TransparentGS: Fast Inverse Rendering of Transparent Objects with Gaussians

Bo Ren; Chengzhi Tao; Dongwei Ye; Huiwen Liu; Jialin Dan; Jie Guo; Kun Zhou; Letian Huang; Yanwen Guo; Yuanqi Li

REVIEW 3 major objections 5 minor 4 references

TransparentGS: Fast Inverse Rendering of Transparent Objects with Gaussians

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

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Pith's one-line read The paper claims that transparent objects with sharp refraction and reflection can be inverse-rendered from ordinary multi-view photos in under an hour, then re-rendered in real time.

desk verdict TransparentGS deserves serious refereeing: a strong systems contribution with a real but acknowledged convergence gap in IterQuery. read the letter →

arxiv 2504.18768 v2 pith:CK3NN2VJ submitted 2025-04-26 cs.GR cs.CV

Letian Huang , Dongwei Ye , Jialin Dan , Chengzhi Tao , Huiwen Liu , Kun Zhou , Bo Ren , Yuanqi Li

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Yanwen Guo Jie Guo

This is my paper · ORCID

classification cs.GRcs.CV

keywords 3DGaussianSplattingtransparentobjectreconstructioninverserenderingrefractionreflectionlightfieldprobesnovelviewsynthesismaterialediting

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

TransparentGS claims that glass-like objects do not need per-view spherical-harmonic colors, slow ray marching, or a network that guesses refracted light. Instead, it extends 3D Gaussian Splatting surface primitives with physical material parameters and renders them through deferred shading, while an environment baked into small Gaussian light-field probes supplies the reflected and refracted light. An iterative depth-alignment step removes the parallax error that comes from averaging several probes, so nearby objects appear crisply through the glass. If the claim holds, transparent objects can be captured from smartphone photos in about an hour and re-rendered in real time with editable materials and separately controllable reflection, refraction, and base color.

What carries the argument

The mechanism is transparent Gaussian primitives paired with baked Gaussian light field probes. Each primitive adds surface material attributes to the usual Gaussian geometry, and deferred shading aggregates the normal, hitting point, and material into a G-buffer before a single secondary ray is cast. Each probe stores a 360-degree color-and-depth panorama of the environment as seen from its position, encoding both distant ambient light and nearby objects; the IterQuery algorithm then uses the depth panoramas to iteratively correct each probe's query direction, driving the probe hit points along the scene surface until they coincide with the first intersection of the queried ray. The fixed-point argument is supplied for one probe, while the paper reports $K=8$ or $K=64$ probes with five iterations in practice.

What would settle it

Reconstruct a scene with a thin, high-contrast rod placed behind a transparent object, then render a view where the rod should appear refracted through the glass. If the manifold assumption gives way, the rod will appear at the wrong position, blurred, or doubled near silhouettes, and inspecting the probe depth panoramas along those rays will show more than one depth value in a single direction.

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Extended reading notes

Core claim

The central claim is that fast inverse rendering of transparent objects becomes possible when appearance is split into physical surface parameters rather than view-dependent color. Each transparent Gaussian primitive carries normal, roughness, metalness, transparency, index of refraction, and base color, and these are $\alpha$-blended into G-buffers before shading, so refraction is sampled once with an aggregated normal and hitting point instead of being averaged per primitive. Incident light is supplied by Gaussian light field probes: sparse caches that store color and depth panoramas of the surrounding scene. The IterQuery algorithm repeatedly re-aims each probe along a refracted or reflected ray until the probes' stored depths indicate the same first intersection with the environment, which removes the parallax blur that naively averaging probes would introduce. On a synthetic dataset with ground truth, the paper reports a mean angular error of $5.53^\circ$ for reconstructed normals, substantially lower than the compared baselines, together with higher PSNR for reflection, refraction, and base-color components, while training in under an hour and rendering at 31–51 frames per second.

