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arxiv: 2607.02372 · v1 · pith:2AAF7Y3Snew · submitted 2026-07-02 · 💻 cs.CV

Learning Spectral and Polarimetric Clues for One-to-Multimodal Novel View Synthesis

Federico Lincetto , Gianluca Agresti , Mattia Rossi , Piergiorgio Sartor , Pietro Zanuttigh This is my paper

Pith reviewed 2026-07-03 15:18 UTC · model grok-4.3

classification 💻 cs.CV
keywords neural renderingnovel view synthesismultimodalinfraredpolarimetricmultispectralimplicit representation
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The pith

Pre-training on multimodal scenes enables RGB-only fine-tuning to render infrared, polarimetric, and multispectral views.

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

The paper introduces a method to synthesize novel views in non-RGB modalities without needing to capture those modalities for each new scene. It does this by first pre-training a neural renderer on scenes that supply multiple types of images so that the model learns how the modalities relate to one another. Those learned relations then allow the model, when adapted to a fresh scene using only its RGB photographs, to output consistent images in the other modalities as well. A reader would care because this removes the requirement for costly specialized cameras on every target scene.

Core claim

SPoILeR performs a multimodal pre-training phase in which the model learns the mutual correlation between different modalities. This correlation knowledge then supports accurate prediction of unconventional modalities during fine-tuning that is supervised solely by RGB images, yielding multi-view consistent renderings of infrared, polarimetric, and multispectral frames even when no samples from those sensors are available for the scene.

What carries the argument

The Spectral and Polarimetric Implicit Learned Representation (SPoILeR), which encodes learned correlations across imaging modalities to enable transfer from pre-training to RGB-supervised fine-tuning.

If this is right

  • The approach produces accurate renderings of infrared, polarimetric, and multispectral data without any input samples from those sensors.
  • Renderings remain multi-view consistent across the new scene.
  • Fine-tuning requires supervision from RGB frames only.

Where Pith is reading between the lines

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

  • If modality correlations prove stable across scenes, the method could be applied to additional imaging types such as thermal or depth data.
  • Applications in fields that use multiple sensors might reduce their dependence on full multimodal capture setups.

Load-bearing premise

Correlations between modalities discovered during pre-training on some scenes will transfer reliably to new scenes that provide only RGB supervision.

What would settle it

Acquire real infrared or polarimetric images of a previously unseen scene and measure whether the model's RGB-only renderings match those ground-truth captures within expected error bounds; systematic mismatch would falsify the transfer claim.

Figures

Figures reproduced from arXiv: 2607.02372 by Federico Lincetto, Gianluca Agresti, Mattia Rossi, Piergiorgio Sartor, Pietro Zanuttigh.

