Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

ePBR: Extended PBR Materials in Image Synthesis

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that adding one transparency channel to intrinsic image maps turns single-image compositing into deterministic, editable glass synthesis.

desk verdict The transparency term is physically wrong, so the paper's central claim about synthesizing glass is unsupported, though the compositing idea is worth a fix. read the letter →

arxiv 2504.17062 v1 pith:T5YJZV4A submitted 2025-04-23 cs.GR cs.CV

Yu Guo , Zhiqiang Lao , Xiyun Song , Yubin Zhou , Zongfang Lin , Heather Yu This is my paper · ORCID
classification cs.GRcs.CV
keywords intrinsicimagedecompositionphysicallybasedrenderingtransparentmaterialsspeculartransmissionthinsurfacemodelscreen-spacecompositingePBRediting
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 a single extra scalar channel—a transparency map $T$—is enough to extend standard intrinsic-image/PBR material representations from opaque surfaces to transparent ones such as glass and windows. It derives a thin-surface BSDF whose transmission term is a microfacet lobe, then shows that in screen space this term becomes two applications of the same blur kernel to the background radiance. Combining the resulting layers with the explicit rule $I=(1-T)(1-M)I_{\mathrm{diff}}+I_{\mathrm{spec}}+TI_{\mathrm{tran}}$ gives deterministic, interpretable image synthesis that needs no Monte Carlo sampling. Against the diffusion-based baseline on the five tested indoor scenes, the paper reports lower LPIPS over the whole composed image, with the largest gains in mirror-like regions. If this holds, glass-like materials become directly editable in single images by changing $T$, roughness, or albedo and re-applying basic image operations.

What carries the argument

The central mechanism is the thin-surface BSDF together with its screen-space reduction: specular transmission is treated as the same microfacet lobe used for reflection but evaluated with an extended normal distribution function (eNDF), and under the zero-thickness assumption this collapses to applying the same GGX blur kernel twice to the background radiance map. The compositing identity $I=(1-T)(1-M)I_{\mathrm{diff}}+I_{\mathrm{spec}}+TI_{\mathrm{tran}}$ then assembles the final image from diffuse irradiance, screen-space ray-traced mirror reflections, and the twice-blurred background, with transparency $T$ stored in the unused blue channel of a standard three-channel PBR map.

What would settle it

Render a thick transparent object, such as a water glass or bottle, in front of a textured background both with a path tracer and with this compositing rule using the same roughness and transparency; if the twice-blurred background shows visible displacement, double imaging, or distortion that grows with thickness or viewing angle, the zero-thickness premise fails. A simpler screen-space variant is to compare Eq. (20) against a Monte Carlo reference for one oblique-angle glass slab and look for background misalignment beyond one pixel.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the transmission behavior of a transparent thin surface can be folded into an intrinsic-image compositing equation. Whereas existing intrinsic/PBR representations only model diffuse and specular reflectance, ePBR adds specular transmittance through an extended normal distribution function and expresses the transmitted radiance as the original background convolved twice with the same GGX kernel. With the transparency map $T$ modulating the transmission layer and $(1-T)(1-M)$ modulating diffuse, the screen-space rule reproduces the path-traced references in high-specular regions and, on LPIPS, beats the diffusion-based recomposition for all five tested scenes.

Load-bearing premise

The load-bearing premise is the thin-surface model: the transparent object is treated as an infinitely thin slab with two parallel faces, negligible refraction offset, no internal reflection, and equal roughness on both sides; if the target is thick, bent, or viewed at steep angles, the transmitted layer is wrong.

Editorial extensions

If this is right

  • Setting $T$ and recomposing with the blur kernels edits a window's transparency and roughness in one image, with no path tracing.
  • Because $T$ occupies the blue channel that older PBR maps leave unused, ePBR materials fit existing intrinsic-image pipelines without extra memory.
  • The composition is deterministic and uses only basic image operations, so resolution and GPU memory no longer limit the synthesis step.
  • The method's validity is limited to thin, nearly flat transparent surfaces; thick or strongly refractive objects require geometry the screen-space representation does not provide.
  • If ePBR maps replace PBR maps in inverse-rendering and decomposition data, learning-based systems could inherit explicit transparency control, a direction the paper names as future work.

