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REVIEW 1 major objections 1 minor 4 references

Ambient-robust Inverse Rendering using Active RGB-NIR Imaging

T0 review · 1 major / 1 minor · reviewed 2026-06-29 · grok-4.3

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Pith's one-line read Active NIR flash illumination enables accurate geometry and reflectance reconstruction from multi-view RGB and NIR images despite varying ambient light.

desk verdict The paper gives a concrete RGB-NIR active capture system and first multi-view dataset for ambient-robust inverse rendering, but the flash separation step rests on assumptions that are not shown to hold under varied real lighting. read the letter →

arxiv 2605.30250 v1 pith:PZACUCKZ submitted 2026-05-28 cs.CV cs.GR

Hoon-Gyu Chung , Jinnyeong Kim , Hyunwoo Kang , Seung-Hwan Baek This is my paper
classification cs.CVcs.GR
keywords inverserenderingRGB-NIRimagingambientrobustnessgeometryreconstructionreflectanceestimationactiveilluminationmulti-view
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 seeks to make inverse rendering robust to ambient illumination by pairing multi-view RGB images captured under ambient light with NIR images captured under active NIR flash. The NIR flash supplies stable point-light shading that remains largely independent of surrounding light, and a three-stage pipeline combines the two modalities to recover object shape and material properties. Existing methods often produce errors when ambient conditions change, so this approach aims to deliver consistent results in real-world settings. The authors built a mobile RGB-NIR imaging system and released a new multi-view dataset captured under multiple ambient conditions to support the method.

What carries the argument

Three-stage inverse rendering pipeline that separates and combines ambient RGB information with active NIR flash shading.

What would settle it

If geometry or reflectance estimates change substantially when ambient lighting is altered while the object, camera positions, and NIR flash remain fixed.

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Extended reading notes

Core claim

By using multi-view RGB images illuminated by ambient light and NIR images acquired with active NIR flash illumination, accurate geometry and reflectance are reconstructed via a three-stage inverse rendering method that exploits the complementary benefits of the two image types.

Load-bearing premise

The NIR flash illumination produces point-light shading that is largely invariant to ambient illumination and can be reliably separated from ambient contributions in the captured NIR images.

Editorial extensions

If this is right

  • Accurate geometry and reflectance estimates are obtained across multiple ambient lighting scenarios.
  • The method outperforms prior inverse rendering approaches on the collected data.
  • A mobile active imaging system supports dense multi-view RGB-NIR acquisition.
  • The first multi-view RGB-NIR inverse rendering dataset under varying ambient conditions is introduced.

Reading between the lines

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

  • The technique could support inverse rendering in outdoor or uncontrolled environments where ambient light fluctuates.
  • The dataset may serve as a testbed for evaluating robustness in other reconstruction methods.
  • Active flash separation in NIR could inspire similar strategies for other spectral bands or modalities.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 1 minor

Summary. The manuscript claims to present an ambient-robust inverse rendering approach that combines multi-view RGB images captured under uncontrolled ambient illumination with NIR images acquired under active NIR flash illumination. A three-stage inverse rendering pipeline exploits the complementary properties of the two modalities to recover geometry and reflectance; the NIR flash is asserted to supply point-light shading that is largely invariant to ambient light. The authors describe a custom mobile RGB-NIR acquisition system and release the first multi-view RGB-NIR inverse-rendering dataset captured under multiple ambient conditions. Experiments are reported to show improved accuracy over prior methods across varying lighting scenarios.