Figure 1
Figure 1. Figure 1: The proposed approach firstly learns the correlation across multiple modalities on a collection of scenes. Then, a single-scene fine-tuning on RGB data alone produces a model able to render views of arbitrary modalities for the considered scene. tasks or enhance result quality. For example, there exist Multispectral (MS) sensors that are sensitive to different bands of visible light, Near-Infrared (NIR) se… view at source ↗
Figure 2
Figure 2. Figure 2: Radiance module architecture scheme: fea￾tures from basis and coefficients are combined and decoded into different radiance modalities [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 5
Figure 5. Figure 5: Scheme of the inverse func￾tion module I. It predicts the esti￾mated latent vector zˆe from multi￾modal radiance values {mˆ 1, ..., mˆ k}. Latent Space Geometry Loss This regularization loss aims to promote the la￾tent space explainability. Considering that the multimodal supervision is strongly limited in the FT, it is reasonable to observe the model estimating suboptimal latents. We observed that the dec… view at source ↗
Figure 6
Figure 6. Figure 6: Scheme of the modality-to-luma module M. It encourages consistency between the refer￾ence RGB luminance g and the estimated lumi￾nance ge predicted from one random modality. As shown in [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative samples from the “Fruits” scene FT performed with only RGB data MultimodalStudio [42], we follow the same strategy of training our model with MMS-DATA raw mosaicked frames and computing the metrics only on masked foreground regions. To evaluate the quality of all rendering results, we use Peak Signal-to-Noise Ratio (PSNR) as the main metric. In addition, we also show Structural Similarity Index… view at source ↗
Figure 8
Figure 8. Figure 8: Tests with an unbalanced com￾bination of modalities. X axis refers to the number of additional modality (MS, Pol, or NIR) frames. Comparison between SPoILeR (Ours) and MMS-FW. 4.4 Unbalanced Combination of Modalities In this section, we investigate the results achieved by MMS-FW when trained with an unbalanced combination of modalities. This test is introduced by [42] and involves scenarios where many fram… view at source ↗
Figure 9
Figure 9. Figure 9: MS, Pol, and NIR render￾ings with different loss ablations DoP in terms of mean angle error (MAngE) and mean absolute error (MAbsE). In Tab. 3 we compare the results achieved by SPoILeR fine-tuning with those of MMS-FW using PolarAnything data. Our model outperforms the competitor in terms of both MAngE and MAbsE, by 14.08° and 0.044, respectively. In this case, PolarAnything cannot generate multi-view con… view at source ↗
Figure 10
Figure 10. Figure 10: Tests with an unbalanced combination of modalities. The X axis corresponds to the number of additional modality (MS or NIR) frames. Comparison between SPoILeR (Ours) and MMS-FW. Results averaged on all 16 X-NeRF scenes. procedure cannot benefit from the multi-view consistency enforced by NeRF-like models during training, as the modality conversion is performed as a final step on each rendering independent… view at source ↗
Figure 11
Figure 11. Figure 11: Qualitative renderings of the “Teddybear” scene from the FT step supervised with only RGB data. All frames are mosaicked, except RGB frames. RGB is demo￾saicked only for visualization purposes [PITH_FULL_IMAGE:figures/full_fig_p026_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Qualitative renderings of the “Toys” scene from the FT step supervised with only RGB data. All frames are mosaicked, except RGB frames. RGB is demosaicked only for visualization purposes [PITH_FULL_IMAGE:figures/full_fig_p027_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Qualitative renderings of the “Birdhouse” scene from the FT step supervised with only RGB data. All frames are mosaicked, except RGB frames. RGB is demo￾saicked only for visualization purposes [PITH_FULL_IMAGE:figures/full_fig_p028_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Qualitative renderings of the “Bouquet” scene from the FT step supervised with only RGB data. All frames are mosaicked, except RGB frames. RGB is demo￾saicked only for visualization purposes [PITH_FULL_IMAGE:figures/full_fig_p029_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Qualitative comparison between SPoILeR and MMS-FW + MST++ in terms of recovered multispectral radiance on the “Bouquet” scene. All frames are mosaicked. “before” and “after” refer to whether the RGB-to-MS conversion in performed before or after the training of MMS-FW [PITH_FULL_IMAGE:figures/full_fig_p030_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Qualitative comparison between SPoILeR and MMS-FW + MST++ in terms of recovered multispectral radiance on the “Teddybear” scene. All frames are mosaicked. “before” and “after” refer to whether the RGB-to-MS conversion in performed before or after the training of MMS-FW [PITH_FULL_IMAGE:figures/full_fig_p031_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Qualitative comparison between SPoILeR and MMS-FW + MST++ in terms of recovered multispectral radiance on the “Toys” scene. All frames are mosaicked. “before” and “after” refer to whether the RGB-to-MS conversion in performed before or after the training of MMS-FW [PITH_FULL_IMAGE:figures/full_fig_p032_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Qualitative comparison between SPoILeR and MMS-FW + MST++ in terms of recovered multispectral radiance on the “Birdhouse” scene. All frames are mosaicked. “before” and “after” refer to whether the RGB-to-MS conversion in performed before or after the training of MMS-FW [PITH_FULL_IMAGE:figures/full_fig_p033_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Qualitative comparison between SPoILeR and MMS-FW + MST++ in terms of recovered multispectral radiance on the “Bouquet” scene. All frames are mosaicked. “before” and “after” refer to whether the RGB-to-MS conversion in performed before or after the training of MMS-FW [PITH_FULL_IMAGE:figures/full_fig_p034_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Qualitative comparison between SPoILeR and MMS-FW + PolarAnything (PA) in terms of recovered polarization on the “Fruits” scene. “before” and “after” refer to whether the RGB-to-Pol conversion in performed before or after the training of MMS-FW [PITH_FULL_IMAGE:figures/full_fig_p035_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Qualitative comparison between SPoILeR and MMS-FW + PolarAnything (PA) in terms of recovered polarization on the “Teddybear” scene. “before” and “after” refer to whether the RGB-to-Pol conversion in performed before or after the training of MMS-FW [PITH_FULL_IMAGE:figures/full_fig_p036_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Qualitative comparison between SPoILeR and MMS-FW + PolarAnything (PA) in terms of recovered polarization on the “Toys” scene. “before” and “after” refer to whether the RGB-to-Pol conversion in performed before or after the training of MMS-FW [PITH_FULL_IMAGE:figures/full_fig_p037_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Qualitative comparison between SPoILeR and MMS-FW + PolarAnything (PA) in terms of recovered polarization on the “Birdhouse” scene. “before” and “after” refer to whether the RGB-to-Pol conversion in performed before or after the training of MMS-FW [PITH_FULL_IMAGE:figures/full_fig_p038_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Qualitative comparison between SPoILeR and MMS-FW + PolarAnything (PA) in terms of recovered polarization on the “Bouquet” scene. “before” and “after” refer to whether the RGB-to-Pol conversion in performed before or after the training of MMS-FW [PITH_FULL_IMAGE:figures/full_fig_p039_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Qualitative renderings of the “Chess” scene from the pre-training step with all-modality supervision. All frames are mosaicked, except RGB frames. RGB is demo￾saicked only for visualization purposes [PITH_FULL_IMAGE:figures/full_fig_p040_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: Qualitative renderings of the “Forestgang 1” scene from the pre-training step with all-modality supervision. All frames are mosaicked, except RGB frames. RGB is demosaicked only for visualization purposes [PITH_FULL_IMAGE:figures/full_fig_p041_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: Qualitative renderings of the “Laurelwreath” scene from the pre-training step with all-modality supervision. All frames are mosaicked, except RGB frames. RGB is demosaicked only for visualization purposes [PITH_FULL_IMAGE:figures/full_fig_p042_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: Qualitative renderings of the “Truck” scene from the pre-training step with all-modality supervision. All frames are mosaicked, except RGB frames. RGB is demo￾saicked only for visualization purposes [PITH_FULL_IMAGE:figures/full_fig_p043_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: Qualitative renderings of the “Aloe” scene from the pre-training step with all-modality supervision. All frames are mosaicked, except RGB frames. RGB is demo￾saicked only for visualization purposes [PITH_FULL_IMAGE:figures/full_fig_p044_29.png] view at source ↗
read the original abstract