Reading between the lines

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

  • A natural stress test is to repeat the five-scene comparison with fully automatic intrinsic estimation, since the reported results use manually corrected metallic and transparency maps; if the LPIPS advantage shrinks, the explicit compositing still wins only when its input channels are trustworthy.
  • The same double-blur trick could be extended to front/back roughness by replacing the identical second blur with a second kernel parameterized by a different roughness, which the paper notes is physically more accurate for glass.
  • Because the composition is differentiable and parameter-light, it could serve as a cheap generative prior for training decomposers that output $T$ directly, avoiding expensive path-traced transparent-object datasets.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes ePBR, an extension of intrinsic image representations to support transparent materials. The method augments standard intrinsic channels (albedo, roughness, metallic, normal, depth, irradiance, mirror reflectance, background radiance) with a transparency map T stored in the blue channel of the PBR material image. The rendering model combines a diffuse Lambertian term, a microfacet specular reflectance term, and a specular transmittance term under a thin-surface assumption, then derives screen-space compositing equations: a split-sum style reflection layer, a transmission layer obtained by applying the GGX blur kernel twice to the background and multiplying by albedo, and a final composition I = (1-T)(1-M)Idiff + Ispec + TItran. The authors evaluate the approach on five InteriorVerse scenes and report lower LPIPS than the diffusion-based RGB↔X baseline, arguing that their deterministic composition provides better control and fidelity for high-specular and transparent regions.

Significance. If the model worked as claimed, it would offer a practical lightweight alternative to Monte Carlo rendering for editing and compositing transparent objects in screen-space intrinsic representations, with the transparency map occupying no extra memory. The paper clearly states its thin-surface simplification and identifies future extensions. However, the central claim depends on the physical correctness of the transmission term, and that term currently uses a reflectance Fresnel factor instead of a transmittance factor, producing qualitatively wrong brightness behavior for glass. The empirical validation is also limited to five scenes with manually adjusted channels, no error bars, and no code release, so the headline 'outperforms diffusion-based method' is not yet established. The paper's contribution is conceptually useful but requires a corrected transmission model and stronger validation.

major comments (3)
  1. [Sec. 3.3, Eq. (8) and Sec. 4.3, Eq. (21)] The transmission term uses the same Fresnel reflectance factor F(ht, ωo) as the reflection lobe, and Eq. (21) scales the transmitted background by (AF0+B), the precomputed reflectance integral. For a smooth dielectric with F0=0.04 at normal incidence this yields a transmittance of about 0.04, whereas a thin glass slab transmits approximately (1-F)^2 ≈ 0.92. At grazing angles the physical transmittance (1-F)^2 tends to zero, while Eq. (21)'s factor grows toward 1, predicting the opposite qualitative behavior of glass. This contradicts the paper's own interpretation that A=(1,1,1) represents 'pure glass with no energy loss' in Sec. 5.2. Since transparency is the core extension and the LPIPS comparison in Fig. 7 depends on the resulting transmission layer, the central claim is not supported as written. The transmission model should use a proper transmittance Fresnel factor, e.g. (1-F)^2 for a thin slab, and the precomputed lookup should be recomputed accordingly.
  2. [Sec. 5.3 and Table 2] The claim that the method 'outperforms diffusion-based method for the entire image' rests on five scenes with no error bars or statistical testing, and the comparison setup is partially hand-tuned: T is set as the inverse of the ground-truth mask, M and T are manually modified to fit the renderings, Abg is set to 1, and E is estimated from RGB↔X. This does not establish a general advantage, especially for the transparent material aspect that is the paper's central novelty. The authors should either provide a broader quantitative comparison with fixed, non-manual channel generation, or restrict the claim to a qualitative demonstration.
  3. [Sec. 4.2, Eq. (19) and Eq. (21)] The reflection and transmission layers both use the same precomputed (AF0+B) factor, but the transmission layer in Eq. (21) is additionally multiplied by albedo A to approximate absorption. This conflates the reflectance-based Fresnel weight with a volume absorption effect and can darken or color-shift the transmitted background in a way that is not physically motivated. At minimum, the paper should state this as an ad hoc approximation with a quantitative demonstration of its effect, rather than presenting it as part of the physically derived compositing model.
minor comments (5)
  1. [Sec. 3.3, Eq. (8)] The half-vector definition ht = −(ωo + ηωi)/||ωo + ηωi|| uses η but the refractive index is not defined in the text; please clarify the notation and its relationship to F0.
  2. [Fig. 4] The roughness evaluation compares 'Ours' with path-traced ground truth, but no numerical error is reported and the figure is small; adding quantitative roughness-sweep errors would strengthen the claim that the double-convolution approximation is accurate.
  3. [Sec. 5.2, Fig. 6(d)] The albedo row says that with A=(0,0,0) 'light cannot scatter or pass through the surface', but Eq. (21) multiplies the transmitted background by A, so a pure black albedo would make the background completely invisible; the physical interpretation of A as both diffuse reflectance and transmission absorption should be discussed more carefully.
  4. [Sec. 5.3] The sentence 'T is the inverse of the ground-truth mask since we found that most of the nonmasked areas are glasses' is unclear; please specify which mask is used and how T is aligned with the object regions in Fig. 7.
  5. [General] The paper would benefit from a release of the compositing code and the exact intrinsic-channel inputs used for the five test scenes, since the method is deterministic and reproducible only if those inputs are specified.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation chain: the screen-space compositing is an explicit linear model assembled from split-sum BRDF pre-integration and thin-slab kernels; the transparency map T is an input channel, not a parameter fitted to the LPIPS comparison.