Load-bearing premise

Each probe's panorama stores only the nearest surface in every direction, so the iterative alignment assumes a refracted or reflected ray hits exactly one scene point per direction; at sharp silhouettes and gaps this is violated, and probes can converge to the wrong background location, bending the reflected or refracted light incorrectly.

Editorial extensions

If this is right

Transparent-scene capture becomes an hour-scale operation with real-time playback, since training cost is dominated by efficient Gaussian rasterization rather than dense network sampling.
Material editing and relighting become practical: normal, roughness, transparency, index of refraction, and base color are decoupled and can be changed independently before re-rendering.
Reconstructed assets can be composed into larger scenes where mirrors, glass, and glossy objects produce secondary ray effects through the same probe query path.
Colored transparent objects can have absorption decoupled from refraction, so an object's inherent color and the background seen through it are separated rather than entangled.
Non-pinhole cameras, including fisheye and panoramic views, can be rendered through the same probe-based pipeline without changing the representation.

Reading between the lines

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

If the single-valued depth assumption behind IterQuery were replaced by multilayer depth or multiple candidate depths per ray, the same probe architecture could plausibly handle self-occluding, hollow, and nested transparent geometries, which the paper names as its main failure mode.
The probe baking stage separates environment reconstruction from transparent-object optimization, so a dynamic scene could be handled by re-baking probes periodically rather than retraining the whole pipeline.
The iterative depth alignment is essentially a coordinate-descent solver for light-path intersection, and the same trick may transfer to other probe-based light field representations, such as radiance caches for glossy indirect illumination, wherever a single first intersection per direction is a good approximation.
Because the convergence proof is only given for one probe, the practical $K=8$ and $K=64$ settings likely rely on ensemble averaging to stabilize silhouettes; a testable extension would measure how often individual probes oscillate or lock onto the wrong background point near depth discontinuities.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: this is a genuine advance in the speed of transparent-object inverse rendering, and the paper is mostly honest about what it can and cannot do. The headline claim, 'first fast inverse rendering with secondary ray effects within one hour,' is plausible and backed by scaling comparisons against Eikonal (22-24h) and NU-NeRF (8-9h). The pipeline pieces are borrowed, but the integration is new: transparent Gaussian primitives with a deferred refraction strategy, GaussProbe for baking indirect light, and IterQuery for parallax correction. That combination gives real improvements in LPIPS and normal MAE on the reported scenes, and the synthetic ground-truth comparisons are a good check on the inverse rendering claims.

The soft spots are real but not disqualifying. The IterQuery convergence section is the weakest link. The proof only handles K=1 and shows a fixed-point condition, not convergence from the initial direction or first-intersection guarantee. For K=8 and K=64 there is no analysis, and Section 4.5 openly shows cases where probes converge to wrong points at depth discontinuities. The stress-test note adds a good point: the convergence check re-queries the same baked depth map, so a self-consistent but incorrect fixed point is invisible to the algorithm. Still, the paper acknowledges these failure modes and shows empirically that more probes help. I would not call this a fatal flaw, but it does mean the strongest claims about high-fidelity refraction need a tighter argument or a quantitative ablation.

Other soft spots: no code or data release, no error bars across multiple runs, and the color-absorption model replaces Beer-Lambert with a learned base color. The last one seems like a reasonable engineering choice given the difficulty of decoupling, and it works in the experiments.

Who is this for? Anyone doing inverse rendering or 3D capture of specular/transparent objects, and the applications section shows practical compositing value. It deserves a serious referee: the novelty is adequate, the experiments cover real and synthetic data, and the limitations section is refreshingly candid. My recommendation is to send it to peer review with a request for code/data and a deeper convergence analysis or at least a quantitative ablation of IterQuery iterations and probe counts.

