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arxiv: 2604.11097 · v1 · submitted 2026-04-13 · 💻 cs.CV

Recognition: unknown

CDPR: Cross-modal Diffusion with Polarization for Reliable Monocular Depth Estimation

Rongjia Yu , Tong Jia , Hao Wang , Xiaofang Li , Xiao Yang , Zinuo Zhang , Cuiwei Liu
Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:40 UTC · model grok-4.3

classification 💻 cs.CV
keywords monocular depth estimationpolarization imagingdiffusion modelscross-modal fusionsurface normal predictionreflective surfacescomputer vision
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The pith

A diffusion model fuses RGB images with polarization measurements through a learnable gate to improve depth estimates on reflective and transparent surfaces.

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

The paper proposes a diffusion-based approach to single-image depth estimation that adds physically measured polarization signals to standard RGB input. It encodes both modalities into one latent space and uses a trainable gating unit to blend them so that noisy polarization data is down-weighted while useful cues around reflections and transparency are kept. Experiments on synthetic and real datasets show the fused model beats pure RGB baselines in difficult regions and stays comparable elsewhere. The same architecture extends to surface normal estimation with only small changes. The central idea is that polarization supplies independent physical constraints that RGB alone cannot provide in ambiguous scenes.

Core claim

CDPR encodes RGB and polarization (AoLP/DoLP) images into a shared latent space with a pre-trained VAE, then applies a learnable confidence-aware gating mechanism to produce an integrated representation that is denoised into a depth map; this selectively suppresses noisy polarization signals while retaining informative cues near reflective or transparent surfaces.

What carries the argument

The learnable confidence-aware gating mechanism that adaptively fuses RGB and polarization latent features before the diffusion denoising steps.

If this is right

  • Depth accuracy improves in textureless, transparent, and specular regions compared with RGB-only diffusion models.
  • Performance remains competitive with RGB-only models on ordinary scenes.
  • The same gated latent fusion works for surface normal prediction after minimal retraining.
  • The framework supports other polarization-guided dense prediction tasks without redesign of the core diffusion pipeline.

Where Pith is reading between the lines

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

  • Similar gating could let polarization improve other single-image tasks such as segmentation or optical flow in adverse lighting.
  • If the polarization cues prove robust across cameras, the method could reduce reliance on active sensors like LiDAR for indoor or automotive depth.
  • The latent-space fusion pattern may generalize to other cross-modal pairs where one modality is noisier than the other.

Load-bearing premise

Polarization measurements supply stable physical cues in hard regions that a gating network can selectively use without adding new errors or needing extra calibration.

What would settle it

A controlled test on real scenes with ground-truth depth in highly specular or transparent areas that shows the gated model produces larger errors than an RGB-only diffusion baseline.

Figures

Figures reproduced from arXiv: 2604.11097 by Cuiwei Liu, Hao Wang, Rongjia Yu, Tong Jia, Xiaofang Li, Xiao Yang, Zinuo Zhang.

Figure 1
Figure 1. Figure 1: RGB Methods (e.g. Marigold [19]) suffers from scale ambiguity in low-texture regions, such as the table surface, where the predicted distance to the background wall is signifi￾cantly overestimated. While this error may have limited impact on perceptual quality even standard metrics, it is substantially amplified in downstream tasks like 3D reconstruction, leading to distorted geometry and missing surfaces.… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our training pipeline. The input consists of an RGB image and its corresponding polarization observations, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Network architecture of the proposed Confidence [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison of depth estimation results on HyperPol dataset. Compared to prior methods, our method [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparison of depth estimation results on HAMMER dataset. Compared to the more complex HyperPol [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative comparison of depth estimation results on in-the-wild examples. As highlighted in the red boxes, RGB-only [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative results of our extended experiments on surface normal estimation. The results show that our method remains [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of RGB input, polarization cues, and [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
read the original abstract

Monocular depth estimation is a fundamental yet challenging task in computer vision, especially under complex conditions such as textureless surfaces, transparency, and specular reflections. Recent diffusion-based approaches have significantly advanced performance by reformulating depth prediction as a denoising process in the latent space. However, existing methods rely solely on RGB inputs, which often lack sufficient cues in challenging regions. In this work, we present CDPR - Cross-modal Diffusion with Polarization for Reliable Monocular Depth Estimation - a novel diffusion-based framework that integrates physically grounded polarization priors to enhance estimation robustness. Specifically, we encode both RGB and polarization (AoLP/DoLP) images into a shared latent space via a pre-trained Variational Autoencoder (VAE), and dynamically fuse multi-modal information through a learnable confidence-aware gating mechanism. This fusion module adaptively suppresses noisy signals in polarization inputs while preserving informative cues, particularly around reflective or transparent surfaces, and provides the integrated latent representation for subsequent monocular depth estimation. Beyond depth estimation, we further verify that our framework can be easily generalized to surface normal prediction with minimal modification, showcasing its scalability to general polarization-guided dense prediction tasks. Experiments on both synthetic and real-world datasets validate that CDPR significantly outperforms RGB-only baselines in challenging regions while maintaining competitive performance in standard scenes.