full rationale

The derivation in Secs. 3–4 is not circular. Eq. (22) is an explicit composition I = (1-T)(1-M)Idiff + Ispec + TItran, where T is a stored material channel (Sec. 5.1: 'Our ePBR model stores the transparency map (T) in the blue channel'), not a quantity derived from the output. Each layer is obtained by standard split-sum demodulation (Eqs. 11–12): Idiff = AE (Eq. 15), Ispec = (AF0+B)CONV(K,Amr) (Eq. 19), Itran = (AF0+B)CONV(K,CONV(K,Abg))A (Eq. 21), with the split-sum factor (AF0+B) precomputed from the GGX/Schlick model (Eq. 16). The transmission kernel is approximated by applying the GGX kernel twice (Eq. 20), justified by thin-slab references [10,13]; this is an approximation, not a restatement of the result. The comparison in Tab. 2 uses ground-truth-derived channels and manual adjustments ('T is the inverse of the ground-truth mask... we manually made some modifications to these channels'), so it is a data-selection/validation issue, not a circular fit; no parameter of Eq. 22 is tuned to minimize LPIPS. The only overlapping-author citations ([14,15]) are background references for layered BSDFs and SVBRDF capture; they are not load-bearing for the compositing claim. The manuscript itself flags limitations ('Energy conservation is not considered' after Eq. 10; 'only thin surface is supported' in Sec. 6; 'can not handle multireflection or color bleeding'); these are physical-correctness caveats, not circularity. The suspicious reuse of the reflection Fresnel/pre-integration factor (AF0+B) in Eq. 21 is a physical modeling error to assess under correctness/energy conservation, but it does not make the derivation equivalent to its inputs. Overall no circular step is exhibited.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The central claim rests on standard rendering approximations plus one paper-specific modeling choice, albedo-as-absorption. There are no fitted constants targeted at the evaluation results, but the fixed blur distance d is an unspecified hand choice. No new physical entities are introduced; the thin-surface transmission model is a known approximation from the cited graphics literature.

free parameters (1)
  • Fixed shading distance d for reflection and transmission blur kernels
    In Sec. 4.2 the authors write 'for simplicity, we use a fixed value for d' in defining the normalized filtering kernel K(R,d); the value is never specified and directly controls blur shape.
assumptions (5)
  • domain assumption Thin-surface assumption: zero-thickness parallel surfaces, refraction offset negligible, internal reflections ignored
    Sec. 3.3 and Fig. 2; needed to write the transmission lobe in Eq. 8 and the double convolution in Eq. 20.
  • domain assumption Split-sum factorization of the specular integral separates the material term from the lighting convolution
    Sec. 4.2, adopted from Unreal Engine [21] and [57]; standard and well tested, but an approximation.
  • domain assumption The BSDF kernel shape is dominated by the normal distribution D(h), so filtering with a D-based kernel approximates the lighting integral
    Sec. 4.2, citing Walter et al. [42].
  • domain assumption Transmission through a thin rough slab can be approximated by applying the same GGX convolution twice
    Eq. 20, based on dual-microfacet and eNDF models [10,13].
  • ad hoc to paper Multiplying the transmitted layer by albedo A approximates absorption after the light passes through the thin surface
    Eq. 21 states the albedo is multiplied to approximate absorption; no measured transmittance model is used.

