning shapes

    Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman. Implicit geometric regularization for learning shapes. In Proceedings of the 37th International Conference on Machine Learning, pages 3789–3799, 2020. 3

    2020

  8. [16]

    Aim 2020: Scene relighting and illumination estimation challenge

    Majed El Helou, Ruofan Zhou, Sabine S ¨usstrunk, Radu Tim- ofte, Mahmoud Afifi, Michael S Brown, Kele Xu, Hengxing Cai, Yuzhong Liu, Li-Wen Wang, et al. Aim 2020: Scene relighting and illumination estimation challenge. arXiv preprint arXiv:2009.12798, 2020. 2

    2020 arXiv

  9. [17]

    Face relighting with geometrically consis- tent shadows

    Andrew Hou, Michel Sarkis, Ning Bi, Yiying Tong, and Xiaoming Liu. Face relighting with geometrically consis- tent shadows. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4217– 4226, 2022. 2

    2022

  10. [18]

    Tensoir: Tensorial inverse rendering

    Haian Jin, Isabella Liu, Peijia Xu, Xiaoshuai Zhang, Song- fang Han, Sai Bi, Xiaowei Zhou, Zexiang Xu, and Hao Su. Tensoir: Tensorial inverse rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 1, 5, 6, 8

    2023

  11. [19]

    Lightit: Illumination modeling and control for diffusion models

    Peter Kocsis, Julien Philip, Kalyan Sunkavalli, Matthias Nießner, and Yannick Hold-Geoffroy. Lightit: Illumination modeling and control for diffusion models. In CVPR, 2024. 2

    2024

  12. [20]

    Un- supervised deep single-image intrinsic decomposition using illumination-varying image sequences

    Louis Lettry, Kenneth Vanhoey, and Luc Van Gool. Un- supervised deep single-image intrinsic decomposition using illumination-varying image sequences. In Computer Graph- ics Forum, pages 409–419. Wiley Online Library, 2018. 2

    2018

  13. [21]

    Learning intrinsic image de- composition from watching the world

    Zhengqi Li and Noah Snavely. Learning intrinsic image de- composition from watching the world. In Computer Vision and Pattern Recognition (CVPR), 2018. 2

    2018

  14. [22]

    Cgintrinsics: Better intrinsic image decomposition through physically-based rendering

    Zhengqi Li and Noah Snavely. Cgintrinsics: Better intrinsic image decomposition through physically-based rendering. In European Conference on Computer Vision (ECCV), 2018. 2

    2018

  15. [23]

    Learning intrinsic image de- composition from watching the world

    Zhengqi Li and Noah Snavely. Learning intrinsic image de- composition from watching the world. In Proceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 9039–9048, 2018. 2

    2018

  16. [24]

    Neuralangelo: High-fidelity neural surface reconstruction

    Zhaoshuo Li, Thomas M ¨uller, Alex Evans, Russell H Tay- lor, Mathias Unberath, Ming-Yu Liu, and Chen-Hsuan Lin. Neuralangelo: High-fidelity neural surface reconstruction. In IEEE Conference on Computer Vision and Pattern Recogni- tion (CVPR), 2023. 1, 3, 6, 8

    2023

  17. [25]

    Shadowneus: Neural sdf reconstruction by shadow ray supervision, 2022

    Jingwang Ling, Zhibo Wang, and Feng Xu. Shadowneus: Neural sdf reconstruction by shadow ray supervision, 2022. 1, 8 9

    2022

  18. [26]

    Openillumination: A multi-illumination dataset for inverse rendering evaluation on real objects

    Isabella Liu, Linghao Chen, Ziyang Fu, Liwen Wu, Haian Jin, Zhong Li, Chin Ming Ryan Wong, Yi Xu, Ravi Ra- mamoorthi, Zexiang Xu, and Hao Su. Openillumination: A multi-illumination dataset for inverse rendering evaluation on real objects. In Advances in Neural Information Proc...