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

2 major / 2 minor

Summary. The paper proposes CDPR, a cross-modal diffusion framework for monocular depth estimation that encodes RGB and polarization (AoLP/DoLP) images into a shared latent space using a pre-trained VAE, fuses them via a learnable confidence-aware gating mechanism to suppress noise while preserving cues in challenging regions (textureless, specular, transparent surfaces), and performs depth prediction as a denoising process. It claims significant outperformance over RGB-only baselines on synthetic and real datasets in difficult areas while remaining competitive elsewhere, with easy generalization to surface normal prediction.

Significance. If the quantitative results and ablations hold, the work offers a concrete way to inject physically grounded polarization priors into latent diffusion pipelines for more reliable dense prediction, which could benefit downstream tasks in robotics and AR/VR where RGB cues fail. The gating fusion and cross-task generalization are potentially reusable ideas.

major comments (2)
  1. [Abstract and §3] Abstract and §3 (method): the central construction encodes AoLP/DoLP via a pre-trained RGB VAE into the same latent space as RGB before gating and diffusion. Because AoLP is periodic (0–π) and DoLP is bounded [0,1] with statistics far from natural images, the resulting latents are likely distorted; the paper must demonstrate (via latent-space visualizations, reconstruction error, or ablation replacing the VAE) that physical polarization priors survive this mapping rather than being irrecoverably lost before the gating stage.
  2. [Abstract and §4] Abstract and §4 (experiments): the claim of 'significant outperformance' in challenging regions is asserted without any reported metrics, error breakdowns, or ablation tables in the provided abstract. The full experiments section must include quantitative comparisons (e.g., AbsRel, RMSE on textureless/specular subsets), ablation of the gating module, and analysis of polarization noise/calibration effects on real data to substantiate the load-bearing robustness claim.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'provides the integrated latent representation for subsequent monocular depth estimation' is redundant with the preceding sentence and could be tightened.
  2. [§3] Notation: AoLP/DoLP are introduced without explicit definition of their ranges or preprocessing steps before VAE encoding; a short paragraph or figure in §3 would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their insightful comments on our manuscript. We address each major comment below and outline the revisions we will make to strengthen the paper.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (method): the central construction encodes AoLP/DoLP via a pre-trained RGB VAE into the same latent space as RGB before gating and diffusion. Because AoLP is periodic (0–π) and DoLP is bounded [0,1] with statistics far from natural images, the resulting latents are likely distorted; the paper must demonstrate (via latent-space visualizations, reconstruction error, or ablation replacing the VAE) that physical polarization priors survive this mapping rather than being irrecoverably lost before the gating stage.

    Authors: We recognize the referee's concern about potential distortion in the latent space due to the differing statistical properties of polarization images compared to natural RGB images. Our approach relies on the pre-trained VAE to project both modalities into a shared space, allowing the gating mechanism to integrate them effectively. To validate that polarization priors are not lost, we will incorporate latent-space visualizations (e.g., t-SNE or PCA projections of latents from RGB and polarization), report reconstruction errors for DoLP/AoLP inputs, and conduct an ablation where we replace the shared VAE with a polarization-specific encoder. This will demonstrate the survival of physical cues and justify our design choice. revision: yes

  2. Referee: [Abstract and §4] Abstract and §4 (experiments): the claim of 'significant outperformance' in challenging regions is asserted without any reported metrics, error breakdowns, or ablation tables in the provided abstract. The full experiments section must include quantitative comparisons (e.g., AbsRel, RMSE on textureless/specular subsets), ablation of the gating module, and analysis of polarization noise/calibration effects on real data to substantiate the load-bearing robustness claim.

    Authors: The abstract provides a high-level summary, while the detailed quantitative results are presented in Section 4 of the manuscript. To more thoroughly substantiate the claims of outperformance in challenging regions, we will revise the experiments section to include: (1) quantitative metrics (AbsRel, RMSE, etc.) broken down by scene types such as textureless, specular, and transparent surfaces; (2) a comprehensive ablation study isolating the contribution of the confidence-aware gating module; and (3) an analysis of the impact of polarization noise and calibration variations on real-world data performance. These additions will provide stronger evidence for the robustness benefits. revision: yes

Circularity Check

0 steps flagged

No circularity: framework uses external pre-trained VAE and independent dataset validation

full rationale

The abstract and described method encode RGB and polarization inputs via a pre-trained VAE (external to the current work), apply a learnable gating module, and feed the result into a diffusion denoiser for depth prediction. No equation or step reduces by construction to a fitted parameter renamed as prediction, nor does any load-bearing claim rest on a self-citation chain that itself assumes the target result. Validation occurs on separate synthetic and real-world datasets, keeping the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the domain assumption that polarization provides useful priors for depth in specific failure cases of RGB, plus the technical assumption that VAE encoding and gating can fuse modalities effectively.

axioms (2)
  • domain assumption Polarization images supply physically grounded additional cues for depth estimation around reflective and transparent surfaces.
    Invoked as the motivation for the cross-modal approach in the abstract.
  • domain assumption A pre-trained VAE can encode both RGB and polarization into a shared latent space suitable for diffusion-based prediction.
    Stated as the encoding step without further justification in the abstract.

pith-pipeline@v0.9.0 · 5543 in / 1307 out tokens · 26699 ms · 2026-05-10T15:40:42.493233+00:00 · methodology

discussion (0)

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