how reviews work

0 comments
Cite this review

Pith. "Pith review of ePBR: Extended PBR Materials in Image Synthesis." pith.science (2026). https://pith.science/paper/T5YJZV4A

@misc{pith2026250417062,
  author       = {Pith},
  title        = {Pith review of: ePBR: Extended PBR Materials in Image Synthesis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T5YJZV4A}},
  note         = {Machine review of arXiv:2504.17062}
}
read the original abstract

Realistic indoor or outdoor image synthesis is a core challenge in computer vision and graphics. The learning-based approach is easy to use but lacks physical consistency, while traditional Physically Based Rendering (PBR) offers high realism but is computationally expensive. Intrinsic image representation offers a well-balanced trade-off, decomposing images into fundamental components (intrinsic channels) such as geometry, materials, and illumination for controllable synthesis. However, existing PBR materials struggle with complex surface models, particularly high-specular and transparent surfaces. In this work, we extend intrinsic image representations to incorporate both reflection and transmission properties, enabling the synthesis of transparent materials such as glass and windows. We propose an explicit intrinsic compositing framework that provides deterministic, interpretable image synthesis. With the Extended PBR (ePBR) Materials, we can effectively edit the materials with precise controls.

Figures

Figures reproduced from arXiv: 2504.17062 by the authors.

Figure 1
Figure 1. Highly specular and transparent objects are very com [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Thin surface assumption: Ignoring the internal re￾flection, light traveling through a transparent thin surface refracts twice as it enters and exits, and reflects once only on the top sur￾face. For a smooth surface, light exits with the same direction as it enters and the offset could be ignored. hr is the half vector between ωo and ωi , which is hr = (ωo + ωi)/(||ωo + ωi ||). Normal distribution function (D), also … view at source ↗
Figure 4
Figure 4. Roughness evaluation. Ours: Directly apply filter￾ing kernel to the specular reflection image; GT: Path tracing with Monte Carlo sampling. \begin {aligned} \label {eqn:rendering_bsdf} L(\wo ) & = \kd \int _{\Hem } \fd (\wo , \wi ) \, L(\wi ) \, \dotp {\wi }{\n } \intd \wi \\ & + \ks \int _{\Hem } \fs (\wo , \wi ) \, L(\wi ) \, \dotp {\wi }{\n } \intd \wi \\ & + \kt \int _{\hem } \ft (\wo , \wi ) \, L(\wi ) \, \dotp … view at source ↗
Figures from the paper (2 more)
Figure 6
Figure 6. Figure 6: ePBR material intrinsic evaluation. (a) The only opaque surface in this figure, as the Metallic (M) increases, the reflectance tint from light color (white) to metal color (red here); (b) Roughness (R) influences how blurry both reflection and transmission appear; (c) …
Figure 7
Figure 7. Figure 7: Image composition. We decompose reference images into intrinsic channels and then recompose them back. Compared to RGB↔X [50], our results generate more accurate reflectance in the high specular regions (marked with green box). In some non-specular regions, our light d…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Hybrid Neural-Microfacet BRDF Model for Real-Time Rendering

    cs.GR 2026-08 conditional novelty 6.0 of 10

    A hybrid BRDF model, combining a GGX analytical term with a tiny learned residual and gating network, fits measured materials more accurately than fully neural models at equal memory cost.

Reference graph

Works this paper leans on

58 extracted references · 49 canonical work pages · cited by 1 Pith paper

  1. [1]

    Efficient rendering of layered materials us- ing an atomic decomposition with statistical operators

    Laurent Belcour. Efficient rendering of layered materials us- ing an atomic decomposition with statistical operators. ACM TOG, 37(4):1, 2018. 2

    work page 2018

  2. [2]

    A practical extension to microfacet theory for the modeling of varying iridescence

    Laurent Belcour and Pascal Barla. A practical extension to microfacet theory for the modeling of varying iridescence. ACM TOG, 36(4):1–14, 2017. 2

    work page 2017

  3. [3]