Referee Report

3 major / 5 minor

Summary. The paper presents TransparentGS, a 3D Gaussian Splatting-based inverse rendering pipeline for transparent objects. The method introduces transparent Gaussian primitives with explicit material attributes, a deferred refraction shading strategy, and baked Gaussian light field probes (GaussProbe) that encode both ambient light and nearby indirect light. To query these probes for reflected and refracted rays, the paper proposes IterQuery, a depth-based iterative algorithm intended to compensate for parallax among multiple probes. The authors claim training within one hour, real-time rendering, and state-of-the-art results for novel view synthesis and inverse rendering on synthetic and real-captured datasets, including support for material editing and composition with secondary ray effects.

Significance. If the claims hold, this is a substantial practical advance: it brings transparent-object inverse rendering into the speed regime of 3D-GS while recovering editable materials and secondary ray effects. Strengths include the explicit physically-based deferred shading pipeline, the synthetic ground-truth evaluation of decoupled reflection, refraction, and base color in Table 5, careful ablations of the deferred strategy and GaussProbe in Table 6, and a reproducible experimental setup with real-captured data. The main concern is that the central IterQuery component lacks a persuasive convergence analysis, and the paper's own limitations section documents failure cases that directly affect the claimed high-fidelity secondary rays.

major comments (3)

[3.3, Eqs. 16-21 and Listing 1] The fixed-point derivation for K=1 (Eqs. 19-21) shows only that a stationary point satisfies o+t_hat d = p_i + t_i d_i; it does not prove that the iteration converges from the initialization of Eq. 15, nor that the fixed point is the first scene intersection along the query ray. For the used configurations K=8 and K=64 no convergence argument is supplied, and Eq. 17's trilinear interpolation implicitly assumes the probe depth maps agree on a single surface point. Section 4.5 and Fig. 18 concede that at depth discontinuities 'not all probes converge to the first intersection,' yielding incorrect results. Because the same converged directions d_i are used to sample the color panorama Phi, this gap is load-bearing for the headline refraction/reflection fidelity claim. Please provide a convergence proof for finite K under defined manifold conditions, or empirically bound the frequency and magnitude of the failure cases on the reported scenes, or restrict the claim accordingly.
[3.3, Listing 1 line 13] The stopping criterion compares the probe depth re-queried with the updated direction against the previous depth. This is a self-consistency check: a wrong fixed point that is not the first intersection is accepted as converged. In particular, the algorithm cannot detect that the query ray crosses a depth discontinuity of the baked environment. The paper should either devise a termination test that verifies the first-intersection property or state this limitation explicitly alongside the convergence claim.
[3.2, Eq. 11] The Beer-Lambert transmittance exp(-sigma(lambda)d) is approximated by the alpha-weighted base color sum T_i alpha_i b_i, which is an unconstrained optimizable parameter rather than a physically derived absorption coefficient. While Table 5 reports base-color PSNR on synthetic scenes, the claim that this design 'effectively decouples the refraction term from the object's inherent color' would be strengthened by an ablation showing that the learned transmittance does not absorb errors from other components of the pipeline, for example by fixing b_i to 1 and observing the effect on the decoupled refraction map.

minor comments (5)

[Section 1] The phrase 'baked and updated fastly' should read 'quickly' or 'rapidly'.
[Section 4.3 heading] The heading 'Ablation studies on inserse rendering' contains a typo: 'inserse' should be 'inverse'.
[Figure 5 caption] The caption entry 'Naïve (Initial)IterQuery' is missing a space before 'IterQuery'.
[Section 4.2] The reported frame rate of 31-51 FPS is not directly comparable to the per-query times given (0.002-0.005 s); clarifying the relationship, for example by amortizing probe baking or including the full deferred shading cost, would aid reproducibility.
[Table 3] For the Glass scene, the PSNR of Ours (27.12) is slightly lower than that of Eikonal (27.15); the text says 'we surpass other methods in most scenes,' which is accurate but should be phrased to avoid overstating the PSNR results on this dataset.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the headline results are independently benchmarked, and the IterQuery convergence gap is a robustness limitation, not a circular step.

