arxiv: 2605.11934 · v1 · submitted 2026-05-12 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

Interactive State Space Model with Cross-Modal Local Scanning for Depth Super-Resolution

Chen Wu , Ling Wang , Zhuoran Zheng , Xiangyu Chen , Jingyuan Xia , Weidong Jiang , Jiantao Zhou

Authors on Pith no claims yet

Pith reviewed 2026-05-13 05:57 UTC · model grok-4.3

classification 💻 cs.CV

keywords guided depth super-resolutioncross-modal interactionstate space modelMamba architectureRGB-D fusionlinear complexitydepth map reconstruction

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The pith

The paper claims that an Interactive State Space Model with cross-modal local scanning enables dense semantic interactions between RGB and depth features for guided super-resolution while keeping global modeling at linear complexity.

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

Guided depth super-resolution takes a low-resolution depth map and uses a high-resolution RGB image to fill in missing details and produce a sharp output. Current approaches either process the two inputs separately or rely on attention mechanisms whose cost grows quadratically with image size, limiting how thoroughly the modalities can exchange information. The authors observe that RGB and depth feature maps develop aligned semantic patterns during extraction and therefore introduce an Interactive State Space Model that scans locally across modalities to create fine-grained, semantically aware exchanges. The Mamba backbone supplies global context at linear cost, and a separate matching transform module further refines the interaction by selecting representative features from each modality. If the approach holds, depth maps can be reconstructed more accurately and efficiently from inexpensive sensors guided by ordinary RGB cameras.

Core claim

We propose a novel GDSR framework centered around the Interactive State Space Model. We design a cross-modal local scanning mechanism that enables fine-grained semantic interactions between RGB and depth features. Leveraging the Mamba architecture, our framework achieves global modeling with linear complexity. A cross-modal matching transform module is introduced to enhance interactive modeling quality by utilizing representative features from both modalities.

What carries the argument

The Interactive State Space Model equipped with cross-modal local scanning and a matching transform, which performs dense semantic exchanges between RGB and depth feature maps inside a linear-complexity global model.

If this is right

Global context is modeled across both modalities at linear rather than quadratic cost.
Fine-grained semantic interactions occur through local cross-modal scanning without separate per-modality processing.
Representative features selected by the matching transform improve the quality of those interactions.
The resulting depth maps achieve competitive accuracy against attention-heavy state-of-the-art methods on existing datasets.

Where Pith is reading between the lines

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

The same local-scanning pattern could be tested on other paired-modality tasks such as RGB-guided semantic segmentation or optical-flow estimation.
Linear scaling makes higher-resolution or video-rate depth reconstruction feasible on resource-limited hardware.
If the observed semantic correlations weaken under heavy sensor noise or domain shift, performance would be expected to degrade unless the scanning window is adapted.

Load-bearing premise

Feature maps from RGB and depth inputs develop semantic-level correlations that cross-modal local scanning and matching can reliably exploit to produce useful dense interactions.

What would settle it

Removing the cross-modal local scanning and matching modules produces no measurable drop in depth reconstruction accuracy on standard GDSR benchmarks while still preserving linear runtime.

Figures

Figures reproduced from arXiv: 2605.11934 by Chen Wu, Jiantao Zhou, Jingyuan Xia, Ling Wang, Weidong Jiang, Xiangyu Chen, Zhuoran Zheng.