    Blender 4.3 manual: Principled BSDF

    Blender. Blender 4.3 manual: Principled BSDF. 2

    work page

  4. [4]

    Extending the Disney BRDF to a BSDF with integrated subsurface scattering

    Brent Burley. Extending the Disney BRDF to a BSDF with integrated subsurface scattering. In ACM SIGGRAPH Course: Physically Based Shading in Theory and Practice , page 9, 2015. 2, 3

    work page 2015

  5. [5]

    Physically-based shading at Disney

    Brent Burley and Walt Disney Animation Studios. Physically-based shading at Disney. In ACM SIGGRAPH Course: Practical Physically Based Shading in Film and Game Production, pages 1–7. vol. 2012, 2012. 2, 3

    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 TOG, 43(1):1–24, 2023. 3

    work page 2023

  7. [7]

    Colorful diffuse intrinsic image decomposition in the wild

    Chris Careaga and Ya ˘gız Aksoy. Colorful diffuse intrinsic image decomposition in the wild. ACM TOG, 43(6):1–12,

    work page

  8. [8]

    A practical and controllable hair and fur model for production path tracing

    Matt Jen-Yuan Chiang, Benedikt Bitterli, Chuck Tappan, and Brent Burley. A practical and controllable hair and fur model for production path tracing. In ACM SIGGRAPH Talks , pages 1–1. 2015. 2

    work page 2015

Show all 58 references
  1. [9]

    Torrance

    Robert L Cook and Kenneth E. Torrance. A reflectance model for computer graphics. ACM TOG, 1(1):7–24, 1982. 3

    1982

  2. [10]

    The dual-microfacet model for cap- turing thin transparent slabs

    Qiang Dai, Jiaping Wang, Yiming Liu, John Snyder, Enhua Wu, and Baining Guo. The dual-microfacet model for cap- turing thin transparent slabs. Computer Graphics Forum, 28 (7):1917–1925, 2009. 6

    1917

  3. [11]

    Single-image SVBRDF capture with a rendering-aware deep network

    Valentin Deschaintre, Miika Aittala, Fredo Durand, George Drettakis, and Adrien Bousseau. Single-image SVBRDF capture with a rendering-aware deep network. ACM TOG, 37(4):1–15, 2018. 3

    2018

  4. [12]

    A general framework for pearlescent ma- terials

    Ib ´on Guill´en, Julio Marco, Diego Gutierrez, Wenzel Jakob, and Adrian Jarabo. A general framework for pearlescent ma- terials. ACM TOG, 39(6):1–15, 2020. 2

    2020

  5. [13]

    Render- ing thin transparent layers with extended normal distribution functions

    Jie Guo, Jinghui Qian, Yanwen Guo, and Jingui Pan. Render- ing thin transparent layers with extended normal distribution functions. IEEE TVCG, 23(9):2108–2119, 2016. 4, 6

    2016

  6. [14]

    Position-free Monte Carlo simulation for arbitrary layered BSDFs

    Yu Guo, Milo ˇs Ha ˇsan, and Shuang Zhao. Position-free Monte Carlo simulation for arbitrary layered BSDFs. ACM TOG, 37(6):1–14, 2018. 2, 6

    2018

  7. [15]

    MaterialGAN: Reflectance capture using a generative SVBRDF model

    Yu Guo, Cameron Smith, Milo ˇs Haˇsan, Kalyan Sunkavalli, and Shuang Zhao. MaterialGAN: Reflectance capture using a generative SVBRDF model. ACM TOG, 39(6):1–13, 2020. 3

    2020

  8. [16]

    Lotus: Diffusion-based visual foundation model for high-quality dense prediction

    Jing He, Haodong Li, Wei Yin, Yixun Liang, Leheng Li, Kaiqiang Zhou, Hongbo Zhang, Bingbing Liu, and Ying- Cong Chen. Lotus: Diffusion-based visual foundation model for high-quality dense prediction. In ICLR, 2025. 3

    2025

  9. [17]

    Ma- terial anything: Generating materials for any 3d object via diffusion

    Xin Huang, Tengfei Wang, Ziwei Liu, and Qing Wang. Ma- terial anything: Generating materials for any 3d object via diffusion. In CVPR, 2025. 3

    2025

  10. [18]