Neural rendering techniques allow for accurate reconstruction of the geometry and color appearance of 3D scenes. Some methods have extended their use to additional imaging modalities, such as multispectral, infrared, or polarimetric data. However, all of these approaches require expensive sensors and calibrated setups to capture new multimodal frames for each new scene. We propose Spectral and Polarimetric Implicit Learned Representation (SPoILeR), a novel method to obtain multi-view consistent renderings of unconventional modalities for scenes where either only RGB frames or very few of the additional modalities are available. Thanks to a multimodal pre-training phase, the model learns the mutual correlation between different modalities. This step allows predicting accurate renderings of unconventional modalities during a fine-tuning phase supervised only by RGB images. Experimental results show that the approach can accurately render infrared, polarimetric, and multispectral frames for scenes where no input sample captured by these types of sensors is provided.

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

1 major / 1 minor

Summary. The manuscript introduces SPoILeR, a neural implicit representation method for one-to-multimodal novel view synthesis. It performs multimodal pre-training across RGB, infrared, polarimetric, and multispectral data to learn cross-modal correlations, followed by fine-tuning on RGB-only supervision for new scenes. This enables rendering of the unconventional modalities without any target-modality input samples for those scenes. The central claim is that the pre-trained correlations transfer sufficiently to produce accurate multi-view consistent renderings of IR, polarimetric, and multispectral frames.

Significance. If the transfer of pre-trained modality correlations holds for novel scenes, the approach would meaningfully lower the barrier to multimodal 3D reconstruction by eliminating the need for expensive calibrated sensors on every target scene. The paper reports experimental results demonstrating accurate renderings under RGB-only fine-tuning, which would be a practical advance if the generalization is robust.

major comments (1)
  1. [Abstract and Experimental Results section] The load-bearing assumption that cross-modal correlations learned during pre-training are sufficiently scene-independent to enable accurate rendering of non-RGB modalities with zero multimodal supervision on entirely new scenes is not adequately secured by the reported experiments. The abstract and described pipeline provide no evidence (e.g., cross-dataset testing or ablation on material/illumination variation) that the pre-training distribution covers the statistics of the test scenes.
minor comments (1)
  1. [Abstract] The abstract supplies no quantitative metrics, error analysis, or baseline comparisons, making it difficult to assess the strength of the reported results.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed review and the opportunity to clarify the generalization properties of SPoILeR. Below we address the major comment point by point.

read point-by-point responses

  1. Referee: [Abstract and Experimental Results section] The load-bearing assumption that cross-modal correlations learned during pre-training are sufficiently scene-independent to enable accurate rendering of non-RGB modalities with zero multimodal supervision on entirely new scenes is not adequately secured by the reported experiments. The abstract and described pipeline provide no evidence (e.g., cross-dataset testing or ablation on material/illumination variation) that the pre-training distribution covers the statistics of the test scenes.

    Authors: We respectfully disagree that the reported experiments fail to secure the central assumption. The pre-training corpus comprises multiple distinct scenes spanning varied materials, surface properties, and illumination conditions across all four modalities. Fine-tuning and quantitative evaluation are performed exclusively on held-out scenes that were never observed during pre-training; these test scenes were captured under different viewpoints, lighting, and material configurations from the pre-training set. The consistent accuracy of the rendered IR, polarimetric, and multispectral outputs on these unseen scenes constitutes direct empirical evidence that the learned cross-modal correlations transfer beyond the exact training scenes. While we do not conduct an explicit cross-dataset evaluation (owing to the scarcity of publicly available calibrated multimodal 3D datasets), the intra-dataset scene diversity and the zero-shot transfer results already address the core concern of scene independence. We are prepared to expand the experimental section with additional qualitative examples highlighting material and illumination variation if the editor deems it necessary. revision: no

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained

full rationale

The paper presents a standard two-stage neural rendering pipeline: multimodal pre-training to capture cross-modal correlations, followed by RGB-supervised fine-tuning on novel scenes. No equations, parameter-fitting steps, or self-citations are described that would make any claimed prediction equivalent to its inputs by construction. The transfer of learned correlations to unseen scenes is an empirical claim resting on the pre-training distribution, not a definitional or fitted tautology. The method is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Based solely on the abstract, the central claim rests on the domain assumption that modality correlations are stable across scenes and can be learned from pre-training data.

axioms (1)
  • domain assumption Multimodal correlations learned on pre-training scenes generalize to new scenes without any multimodal input
    This transfer is required for the fine-tuning phase to produce accurate unconventional modality renderings from RGB alone.
invented entities (1)
  • SPoILeR model no independent evidence
    purpose: Implicit representation that encodes cross-modality correlations for novel view synthesis
    New architecture introduced to perform the described pre-training and fine-tuning

pith-pipeline@v0.9.1-grok · 5704 in / 1270 out tokens · 20902 ms · 2026-07-03T15:18:30.231293+00:00 · methodology

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