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arxiv: 2606.31637 · v1 · pith:QDPJBGM3new · submitted 2026-06-30 · 💻 cs.GR · cs.AI

Intrinsic decomposition and editing of 3D Gaussian splats

Alexandre Lanvin , Jeffrey Hu , Simon Lucas , Adrien Bousseau , George Drettakis This is my paper

Pith reviewed 2026-07-01 02:46 UTC · model grok-4.3

classification 💻 cs.GR cs.AI
keywords intrinsic decompositionGaussian splattingalbedoshading3D scene editingmulti-view optimizationradiance fields
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The pith

Modeling intrinsic decomposition as independent Gaussian primitive sets disentangles multi-view images into albedo and shading for consistent 3D editing.

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

The paper tries to establish that representing albedo and shading as separate sets of Gaussian primitives, separated by an optimization guided by data-driven predictions, turns multi-view photographs into an editable 3D radiance field. A sympathetic reader would care because this separation lets texture changes made in one photo survive re-rendering from new viewpoints while keeping lighting plausible. The work focuses on planar surfaces and supplies a workflow that records the edit directly in the decomposed representation.

Core claim

Intrinsic decomposition is modeled using independent sets of Gaussian primitives for albedo and shading. An optimization procedure guided by data-driven predictions disentangles multi-view photographs into these sets. This enables an editing workflow in which users modify the albedo of a planar surface in one image, after which the edited radiance field can be re-rendered with plausible lighting from arbitrary viewpoints.

What carries the argument

Independent sets of Gaussian primitives for each intrinsic component, with each set adapting to the characteristics of its layer during optimization.

If this is right

  • Edits performed on albedo in a single image become visible with correct shading from any viewpoint.
  • Lighting and view-dependent effects remain unchanged when only the albedo component is modified.
  • The separation supports direct texture modification on planar surfaces without post-processing.
  • The intrinsic sets allow the radiance field to be re-rendered under arbitrary viewpoints after capture of the edit.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same separation principle could be tested on non-planar geometry if the optimization is extended beyond planar assumptions.
  • The workflow might combine with existing 2D image editors to import edits into full 3D scenes.
  • Successful disentanglement could reduce the need for manual correction when relighting the edited scene.

Load-bearing premise

Data-driven predictions can guide the optimization to separate albedo and shading into independent Gaussian sets without introducing view-inconsistent artifacts.

What would settle it

A rendering from a novel viewpoint after an albedo edit on a planar surface that shows color bleeding, lighting mismatch, or view-dependent artifacts would falsify the decomposition claim.

Figures

Figures reproduced from arXiv: 2606.31637 by Adrien Bousseau, Alexandre Lanvin, George Drettakis, Jeffrey Hu, Simon Lucas.

Figure 1
Figure 1. Figure 1: By decoupling albedo and shading in a radiance field (a-b) our method enables direct edition of albedo in a physically plausible [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The intrinsic image model expresses the observed image (left) as the product of diffuse albedo and shading, summed with a [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the pipeline for intrinsic reconstruction using distincts sets of Gaussians. Our pipeline takes as input multiple [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Each intrinsic field is optimized to represent different quantities. In Albedo field* and Shading field*, we have artificially scaled [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: To capture a user edit, we initialize new Gaussians on the textured proxy plane (a). These Gaussians are added to the set of [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: We added custom widgets to enable the selection of different intrinsic fields and editing of the albedo field. The user can then [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Intrinsic decomposition and albedo editing on real scenes. See accompanying video for animated viewpoints. [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Intrinsic decomposition and albedo editing on a synthetic scene, using ground-truth albedo for supervision. By representing [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Compared to our approach, GI-GS [Chen et al [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Our intrinsic decomposition enables realistic editing of colors and textures compared to previous methods where shading [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Our synthetic scene Manuscript submitted to ACM [PITH_FULL_IMAGE:figures/full_fig_p013_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Shading field reconstruction with color regularization turned off/on. Our shading reconstruction is more constrained while [PITH_FULL_IMAGE:figures/full_fig_p014_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Predicted albedo images can lack details and exhibit a slight color shift due to unknown nonlinearities, which yields blurrier [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: DiffusionRenderer tends to predict blurry albedo images (b). As a result, the high-frequency details present in the original [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
read the original abstract

Intrinsic decomposition which expresses image colors as the product of diffuse albedo and shading, possibly augmented with view-dependent residuals has a long history in image editing as it enables the modification of object colors and textures without altering lighting. We extend intrinsic decomposition to radiance fields represented with Gaussian splatting by proposing solutions to three key aspects of such decomposition. First, we describe how to model the intrinsic decomposition as independent sets of Gaussian primitives, which allows each set to adapt to the characteristics of the layer it represents. Second, we present an optimization procedure guided by data-driven predictions to disentangle multi-view photographs of a scene into the aforementioned intrinsic sets. Finally, we provide an editing workflow where users modify the texture of planar surfaces simply by modifying the albedo of that surface in one image. Capturing this edit within the intrinsic radiance field allows re-rendering of the edited scene with plausible lighting under arbitrary viewpoints.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper extends intrinsic decomposition to 3D Gaussian splatting by representing albedo and shading (plus view-dependent residuals) as independent sets of Gaussian primitives. It describes an optimization procedure that uses data-driven predictions to disentangle multi-view photographs into these separate intrinsic sets, and supplies an editing workflow in which a user edit to albedo on a planar surface in one image is propagated to enable consistent re-rendering with plausible lighting from arbitrary viewpoints.

Significance. If the separation holds, the work would enable practical intrinsic editing inside a popular real-time radiance-field representation. Modeling the layers as independent primitive sets is a clear strength, as it lets each component adapt its density and appearance statistics without forcing a shared parameterization. The guided optimization and single-view editing workflow are also positive contributions that could reduce the need for post-hoc corrections. No machine-checked proofs or parameter-free derivations are claimed, but the approach is falsifiable via standard view-consistency and editing metrics.

minor comments (2)
  1. The abstract and introduction would benefit from a short statement of the concrete loss terms or prediction networks that guide the disentanglement, even if full details appear later.
  2. Figure captions and the editing section should explicitly note whether any post-optimization correction or view-consistency regularizer is applied after the initial separation.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the constructive and positive review, including the favorable assessment of the modeling approach, optimization procedure, and editing workflow. The recommendation of minor revision is noted. No specific major comments were provided in the report, so our response below addresses the overall feedback.

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The provided abstract and description model intrinsic decomposition via independent Gaussian primitive sets, an optimization guided by external data-driven predictions, and a user editing workflow. No equations, loss terms, fitted parameters, or self-citations are shown that would reduce any claimed prediction or decomposition to a tautology, self-definition, or input by construction. The central claims rely on external guidance rather than internal reductions, rendering the derivation self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Only the abstract is available, so the ledger is necessarily incomplete. The central modeling choice of independent Gaussian sets for albedo and shading is treated as a domain assumption rather than derived.

axioms (1)
  • domain assumption Data-driven predictions suffice to guide separation of albedo and shading across multiple views into independent Gaussian sets.
    The optimization procedure described in the abstract relies on this separation being achievable.

pith-pipeline@v0.9.1-grok · 5683 in / 1146 out tokens · 73077 ms · 2026-07-01T02:46:10.415256+00:00 · methodology

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