  19. [27]

    Unsuper- vised learning for intrinsic image decomposition from a sin- gle image

    Yunfei Liu, Yu Li, Shaodi You, and Feng Lu. Unsuper- vised learning for intrinsic image decomposition from a sin- gle image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 3248– 3257, 2020. 2

    2020

  20. [28]

    Decoupled weight de- cay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight de- cay regularization. In International Conference on Learning Representations, 2018. 6

    2018

  21. [29]

    Nerf: Representing scenes as neural radiance fields for view syn- thesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis. Communications of the ACM, 65(1):99–106, 2021. 1, 2, 5

    2021

  22. [30]

    Instant neural graphics primitives with a multires- olution hash encoding

    Thomas M ¨uller, Alex Evans, Christoph Schied, and Alexan- der Keller. Instant neural graphics primitives with a multires- olution hash encoding. ACM Trans. Graph. , 41(4):102:1– 102:15, 2022. 3

    2022

  23. [31]

    A dataset of multi-illumination images in the wild

    Lukas Murmann, Michael Gharbi, Miika Aittala, and Fredo Durand. A dataset of multi-illumination images in the wild. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4080–4089, 2019. 2

    2019

  24. [32]

    Learning physics-guided face re- lighting under directional light

    Thomas Nestmeyer, Jean-Franc ¸ois Lalonde, Iain Matthews, and Andreas Lehrmann. Learning physics-guided face re- lighting under directional light. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5124–5133, 2020. 2

    2020

  25. [33]

    Total relighting: learning to relight portraits for background replacement

    Rohit Pandey, Sergio Orts-Escolano, Chloe Legendre, Chris- tian Haene, Sofien Bouaziz, Christoph Rhemann, Paul E De- bevec, and Sean Ryan Fanello. Total relighting: learning to relight portraits for background replacement. ACM Trans. Graph., 40:43–1, 2021. 2

    2021

  26. [34]

    Pedregosa, G

    F. Pedregosa, G. Varoquaux, A. Gramfort, V . Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V . Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learni...

    2011

  27. [35]

    Wdrn: A wavelet decomposed relightnet for image relighting.arXiv preprint arXiv:2009.06678, 2020

    Densen Puthussery, Melvin Kuriakose, Jiji C V , et al. Wdrn: A wavelet decomposed relightnet for image relighting.arXiv preprint arXiv:2009.06678, 2020. 2

    2009 arXiv

  28. [36]

    Nerf for outdoor scene relighting

    Viktor Rudnev, Mohamed Elgharib, William Smith, Lingjie Liu, Vladislav Golyanik, and Christian Theobalt. Nerf for outdoor scene relighting. In European Conference on Com- puter Vision (ECCV), 2022. 2

    2022

  29. [37]

    Nerv: Neural reflectance and visibility fields for relighting and view synthesis

    Pratul P Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, and Jonathan T Barron. Nerv: Neural reflectance and visibility fields for relighting and view synthesis. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pag...

    2021

  30. [38]

    Single image portrait relighting

    Tiancheng Sun, Jonathan T Barron, Yun-Ta Tsai, Zexiang Xu, Xueming Yu, Graham Fyffe, Christoph Rhemann, Jay Busch, Paul E Debevec, and Ravi Ramamoorthi. Single image portrait relighting. ACM Trans. Graph., 38(4):79–1,

  31. [39]

    Relight my nerf: A dataset for novel view synthesis and relighting of real world objects

    Marco Toschi, Riccardo De Matteo, Riccardo Spezialetti, Daniele De Gregorio, Luigi Di Stefano, and Samuele Salti. Relight my nerf: A dataset for novel view synthesis and relighting of real world objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ...

    2023

  32. [40]

    Deep relighting networks for image light source manipulation

    Li-Wen Wang, Wan-Chi Siu, Zhi-Song Liu, Chu-Tak Li, and Daniel PK Lun. Deep relighting networks for image light source manipulation. arXiv preprint arXiv:2008.08298,

    2008 arXiv

  33. [41]

    Learning unified decompositional and compositional nerf for editable novel view synthesis

    Yuxin Wang, Wayne Wu, and Dan Xu. Learning unified decompositional and compositional nerf for editable novel view synthesis. In ICCV, 2023. 1

    2023

  34. [42]

    Image quality assessment: from error visibility to structural similarity

    Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Si- moncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600–612, 2004. 6

    2004

  35. [43]

    Sire-ir: Inverse rendering for brdf reconstruction with shadow and illumi- nation removal in high-illuminance scenes

    Ziyi Yang, Yanzhen Chen, Xinyu Gao, Yazhen Yuan, Yu Wu, Xiaowei Zhou, and Xiaogang Jin. Sire-ir: Inverse rendering for brdf reconstruction with shadow and illumi- nation removal in high-illuminance scenes. arXiv preprint arXiv:2310.13030, 2023. 1, 2

    arXiv

  36. [44]

    IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis

    Weicai Ye, Shuo Chen, Chong Bao, Hujun Bao, Marc Polle- feys, Zhaopeng Cui, and Guofeng Zhang. IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis. In Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision , 2023. 1, 2,...

