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arxiv: 2606.07288 · v1 · pith:Q4SXDDUOnew · submitted 2026-06-05 · 💻 cs.CV · cs.GR

ExMesh: EXplicit Mesh Reconstruction with Topology Adaptation

Chuanjin Fan , Lifan Wu , Wenjie Chang , Hanzhi Chang , Wenfei Yang , Tianzhu Zhang This is my paper

Pith reviewed 2026-06-27 22:28 UTC · model grok-4.3

classification 💻 cs.CV cs.GR
keywords explicit mesh reconstructiontopology adaptationdifferentiable optimizationmulti-view reconstructionUV maintenancevertex splittingcoarse-to-fine refinement
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The pith

ExMesh optimizes explicit meshes directly from multi-view images by folding discrete topology updates into the differentiable pipeline.

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

The paper establishes that explicit surface meshes can be refined from coarse to fine by interleaving continuous gradient-based optimization with discrete vertex split and merge operations while keeping UV coordinates consistent. This matters because it removes the need for post-processing steps such as Marching Cubes that commonly produce artifacts and disconnected geometry. A sympathetic reader sees the possibility of obtaining concise, textured meshes whose topology adapts automatically during training rather than after the fact.

Core claim

ExMesh is the first framework to seamlessly integrate discrete topology operations into a continuous differentiable optimization pipeline for direct explicit mesh reconstruction from multi-view images, using an adaptive vertex splitting and merging strategy along with real-time UV maintenance to enable coarse-to-fine optimization while preserving geometric integrity.

What carries the argument

Adaptive vertex splitting and merging strategy with real-time UV maintenance that inserts discrete topology changes inside the continuous differentiable optimization loop.

If this is right

  • Mesh topology adapts during optimization to capture fine details without post-processing.
  • UV coordinates stay consistent for texturing even as vertex connectivity changes.
  • Reconstruction reaches a balance of accuracy, speed, and mesh conciseness.
  • Artifacts and fragmentation from intermediate representations are avoided.

Where Pith is reading between the lines

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

  • The same split-merge-plus-UV mechanism could be inserted into other explicit differentiable renderers for end-to-end scene optimization.
  • If the per-iteration cost of topology updates stays low, the approach may support incremental reconstruction from streaming images.
  • Similar discrete topology primitives might transfer to non-surface explicit representations such as point clouds or voxel grids.

Load-bearing premise

An adaptive vertex splitting and merging strategy combined with real-time UV maintenance can refine mesh topology to capture detail without introducing degenerate faces or inconsistent texturing during coarse-to-fine optimization.

What would settle it

Observe whether repeated topology updates during optimization on a test scene produce any degenerate triangles or UV coordinate discontinuities that break texturing.

Figures

Figures reproduced from arXiv: 2606.07288 by Chuanjin Fan, Hanzhi Chang, Lifan Wu, Tianzhu Zhang, Wenfei Yang, Wenjie Chang.

Figure 1
Figure 1. Figure 1: The coarse-to-fine optimization process of our ExMesh framework. Driven by adaptive vertex splitting and merging, together [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our ExMesh framework. We utilize a differentiable renderer to compute a photometric loss between the rendered [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Visualization of our vertex merge operation, which elim [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative geometric comparison on the DTU dataset. Our method produces structurally clean and high-fidelity meshes. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative comparison on the NeRF-synthetic dataset. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative rendering comparison on the NeRF-synthetic [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 10
Figure 10. Figure 10: Ablation study on UV reconstruction. 0.5x 0.75x 1.0x 1.5x 2.0x 0.58 0.60 0.62 0.64 0.66 0.68 C h a m fe r Dis t a n c e Baseline (0.56) n (Depth Loss) m (Silhouette Loss) s (Laplacian Loss) b (Offset Loss) Degeneracy Threshold [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗
Figure 12
Figure 12. Figure 12: Visualization of Reconstructed Meshes with Vertex Color on DTU Dataset. [PITH_FULL_IMAGE:figures/full_fig_p013_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Ablation study on the Laplacian smoothing loss ( [PITH_FULL_IMAGE:figures/full_fig_p014_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Analysis of the relationship between geometric accu [PITH_FULL_IMAGE:figures/full_fig_p014_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Qualitative geometric comparison on DTU dataset (part 1). [PITH_FULL_IMAGE:figures/full_fig_p015_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Qualitative geometric comparison on DTU dataset (part 2). [PITH_FULL_IMAGE:figures/full_fig_p016_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Qualitative geometric comparison on DTU dataset (part 3). [PITH_FULL_IMAGE:figures/full_fig_p017_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Qualitative geometric comparison on DTU dataset (part 4). [PITH_FULL_IMAGE:figures/full_fig_p018_18.png] view at source ↗
read the original abstract

Reconstructing surface meshes from multi-view images has remained a core challenge in recent years. Most existing methods, whether implicit or explicit, depend on intermediate representations and post-processing steps like Marching Cubes or TSDF fusion, often resulting in artifacts and fragmented geometry. Directly optimizing explicit meshes is a promising approach. However, it presents two critical challenges. The first is how to adaptively refine mesh topology to capture detail without introducing degenerate faces. The second is how to maintain consistent UV coordinates for high-fidelity texturing as the mesh structure evolves. To overcome these, we propose ExMesh, a novel framework that directly optimizes explicit meshes by integrating differentiable optimization with discrete topology updates. Specifically, we introduce an adaptive vertex splitting and merging strategy, along with real-time UV maintenance, to enable coarse-to-fine optimization while preserving geometric integrity. To our knowledge, ExMesh is the first framework to seamlessly integrate discrete topology operations into a continuous differentiable optimization pipeline. Extensive experiments demonstrate that ExMesh achieves a balance among accuracy, computational efficiency, and mesh conciseness.