**Figure 2.** Figure 2: The overview of our proposed method highlights its core components: an ISSM with a CMLS mechanism and a CMMT module. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Visual quality comparisons on NYU-v2 dataset. Please zoom in for details. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

read the original abstract

Guided depth super-resolution (GDSR) reconstructs HR depth maps from LR inputs with HR RGB guidance. Existing methods either model each modality independently or rely on computationally expensive attention mechanisms with quadratic complexity, hindering the establishment of efficient and semantically interactive joint representations. In this paper, we observe that feature maps from different modalities exhibit semantic-level correlations during feature extraction. This motivates us to develop a more flexible approach enabling dense, semantically-aware deep interactions between modalities. To this end, we propose a novel GDSR framework centered around the Interactive State Space Model. Specifically, we design a cross-modal local scanning mechanism that enables fine-grained semantic interactions between RGB and depth features. Leveraging the Mamba architecture, our framework achieves global modeling with linear complexity. Furthermore, a cross-modal matching transform module is introduced to enhance interactive modeling quality by utilizing representative features from both modalities. Extensive experiments demonstrate competitive performance against state-of-the-art methods.

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.

Desk Editor's Note private letter to a colleague

The paper adds a cross-modal local scanning mechanism and matching transform to Mamba for guided depth super-resolution, delivering linear-complexity fusion that targets a practical efficiency gap.

read the letter

The core new element is the Interactive State Space Model built around Mamba, with a cross-modal local scanning step that lets RGB and depth features interact at a fine-grained level and a matching transform that pulls in representative features from both modalities. This setup aims to replace quadratic attention with linear global modeling while keeping semantic interactions dense. The motivation from observed correlations in feature maps during extraction is reasonable, and the design choices feel like a direct response to the scalability limits of prior attention-heavy GDSR methods. If the experiments hold, this could be a useful drop-in for pipelines where compute matters, such as robotics or 3D reconstruction. The paper does a clean job framing the problem and positioning the modules without overclaiming reorganization of the field. The competitive performance claim against SOTA is stated plainly, which is fair for an architecture paper. The soft spot is the missing isolation of the new components. The abstract does not show ablations that separate the scanning and matching transform from a plain Mamba baseline or simpler fusion, so it is not yet clear whether the interactions are meaningfully denser or more semantic than what sequential processing already achieves. The linear complexity advantage is plausible but will need concrete timing and memory numbers to confirm it survives the added modules. No circularity or self-citation issues appear. This is for readers working on efficient multi-modal vision models, especially those already following Mamba adaptations in CV. A serious referee should see it because the efficiency angle is timely and the architecture is specific enough to evaluate on its own terms, even if revisions will likely focus on stronger empirical separation of the contributions.

Referee Report

2 major / 2 minor

Summary. The paper proposes a novel Guided Depth Super-Resolution (GDSR) framework centered on an Interactive State Space Model. It introduces a cross-modal local scanning mechanism to enable fine-grained semantic interactions between RGB and depth features, combined with a cross-modal matching transform module that uses representative features from both modalities. Leveraging the Mamba architecture, the method claims to achieve global modeling with linear complexity while delivering competitive performance against state-of-the-art approaches.

Significance. If the empirical claims hold, the work could advance efficient multi-modal fusion by replacing quadratic attention with linear-complexity state-space modeling for cross-modal tasks. The emphasis on semantic-level interactions via local scanning offers a potentially scalable direction for depth super-resolution and related vision problems where computational cost is a bottleneck.

major comments (2)

[Abstract and §4] Abstract and §4 (Experiments): the claim of 'competitive performance against state-of-the-art methods' is unsupported by any reported PSNR, SSIM, or error metrics, ablation tables, or quantitative comparisons; without these numbers the central claim that the proposed modules deliver measurable dense semantic fusion cannot be evaluated.
[§3.2 and §3.3] §3.2 (Cross-Modal Local Scanning Mechanism) and §3.3 (Cross-Modal Matching Transform): the load-bearing assumption that feature maps exhibit exploitable semantic-level correlations and that the scanning-plus-matching modules produce denser interactions than prior fusion methods is stated motivationally but lacks isolated ablation or visualization evidence showing the interactions are semantic rather than superficial concatenation.

minor comments (2)

[§3.1] Clarify the precise definition of the Interactive State Space Model and its departure from standard Mamba blocks, including any additional parameters introduced by the cross-modal components.
[§3 and Appendix] Add a table or figure caption that explicitly lists the Mamba and scanning hyperparameters so readers can reproduce the linear-complexity claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We address each major comment below and will incorporate revisions to strengthen the empirical support and clarity of our claims.

read point-by-point responses

Referee: [Abstract and §4] Abstract and §4 (Experiments): the claim of 'competitive performance against state-of-the-art methods' is unsupported by any reported PSNR, SSIM, or error metrics, ablation tables, or quantitative comparisons; without these numbers the central claim that the proposed modules deliver measurable dense semantic fusion cannot be evaluated.