full rationale

TransparentGS's central claim—fast inverse rendering of transparent objects with secondary ray effects—is validated against external, non-circular evidence: held-out novel views on real-captured scenes, and a synthetic Blender dataset with ground-truth normal, reflection, refraction, and base-color maps (Sec. 4.1–4.2, Tables 3–5). The material attributes (roughness, metalness, transparency, IOR, base color) are optimized as inverse-rendering unknowns against input photographs; this is the standard use of data, not a hidden prediction, and the synthetic ground truth is not derived from the method. The GaussProbe environment is baked from the segmented environment via vanilla 3D-GS and the optimal projection strategy [Huang et al. 2024]; that citation is a peer-reviewed, externally checkable mathematical result and is not used to force the paper's conclusions. The IterQuery algorithm is a fixed-point procedure whose purpose is to reduce parallax in probe queries; its K=1 derivation (Eqs. 19–21) merely characterizes a fixed point and does not prove convergence, and Sec. 4.5 openly acknowledges failure cases at depth discontinuities. This is an unresolved correctness/robustness gap, not a circular reduction of the output to the input. No fitted quantity is renamed as a prediction, no load-bearing argument reduces to a self-citation, and no known result is merely renamed. Therefore the paper exhibits no significant circularity.

Assumptions & free parameters 7 free parameters · 6 assumptions · 2 invented entities

The central claim relies on standard shading equations plus a set of domain assumptions about segmentation, single-valued depth manifolds, deferred alpha-blended ray estimation, and a simplified two-refraction light path. The material and probe parameters are fitted or hand-set rather than derived, and the colored-absorption model introduces an ad hoc base-color substitution. No new physical entity is postulated.

free parameters (7)

roughness rho
Per-Gaussian surface roughness optimized to match input photos; used in the Cook-Torrance BRDF in Eqs. 3-5.
metallic m
Per-Gaussian metalness optimized; blends specular and diffuse reflection in Eq. 4.
transparency t
Per-Gaussian scalar interpolating between BRDF and BTDF in Eq. 4; central to the transparent rendering model.
index of refraction eta
Per-object or per-Gaussian IOR controlling Snell refraction; Fig. 19 shows values 1.1 and 1.45 and a varying IOR map.
base color b_i
Per-Gaussian optimizable color parameter used to approximate Beer-Lambert absorption in Eq. 11; it is a fitted albedo rather than a physically measured transmittance.
number of probes K and IterQuery iteration count = K=8 or 64, iterations=5
Hand-set in Section 4.1; convergence and rendering quality depend on both, as shown in Fig. 15.
loss weights lambda1, lambda2, lambda3 = 0.2, 0.2, 1
Fixed manually in Section 4.1 with no sensitivity analysis reported.

assumptions (6)

domain assumption The scene can be separated into an opaque environment and transparent foreground using SAM2 and GroundingDINO segmentation.
Section 3.4 and Fig. 7 use segmentation masks to remove transparent objects before baking GaussProbe. Segmentation errors are discussed in Section 4.5 but still affect the baked environment.
domain assumption Each GaussProbe depth panorama is a single-valued 2D manifold of first intersections.
Required for the IterQuery fixed-point iteration in Section 3.3 and Eqs. 16-18. Section 4.5, Fig. 18 admits discontinuities that violate this assumption.
domain assumption A single alpha-weighted normal and alpha-weighted hit point per pixel determines the correct refracted and reflected ray.
The deferred refraction strategy in Section 3.2, Eqs. 8-10, collapses multiple Gaussian surface samples into one normal and one hit point, which is an approximation for multi-layer transparent surfaces.
domain assumption Light paths inside transparent objects consist of exactly two refractions with at most one total internal reflection.
Stated in Section 4.5; hollow objects and intricate self-occlusion are excluded, which limits the scope of the central claim.
ad hoc to paper Beer-Lambert absorption can be approximated by the alpha-weighted learned base color.
Eq. 11 introduces e^{-sigma(lambda)d} and the following text replaces it with the alpha-weighted base color. This is a modeling choice not derived from physical path length, and it is not independently validated.
standard math The Cook-Torrance BRDF with Schlick Fresnel and perfect specular transmission is adequate for transparent Gaussian primitives.
Used in Section 3.2, Eqs. 3-6 as the physical shading basis; this is a standard graphics assumption.