    Diffusion model-based image editing: A survey

    Yi Huang, Jiancheng Huang, Yifan Liu, Mingfu Yan, Jiaxi Lv, Jianzhuang Liu, Wei Xiong, He Zhang, Liangliang Cao, and Shifeng Chen. Diffusion model-based image editing: A survey. IEEE TPAMI, (01):1–27, 2025. 2

    2025

  11. [19]

    A comprehensive framework for rendering lay- ered materials

    Wenzel Jakob, Eugene d’Eon, Otto Jakob, and Steve Marschner. A comprehensive framework for rendering lay- ered materials. ACM TOG, 33(4):1–14, 2014. 2

    2014

  12. [20]

    Mitsuba 3 renderer, 2022

    Wenzel Jakob, S ´ebastien Speierer, Nicolas Roussel, Merlin Nimier-David, Delio Vicini, Tizian Zeltner, Baptiste Nicolet, Miguel Crespo, Vincent Leroy, and Ziyi Zhang. Mitsuba 3 renderer, 2022. 2

    2022

  13. [21]

    Real shading in Unreal Engine

    Brian Karis and Epic Games. Real shading in Unreal Engine

  14. [22]

    Physically Based Shading Theory Practice, 4(3):1,

    Proc. Physically Based Shading Theory Practice, 4(3):1,

  15. [23]

    In- trinsic image diffusion for indoor single-view material esti- mation

    Peter Kocsis, Vincent Sitzmann, and Matthias Nießner. In- trinsic image diffusion for indoor single-view material esti- mation. In CVPR, pages 5198–5208, 2024. 3

    2024

  16. [24]

    Inverse ren- dering for complex indoor scenes: Shape, spatially-varying lighting and SVBRDF from a single image

    Zhengqin Li, Mohammad Shafiei, Ravi Ramamoorthi, Kalyan Sunkavalli, and Manmohan Chandraker. Inverse ren- dering for complex indoor scenes: Shape, spatially-varying lighting and SVBRDF from a single image. In CVPR, pages 2475–2484, 2020. 3

    2020

  17. [25]

    DiffusionRenderer: Neural inverse and forward rendering with video diffusion models

    Ruofan Liang, Zan Gojcic, Huan Ling, Jacob Munkberg, Jon Hasselgren, Zhi-Hao Lin, Jun Gao, Alexander Keller, Nandita Vijaykumar, Sanja Fidler, et al. DiffusionRenderer: Neural inverse and forward rendering with video diffusion models. In CVPR, 2025. 3

    2025

  18. [26]

    IntrinsicDiffusion: Joint in- trinsic layers from latent diffusion models

    Jundan Luo, Duygu Ceylan, Jae Shin Yoon, Nanxuan Zhao, Julien Philip, Anna Fr ¨uhst¨uck, Wenbin Li, Christian Richardt, and Tuanfeng Wang. IntrinsicDiffusion: Joint in- trinsic layers from latent diffusion models. In ACM SIG- GRAPH Conference Papers, pages 1–11, 2024. 3

    2024

  19. [27]

    OpenSVBRDF: A database of measured spatially-varying reflectance

    Xiaohe Ma, Xianmin Xu, Leyao Zhang, Kun Zhou, and Hongzhi Wu. OpenSVBRDF: A database of measured spatially-varying reflectance. ACM TOG, 42(6):1–14, 2023. 3

    2023

  20. [28]

    Light scattering from human hair fibers

    Stephen R Marschner, Henrik Wann Jensen, Mike Cam- marano, Steve Worley, and Pat Hanrahan. Light scattering from human hair fibers. ACM TOG, 22(3):780–791, 2003. 2

    2003

  21. [29]

    A practical ply-based appearance model of woven fabrics

    Zahra Montazeri, Søren B Gammelmark, Shuang Zhao, and Henrik Wann Jensen. A practical ply-based appearance model of woven fabrics. ACM TOG, 39(6):1–13, 2020. 2

    2020

  22. [30]

    Generalization of Lam- bert’s reflectance model

    Michael Oren and Shree K Nayar. Generalization of Lam- bert’s reflectance model. In ACM SIGGRAPH Conference Papers, pages 239–246, 1994. 3

    1994

  23. [31]

    GPU Gems 2: Pro- gramming techniques for high-performance graphics and general-purpose computation (GPU Gems)