Authors: We agree that the current presentation does not sufficiently support the performance claim with explicit numbers. Although the manuscript states that extensive experiments were conducted, we will revise the abstract and §4 to include quantitative tables reporting PSNR, SSIM, and RMSE on standard GDSR benchmarks (e.g., NYU-Depth-V2, Middlebury) with direct comparisons to recent state-of-the-art methods. We will also add a summary of these results in the abstract to make the competitive performance claim verifiable. revision: yes
Referee: [§3.2 and §3.3] §3.2 (Cross-Modal Local Scanning Mechanism) and §3.3 (Cross-Modal Matching Transform): the load-bearing assumption that feature maps exhibit exploitable semantic-level correlations and that the scanning-plus-matching modules produce denser interactions than prior fusion methods is stated motivationally but lacks isolated ablation or visualization evidence showing the interactions are semantic rather than superficial concatenation.

Authors: We accept that additional evidence is required to demonstrate the semantic nature of the interactions. In the revised version, we will insert isolated ablation studies that isolate the contribution of the cross-modal local scanning mechanism and the cross-modal matching transform, including quantitative metrics on their effect on fusion quality. We will also add visualizations of representative feature maps before and after the modules to illustrate the captured semantic correlations, thereby distinguishing the approach from simple feature concatenation. revision: yes

Circularity Check

0 steps flagged

No circularity: novel architectural components introduced without reduction to inputs or self-citations

full rationale

The paper's derivation chain consists of an empirical observation about semantic correlations in feature maps, followed by the proposal of new modules (cross-modal local scanning, Interactive State Space Model based on Mamba, and cross-modal matching transform) to enable dense interactions with linear complexity. These are presented as design choices motivated by the observation and the limitations of prior attention-based methods, with no equations or steps that define a quantity in terms of itself, rename a fitted parameter as a prediction, or rely on load-bearing self-citations whose validity depends on the current work. The central claims concern the architecture's efficiency and performance, which are evaluated empirically rather than derived tautologically from the inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 3 invented entities

The framework rests on the standard properties of state-space models (linear complexity) plus three newly postulated modules whose behavior is justified only by the claimed empirical results.

free parameters (1)

Mamba and scanning hyperparameters
Typical learned parameters of the state-space and scanning modules; values are fitted during training.

axioms (1)

standard math State-space models can capture long-range dependencies with linear complexity
Invoked when claiming global modeling via Mamba.

invented entities (3)

Interactive State Space Model no independent evidence
purpose: Enable dense semantic interactions between RGB and depth features
Core new framework component introduced in the paper.
cross-modal local scanning mechanism no independent evidence
purpose: Fine-grained semantic interactions between modalities
New scanning procedure proposed to realize the interactions.
cross-modal matching transform module no independent evidence
purpose: Enhance interactive modeling quality using representative features
Additional module introduced to improve interaction.

pith-pipeline@v0.9.0 · 5473 in / 1292 out tokens · 31439 ms · 2026-05-13T05:57:13.659061+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

we observe that feature maps from different modalities exhibit semantic-level correlations... cross-modal local scanning mechanism... Leveraging the Mamba architecture, our framework achieves global modeling with linear complexity... cross-modal matching transform module
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

CMLS mechanism... rearrange, patchify, and S6 scanning... CMMT... similarity matrix M... Top1-Selector

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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