invented entities (2)

Transparent Gaussian primitives
purpose: Extend 3D-GS primitives with normals and BSDF parameters for physically based shading of transparent surfaces.
A computational representation introduced by the paper, not a falsifiable physical entity; its utility is shown only within the paper's own experiments.
GaussProbe
purpose: Baked panoramic RGBD light-field probes that encode ambient light and nearby indirect light for querying refraction and reflection.
An algorithmic data structure built from reconstructed 3D-GS scenes; it makes no independently measurable prediction outside the pipeline.

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Cite this review

Pith. "Pith review of TransparentGS: Fast Inverse Rendering of Transparent Objects with Gaussians." pith.science (2026). https://pith.science/paper/CK3NN2VJ

@misc{pith2026250418768,
  author       = {Pith},
  title        = {Pith review of: TransparentGS: Fast Inverse Rendering of Transparent Objects with Gaussians},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CK3NN2VJ}},
  note         = {Machine review of arXiv:2504.18768}
}

read the original abstract

The emergence of neural and Gaussian-based radiance field methods has led to considerable advancements in novel view synthesis and 3D object reconstruction. Nonetheless, specular reflection and refraction continue to pose significant challenges due to the instability and incorrect overfitting of radiance fields to high-frequency light variations. Currently, even 3D Gaussian Splatting (3D-GS), as a powerful and efficient tool, falls short in recovering transparent objects with nearby contents due to the existence of apparent secondary ray effects. To address this issue, we propose TransparentGS, a fast inverse rendering pipeline for transparent objects based on 3D-GS. The main contributions are three-fold. Firstly, an efficient representation of transparent objects, transparent Gaussian primitives, is designed to enable specular refraction through a deferred refraction strategy. Secondly, we leverage Gaussian light field probes (GaussProbe) to encode both ambient light and nearby contents in a unified framework. Thirdly, a depth-based iterative probes query (IterQuery) algorithm is proposed to reduce the parallax errors in our probe-based framework. Experiments demonstrate the speed and accuracy of our approach in recovering transparent objects from complex environments, as well as several applications in computer graphics and vision.

Figures

Figures reproduced from arXiv: 2504.18768 by the authors.

**Figure 1.** TransparentGS is a novel inverse rendering pipeline based on 3D-GS, specifically designed for reconstructing transparent objects under various light [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

**Figure 2.** The overview of our TransparentGS pipeline. Each 3D scene is firstly separated into transparent objects and opaque environment using SAM2 [Ravi et al. 2024] guided by GroundingDINO [Liu et al. 2024]. For transparent objects, we propose transparent Gaussian primitives, which explicitly encode both geometric and material properties within 3D Gaussians. And the properties are rasterized into maps for subsequent deferre… view at source ↗

**Figure 3.** Difference between forward and deferred refraction strategies. The gray arrows denote the normal attributes n𝑖 of transparent Gaussian primitives (the blue ellipsoids). Deferred refraction integrates the alphaweighted normal N and the alpha-weighted hitting point X, and samples the GaussProbe with the single refracted ray 𝜔𝑡 (the orange arrow). some work on deferred reflection [Wu et al. 2024a; Ye et al. 2024], we … view at source ↗

Figures from the paper (16 more)

**Figure 4.** Figure 4: Illustration of our baking pipeline for Gaussian light field probes. Given a set of environmental images with the transparent object removed, we can reconstruct the 3D scene using the original 3D-GS [Kerbl et al. 2023]. We voxelize the scene and place virtual cameras a…