Significance. If the NIR-flash separation step proves reliable, the work would offer a practical route to inverse rendering that remains stable under real-world ambient variation, a long-standing obstacle in the field. The new dataset and mobile capture platform constitute concrete, reusable contributions that could support follow-on research even if the algorithmic details require refinement.

major comments (1)
  1. [Abstract / §3] Abstract / §3 (three-stage pipeline): the central claim that active NIR flash shading is 'largely invariant to ambient illumination' and 'reliably separated' is load-bearing for all downstream geometry and reflectance estimates, yet no equation, calibration procedure, or explicit separation formula is supplied. Without these, it is impossible to verify whether the method assumes linear NIR sensor response, negligible crosstalk, or low-frequency ambient NIR, any of which would invalidate the invariance premise under sunlight or broadband LED sources.
minor comments (1)
  1. [Abstract] The abstract states that the method 'exploits the complementary benefits' of RGB and NIR but does not name the three stages; a one-sentence outline of the stages would improve readability without lengthening the abstract.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comment on the NIR separation step. We address it point-by-point below and will revise the manuscript to supply the missing details.

read point-by-point responses

  1. Referee: [Abstract / §3] Abstract / §3 (three-stage pipeline): the central claim that active NIR flash shading is 'largely invariant to ambient illumination' and 'reliably separated' is load-bearing for all downstream geometry and reflectance estimates, yet no equation, calibration procedure, or explicit separation formula is supplied. Without these, it is impossible to verify whether the method assumes linear NIR sensor response, negligible crosstalk, or low-frequency ambient NIR, any of which would invalidate the invariance premise under sunlight or broadband LED sources.

    Authors: We agree that the manuscript does not supply an explicit separation equation or calibration details in §3. In the revised version we will add a dedicated paragraph (and accompanying equation) that formalizes the separation as I_NIR^flash = I_NIR^with - I_NIR^ambient, describe the capture protocol used to obtain the ambient-only NIR image, and report the sensor calibration steps that confirm linear response and negligible RGB-NIR crosstalk on the hardware employed. We will also state the operating assumption that ambient NIR is spatially low-frequency relative to the flash and therefore removable by direct subtraction. These additions will allow readers to evaluate the invariance claim under the tested conditions, including sunlight. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity detected; method description contains no self-referential derivations

full rationale

The provided abstract and text describe a three-stage inverse rendering pipeline that exploits RGB ambient images and active NIR flash images for geometry and reflectance recovery. No equations, fitting procedures, or derivation steps are shown that reduce a claimed prediction or result to its own inputs by construction. The NIR flash separation is presented as an enabling physical property rather than a fitted or self-defined quantity. The central claim therefore remains independent of any circular reduction within the visible content, warranting a score of 0.

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

Only abstract available; no free parameters, axioms, or invented entities can be extracted beyond the high-level claim that NIR flash shading is ambient-invariant.

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Cite this review

Pith. "Pith review of Ambient-robust Inverse Rendering using Active RGB-NIR Imaging." pith.science (2026). https://pith.science/paper/PZACUCKZ

@misc{pith2026260530250,
  author       = {Pith},
  title        = {Pith review of: Ambient-robust Inverse Rendering using Active RGB-NIR Imaging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PZACUCKZ}},
  note         = {Machine review of arXiv:2605.30250}
}
read the original abstract

Inverse rendering aims to reconstruct geometry and reflectance of objects from images. Despite recent progress, existing methods often produces inaccurate reconstructions that are sensitive to ambient illumination conditions. Here we introduce an ambient-robust inverse rendering method enabled by active RGB-NIR imaging. Our key insight is to leverage near-infrared (NIR) flash illumination-imperceptible to human observers-to obtain stable point-light shading that is largely invariant to ambient illumination. By using multi-view RGB images illuminated by ambient light and NIR images acquired with active NIR flash illumination, we reconstruct accurate geometry and reflectance by exploiting the complementary benefits of RGB and NIR images via a three-stage inverse rendering method. To enable dense multi-view acquisition, we develop an active imaging system equipped with a RGB-NIR camera and a NIR flash mounted on a mobile base. Using this system, we collect the first multi-view RGB-NIR inverse rendering dataset captured under multiple ambient illumination conditions. Experiments demonstrate that our method outperforms prior approaches, achieving accurate geometry and reflectance estimation across multiple ambient lighting scenarios.

Figures

Figures reproduced from arXiv: 2605.30250 by the authors.