    Matt Pharr and Randima Fernando. GPU Gems 2: Pro- gramming techniques for high-performance graphics and general-purpose computation (GPU Gems) . Addison- Wesley Professional, 2005. 5

    2005

  24. [32]

    Physi- cally based rendering: From theory to implementation

    Matt Pharr, Wenzel Jakob, and Greg Humphreys. Physi- cally based rendering: From theory to implementation. MIT Press, 2023. 2

    2023

  25. [33]

    DiffusionLight: Light probes for free by painting a chrome ball

    Pakkapon Phongthawee, Worameth Chinchuthakun, Non- taphat Sinsunthithet, Varun Jampani, Amit Raj, Pramook Khungurn, and Supasorn Suwajanakorn. DiffusionLight: Light probes for free by painting a chrome ball. In CVPR, pages 98–108, 2024. 6

    2024

  26. [34]

    An efficient represen- tation for irradiance environment maps

    Ravi Ramamoorthi and Pat Hanrahan. An efficient represen- tation for irradiance environment maps. InACM SIGGRAPH Conference Papers, pages 497–500, 2001. 3

    2001

  27. [35]

    Hierarchical text-conditional image gener- ation with clip latents

    Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image gener- ation with clip latents. arXiv preprint arXiv:2204.06125, 1 (2):3, 2022. 2

    2022 arXiv

  28. [36]

    Single-image reflectance and transmittance estima- tion from any flatbed scanner

    Carlos Rodr ´ıguez-Pardo, David Pascual-Hernandez, Javier Rodriguez-Vazquez, Jorge Lopez-Moreno, and Elena Garces. Single-image reflectance and transmittance estima- tion from any flatbed scanner. Computers & Graphics, page 104186, 2025. 3

    2025

  29. [37]

    High-resolution image syn- thesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj¨orn Ommer. High-resolution image syn- thesis with latent diffusion models. In CVPR, pages 10684– 10695, 2022. 2, 6

    2022

  30. [38]

    An inexpensive BRDF model for physically-based rendering

    Christophe Schlick. An inexpensive BRDF model for physically-based rendering. Computer Graphics Forum, 13 (3):233–246, 1994. 4

    1994

  31. [39]

    Alchemist: Parametric control of material prop- erties with diffusion models

    Prafull Sharma, Varun Jampani, Yuanzhen Li, Xuhui Jia, Dmitry Lagun, Fredo Durand, Bill Freeman, and Mark Matthews. Alchemist: Parametric control of material prop- erties with diffusion models. In CVPR, pages 24130–24141,

  32. [40]

    Meta 3d assetgen: Text-to-mesh generation with high- quality geometry, texture, and pbr materials

    Yawar Siddiqui, Tom Monnier, Filippos Kokkinos, Mahen- dra Kariya, Yanir Kleiman, Emilien Garreau, Oran Gafni, Natalia Neverova, Andrea Vedaldi, Roman Shapovalov, et al. Meta 3d assetgen: Text-to-mesh generation with high- quality geometry, texture, and pbr materials. In NeurI...

    2025

  33. [41]

    Unreal Engine 5.5: Physically based materials

    UE5. Unreal Engine 5.5: Physically based materials. 2

  34. [42]

    Robust Monte Carlo methods for light transport simulation

    Eric Veach. Robust Monte Carlo methods for light transport simulation. Stanford University, 1998. 2, 3

    1998

  35. [43]

    Microfacet models for refraction through rough surfaces

    Bruce Walter, Stephen R Marschner, Hongsong Li, and Ken- neth E Torrance. Microfacet models for refraction through rough surfaces. Rendering techniques, 2007:18th, 2007. 4, 5

    2007

  36. [44]

    Spongecake: A layered microflake surface appearance model

    Beibei Wang, Wenhua Jin, Milo ˇs Ha ˇsan, and Ling-Qi Yan. Spongecake: A layered microflake surface appearance model. ACM TOG, 42(1):1–16, 2022. 2

    2022

  37. [45]

    NFPLight: Deep SVBRDF estimation via the combination of near and far field point lighting.ACM TOG, 43(6):1–11, 2024

    Li Wang, Lianghao Zhang, Fangzhou Gao, Yuzhen Kang, and Jiawan Zhang. NFPLight: Deep SVBRDF estimation via the combination of near and far field point lighting.ACM TOG, 43(6):1–11, 2024. 3