**Figure 5.** Figure 5: Illustration of our depth-based iterative Gaussian probes query (IterQuery) algorithm. For clarity, we illustrate four selected probes, with positions p1, p2, p3, and p4, chosen from the eight (orange circles). The black camera with the red arrow represents the queried…

**Figure 6.** Figure 6: Illustration of our mesh-GS fusion strategy. GS→Mesh: We use transparent Gaussian primitives to guide the primary ray sampling of the SDF, efficiently generating an accurate mesh. Mesh→GS: We employ the mesh as a proxy for fast secondary ray tracing, which are then uti…

**Figure 7.** Figure 7: 3D scene segmentation results on the Glass scene. Top left: Image segmentation results. Bottom left: Segmented scene represented by the original 3D-GS [Kerbl et al. 2023]. Right: Probes baked from the segmented scene. the estimated SDF. We extract the explicit mesh fro…

**Figure 8.** Figure 8: Qualitative comparison of novel-view synthesis and inverse rendering results on real-captured scenes with colorless transparent objects. The left image showcases the transparent object along with its specular reflection component decoupled using our method. The black r…

**Figure 9.** Figure 9: Surface mesh reconstruction results of our method on the real-captured and synthetic datasets. we present quantitative results measured with three standard metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) [Wang et al. 2004] and Learned Pe…

**Figure 10.** Figure 10: Qualitative comparison of novel-view synthesis and inverse rendering results on real-captured scenes with colored transparent objects. The left images showcase the input images of the real-captured scenes with colored transparent objects. By handling absorption as des…

**Figure 11.** Figure 11: Qualitative comparison of novel-view synthesis and inverse rendering results on the synthetic dataset. 2.5 1.0 Rendering Normal Reflected direction Refracted direction IOR Fresnel term Reflection Refraction Hitting point Environment Mask GaussProbe [PITH_FULL_IMAGE:f…

**Figure 12.** Figure 12: Detailed intermediate results of our method. Left: the environment and the corresponding GaussProbe. Right: maps of additional parameters. terms of LPIPS, and achieve the highest average metrics. Since realcaptured scenes lack ground-truth geometry, material, and lig…

**Figure 13.** Figure 13: Qualitative ablation studies on the KITTY scene. “Deferred” and “Forward" mean the deferred shading strategy and the forward shading strategy, respectively [PITH_FULL_IMAGE:figures/full_fig_p012_13.png]

**Figure 15.** Figure 15: The impact of probes configurations on the convergence of the IterQuery algorithm. The leftmost image illustrates the number of the probes 𝐾 and the placement of the probes. In the 3rd iteration of the second row’s reflection, it appears clearer but is incorrect, as o…

**Figure 15.** Figure 15: Fortunately, this can be resolved by increasing the number [PITH_FULL_IMAGE:figures/full_fig_p014_15.png]

**Figure 16.** Figure 16: Applications. Our method supports the rendering and navigation of scenes that integrate triangle meshes, traditional 3D-GS [Kerbl et al. 2023] and transparent Gaussian primitives, as well as non-pinhole cameras. our method demonstrates sufficient robustness to such in…

**Figure 17.** Figure 17: Robustness of our method to segmentation results. The red boxes highlight the regions with imprecise segmentation results [PITH_FULL_IMAGE:figures/full_fig_p015_17.png]

**Figure 18.** Figure 18: Failure cases for the IterQuery algorithm. The green box highlights the regions where our IterQuery algorithm fails when there are nonnegligible singularities that clearly violate the manifold constraint. 2.5 1.0 Rendering IOR map IOR=1.1 IOR=1.45 Varying IOR [PITH…

**Figure 19.** Figure 19: Transparent objects with different IORs. Higher index of refraction values are depicted in blue, and lower values are depicted in green. 3D-GS. By introducing the transparent Gaussian primitives, which incorporate material attributes alongside positions and shapes, o…

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Reference graph

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Pith tools

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

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