Figure 1
Figure 1. (a) We present an imaging system comprised of an RGB–NIR camera and an NIR flash mounted on a robotic arm attached to a mobile base. Using [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. RGB–NIR vision system and image processing pipeline. (a) Our vision system is composed of a robotic arm, a mobile robot base, and a pixel-aligned RGB–NIR camera with an NIR flash. Insets show each component as well as the spectral profile of the camera and the NIR flash. (b) Sample of RGB-NIR image pairs. We acquire RGB and NIR frames with multiple exposure time. For NIR, we subtract NIR flash on image and NIR flash… view at source ↗
Figure 3
Figure 3. RGB-NIR inverse rendering dataset. (a) We capture multi-view RGB–NIR image pairs with active NIR flash for four real-world objects under different ambient illumination conditions. (b) We render synthetic dataset using Mitsuba 3 [Jakob et al. 2022], matching the real-world acquisition setup. SIGGRAPH Conference Papers ’26, July 19–23, 2026, Los Angeles, CA, USA [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Three-stage RGB–NIR inverse rendering. We initialize geometry using RGB images. NIR images with flash lighting are then used to estimate reflectance in a manner robust to ambient lighting and further refine geometry. RGB albedo and RGB environment map are then estimate…
Figure 6
Figure 6. Figure 6: RGB environment inverse rendering. Given the parameters from Stage 2, we estimate RGB albedo and environment map using multi￾view RGB images. The fire icon indicates trainable parameters, while the snowflake icon denotes fixed parameters. ˆ𝐼 NIR and 𝐼 NIR , Lgeom is th…
Figure 7
Figure 7. Figure 7: Validation of the RGB–NIR BRDF model. Sharing roughness and metallic across RGB and NIR channels enables accurate modeling of the measured hyperspectral BRDFs [Dupuy and Jakob 2018]. diffuse albedo independently for each channel. We validate this as￾sumption by fitting…
Figure 8
Figure 8. Figure 8: Comparison with passive RGB inverse rendering methods. Our method enables ambient-robust reconstruction, outperforming passive RGB inverse rendering approaches: R3DG [Gao et al. 2024], GS-IR [Liang et al. 2024], and IRGS [Gu et al. 2025]. Input RGB image Roughness Wild…
Figure 10
Figure 10. Figure 10: Comparison with diffusion-based inverse rendering. We re￾construct RGB diffuse albedo more accurately than MaterialFusion [Litman et al. 2025], with less shading contamination and color bias. artifacts and color bias in the estimated diffuse albedo. Additional results…
Figure 11
Figure 11. Figure 11: Ambient-robust reconstruction across environment maps on real-world dataset. Our method reconstructs surface reflectance for real-world objects under multiple ambient illumination conditions, producing stable reflectance and environment estimation despite lighting var…
Figure 13
Figure 13. Figure 13: Reflectance reconstruction under real-world outdoor illumi￾nations Our method reconstructs consistent reflectance under two different outdoor illuminations containing NIR ambient light. Realistic Relighting. While the reconstructed geometric and re￾flectance parameter…
Figure 12
Figure 12. Figure 12: RGB-diffuse albedo reconstruction across environment maps. Across diverse environment maps, our method robustly recovers consistent RGB diffuse albedo for synthetic objects, demonstrating effective disentan￾glement of surface reflectance from varying ambient illuminat…
Figure 15
Figure 15. Figure 15: Impact of NIR flash inverse rendering. NIR flash inverse ren￾dering improves reconstruction accuracy of our method by leveraging NIR point-light shading. leading to degraded performance. This issue may be mitigated by using a brighter NIR source or reducing the object…
Figure 16
Figure 16. Figure 16: Dependency on material types and scene complexity. Our method is robust across diverse material appearances, enabling accurate reconstruction for diffuse, specular, and metallic surfaces. We also reconstruct accurate reflectance and ambient illumination of multiple ob…

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Works this paper leans on

4 extracted references · 3 canonical work pages

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