    2024

  38. [46]

    Mate- rialist: Physically based editing using single-image inverse rendering

    Lezhong Wang, Duc Minh Tran, Ruiqi Cui, Thomson TG, Manmohan Chandraker, and Jeppe Revall Frisvad. Mate- rialist: Physically based editing using single-image inverse rendering. arXiv preprint arXiv:2501.03717, 2025. 3

    2025 arXiv

  39. [47]

    Boosting 3d object generation through PBR materials

    Yitong Wang, Xudong Xu, Li Ma, Haoran Wang, and Bo Dai. Boosting 3d object generation through PBR materials. In SIGGRAPH Asia Conference Papers, pages 1–11, 2024. 3

    2024

  40. [48]

    Depth anything: Unleashing the power of large-scale unlabeled data

    Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In CVPR, pages 10371–10381, 2024. 3

    2024

  41. [49]

    Depth any- thing v2

    Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiao- gang Xu, Jiashi Feng, and Hengshuang Zhao. Depth any- thing v2. In NeurIPS, pages 21875–21911, 2024

    2024

  42. [50]

    Stablenormal: Reducing diffusion variance for stable and sharp normal

    Chongjie Ye, Lingteng Qiu, Xiaodong Gu, Qi Zuo, Yushuang Wu, Zilong Dong, Liefeng Bo, Yuliang Xiu, and Xiaoguang Han. Stablenormal: Reducing diffusion variance for stable and sharp normal. ACM TOG, 43(6):1–18, 2024. 3

    2024

  43. [51]

    RGBX: Image decomposition and synthesis using material- and lighting-aware diffusion models

    Zheng Zeng, Valentin Deschaintre, Iliyan Georgiev, Yannick Hold-Geoffroy, Yiwei Hu, Fujun Luan, Ling-Qi Yan, and Miloˇs Ha ˇsan. RGBX: Image decomposition and synthesis using material- and lighting-aware diffusion models. InACM SIGGRAPH Conference Papers, 2024. 2, 3, 5, 7, 8

    2024

  44. [52]

    Adding conditional control to text-to-image diffusion models

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In CVPR, pages 3836–3847, 2023. 2

    2023

  45. [53]

    Scaling in-the-wild training for diffusion-based illumination harmo- nization and editing by imposing consistent light transport

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Scaling in-the-wild training for diffusion-based illumination harmo- nization and editing by imposing consistent light transport. In ICLR, 2025. 2

    2025

  46. [54]

    The unreasonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586–595,

  47. [55]

    Dreammat: High-quality PBR material generation with geometry-and light-aware diffusion models

    Yuqing Zhang, Yuan Liu, Zhiyu Xie, Lei Yang, Zhongyuan Liu, Mengzhou Yang, Runze Zhang, Qilong Kou, Cheng Lin, Wenping Wang, et al. Dreammat: High-quality PBR material generation with geometry-and light-aware diffusion models. ACM TOG, 43(4):1–18, 2024. 3

    2024

  48. [56]

    Learning-based inverse rendering of complex indoor scenes with differentiable Monte Carlo raytracing

    Jingsen Zhu, Fujun Luan, Yuchi Huo, Zihao Lin, Zhihua Zhong, Dianbing Xi, Rui Wang, Hujun Bao, Jiaxiang Zheng, and Rui Tang. Learning-based inverse rendering of complex indoor scenes with differentiable Monte Carlo raytracing. In SIGGRAPH Asia Conference Papers, pages 1–8, 202...

    2022

  49. [57]

    MCMat: Multiview-consistent and phys- ically accurate pbr material generation

    Shenhao Zhu, Lingteng Qiu, Xiaodong Gu, Zhengyi Zhao, Chao Xu, Yuxiao He, Zhe Li, Xiaoguang Han, Yao Yao, Xun Cao, et al. MCMat: Multiview-consistent and phys- ically accurate pbr material generation. arXiv preprint arXiv:2412.14148, 2024. 3

    2024 arXiv

  50. [58]

    Real-time denoising using BRDF pre-integration factoriza- tion

    Tao Zhuang, Pengfei Shen, Beibei Wang, and Ligang Liu. Real-time denoising using BRDF pre-integration factoriza- tion. Computer Graphics Forum, 40(7):173–180, 2021. 5

    2021

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.