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 / 0 minor

Summary. The paper proposes ExMesh, a framework for direct explicit mesh reconstruction from multi-view images. It addresses two challenges—adaptive topology refinement without degenerate faces and consistent UV maintenance during mesh evolution—by combining differentiable optimization with discrete topology updates via an adaptive vertex splitting/merging strategy and real-time UV maintenance. The central claim is that ExMesh is the first to seamlessly integrate discrete topology operations into a continuous differentiable pipeline, with experiments showing a balance among accuracy, efficiency, and mesh conciseness.

Significance. If the integration of discrete operations into the differentiable pipeline can be shown to avoid degenerate faces and UV inconsistencies while enabling coarse-to-fine refinement, the approach could advance explicit mesh methods by reducing reliance on post-processing steps such as Marching Cubes. The novelty claim regarding the first seamless integration would be noteworthy if substantiated with technical details.

major comments (2)
  1. [Abstract] Abstract: the manuscript supplies no equations, pseudocode, loss formulations, or implementation details for the adaptive vertex splitting/merging strategy or the real-time UV maintenance mechanism, preventing evaluation of whether the discrete operations are correctly embedded in the differentiable pipeline or whether the claimed avoidance of degenerate faces and UV inconsistency holds.
  2. [Abstract] Abstract: the claim that ExMesh is 'the first framework to seamlessly integrate discrete topology operations into a continuous differentiable optimization pipeline' is presented without reference to prior work on differentiable topology changes or any comparative analysis, making it impossible to assess the novelty or correctness of the integration.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments below regarding the abstract's technical content and the novelty claim. We will revise the manuscript accordingly to improve clarity and substantiation.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the manuscript supplies no equations, pseudocode, loss formulations, or implementation details for the adaptive vertex splitting/merging strategy or the real-time UV maintenance mechanism, preventing evaluation of whether the discrete operations are correctly embedded in the differentiable pipeline or whether the claimed avoidance of degenerate faces and UV inconsistency holds.

    Authors: The abstract is a high-level summary and does not include equations or pseudocode by design. The full manuscript details the adaptive vertex splitting/merging strategy (including the criteria for split/merge decisions and integration with the differentiable renderer) and the real-time UV maintenance mechanism (with explicit handling to prevent inconsistencies during topology changes) in the Methods section, along with loss formulations and implementation specifics. These demonstrate the embedding into the continuous optimization pipeline and avoidance of degeneracies. We will revise the abstract to include a brief reference to these components and their key properties to aid evaluation. revision: yes

  2. Referee: [Abstract] Abstract: the claim that ExMesh is 'the first framework to seamlessly integrate discrete topology operations into a continuous differentiable optimization pipeline' is presented without reference to prior work on differentiable topology changes or any comparative analysis, making it impossible to assess the novelty or correctness of the integration.

    Authors: The claim is qualified as 'to our knowledge' in the abstract. The full manuscript includes a related work section discussing prior approaches to topology adaptation in mesh optimization. We will expand this with specific references to differentiable topology methods and add a comparative analysis in the introduction or experiments to substantiate the integration's novelty and correctness. If needed, we can adjust the wording of the claim based on the expanded discussion. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The abstract and available description present a high-level method overview with no equations, fitted parameters, predictions, or derivation steps. The central claim is a novelty assertion about integrating discrete operations into a differentiable pipeline, supported by experimental results rather than any self-referential mathematical reduction. No load-bearing steps reduce to inputs by construction, self-citation, or ansatz smuggling. The work is self-contained against external benchmarks with no identified circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities beyond the high-level description of the framework itself.

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    Methodology 7.1. Pseudocode for Topology Operations Sections 3.2 and 3.3 introduce our core adaptive vertex splitting and merging strategies. This section provides de- tailed pseudocode for these operations and their correspond- ing UV maintenance routines. Algorithm 1Adaptive Vertex Splitting Input:MeshM(V,F), UV coordinatesU, UV mapU map, Vertex EMA Gra...

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    Hyperparameters Table 6 summarizes the main hyperparameters used in our framework

    Implementation Details 8.1. Hyperparameters Table 6 summarizes the main hyperparameters used in our framework. All values are selected based on validation per- formance and follow common practice in related works. scan24 scan37 scan65 scan69 scan40 scan55 scan83 scan97 scan105 scan106 scan110 scan114 scan118 scan122 scan63 Figure 12. Visualization of Reco...

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    Additional Results 9.1. Additional Results on DTU Dataset Figure 12 shows meshes with vertex color reconstructed by our method on the DTU [20] dataset, demonstrating high- fidelity geometry and realistic surface appearance. In ad- dition, Figures 15–18 present a comprehensive visual com- parison between our approach and several recent baselines, including...

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    Additional Ablations 10.1. Loss Function To further investigate the impact of regularization terms in our framework, we conduct ablation studies on the Lapla- cian smoothing loss (L s) and the bi-vertex offset regular- ization loss (L b) on the DTU dataset. As illustrated in Figure 13, removingL s leads to increased high-frequency noise and jagged artifac...

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    Discussion While our method achieves strong reconstruction quality and efficiency, there remain practical limitations. First, the framework depends on careful tuning of multiple hyperpa- rameters, such as loss weights and topology update thresh- olds, which could be time-consuming and may require ex- pert knowledge. In addition, the efficiency of UV unwra...