    2023

  37. [45]

    Relighting neural radiance fields with shadow and highlight hints

    Chong Zeng, Guojun Chen, Yue Dong, Pieter Peers, Hongzhi Wu, and Xin Tong. Relighting neural radiance fields with shadow and highlight hints. In ACM SIGGRAPH 2023 Con- ference Proceedings, 2023. 1, 2, 5, 7, 8

    2023

  38. [46]

    PhySG: Inverse rendering with spherical gaussians for physics-based material editing and relighting

    Kai Zhang, Fujun Luan, Qianqian Wang, Kavita Bala, and Noah Snavely. PhySG: Inverse rendering with spherical gaussians for physics-based material editing and relighting. In The IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), 2021. 2

    2021

  39. [47]

    The unreasonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shecht- man, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 586–595, 2018. 6

    2018

  40. [48]

    Ner- factor: Neural factorization of shape and reflectance under an unknown illumination

    Xiuming Zhang, Pratul P Srinivasan, Boyang Deng, Paul De- bevec, William T Freeman, and Jonathan T Barron. Ner- factor: Neural factorization of shape and reflectance under an unknown illumination. ACM Transactions on Graphics (ToG), 40(6):1–18, 2021. 1, 2

    2021

  41. [49]

    Modeling indirect illumination for inverse rendering

    Yuanqing Zhang, Jiaming Sun, Xingyi He, Huan Fu, Rongfei Jia, and Xiaowei Zhou. Modeling indirect illumination for inverse rendering. In CVPR, 2022. 1, 2, 3, 5, 6

    2022

  42. [50]

    Deep single-image portrait relighting

    Hao Zhou, Sunil Hadap, Kalyan Sunkavalli, and David W Ja- cobs. Deep single-image portrait relighting. In Proceedings 10 of the IEEE International Conference on Computer Vision , pages 7194–7202, 2019. 2 11 MLI-NeRF: Multi-Light Intrinsic-Aware Neural Radiance Fields Supplemen...

    2019

  43. [52]

    More results on the Synthetic Dataset

  44. [53]

    More results on the Real Object Dataset

  45. [54]

    We have also submitted a supplementary video to showcase the results of our method on all datasets

    More results on the ReNe Dataset. We have also submitted a supplementary video to showcase the results of our method on all datasets

  46. [55]

    7 to Fig

    More results on the Synthetic Dataset From Fig. 7 to Fig. 10, we present additional qualitative results with all settings including Single Light, Multiple Lights, and Random Lights on the Synthetic Dataset. In the Synthetic Dataset, we conduct experiments on four scenes: Hotdo...

  47. [56]

    Overall, since the camera view used in comparisons is the same, the reflectance should have the same GT across all settings

    fails to produce satisfactory results, with residual cast shadows mixing into the reflectance. Overall, since the camera view used in comparisons is the same, the reflectance should have the same GT across all settings. Our predicted reflectance consistently outper- forms othe...

  48. [57]

    More results on the Real Object Dataset We present different results for the 4 scenes of the Real Ob- ject Dataset, including Fish, Pikachu, Pixiu, and FurScene [13, 45]. In Fig. 11, we show additional results on further scenes. In Fig. 12, we present the results of reflectanc...

  49. [58]

    15 to Fig

    More results on the ReNe Dataset Fig. 15 to Fig. 18 present more detailed results on the ReNe dataset, where the four scenes are labeled as apple, cube, garden, and savannah in the original dataset. Similar to our observations on the Synthetic Dataset, our method outper- forms...

  50. [331]

    Wiley Online Library, 2021. 2

    2021

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.