arxiv: 2605.01171 · v1 · submitted 2026-05-02 · 💻 cs.CV · cs.LG

Recognition: unknown

CADFit: Precise Mesh-to-CAD Program Generation with Hybrid Optimization

Ghadi Nehme , Eamon Whalen , Faez Ahmed

Authors on Pith no claims yet

Pith reviewed 2026-05-09 14:08 UTC · model grok-4.3

classification 💻 cs.CV cs.LG

keywords CAD reconstructionmesh-to-CADparametric modelinghybrid optimizationprogram generationreverse engineeringvolumetric IoUgeometric feedback

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The pith

CADFit reconstructs complex editable CAD programs from meshes using incremental IoU-driven optimization over parametric operations.

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

Recovering editable parametric CAD programs from mesh geometry remains difficult because most existing techniques output non-editable formats or fail on shapes beyond simple extrusions. CADFit treats the task as an optimization problem that builds the program step by step, choosing and refining operations such as extrusions, revolutions, fillets, and chamfers to maximize volumetric overlap with the input mesh. Experiments across CAD benchmarks show gains in overlap metrics and large drops in the rate of invalid programs, with the biggest improvements appearing on intricate designs. This matters because valid editable CAD sequences support direct modification, manufacturing workflows, and the creation of richer training data for future automated reverse-engineering tools.

Core claim

The paper claims that formulating reconstruction as incremental IoU-driven optimization over structured CAD programs, while supporting a rich set of parametric operations and using geometric feedback for validation at each step, recovers complex, valid, and editable construction sequences that achieve higher volumetric Intersection-over-Union and Chamfer Distance while reducing the Invalid Ratio compared with prior mesh-to-CAD methods.

What carries the argument

Incremental IoU-driven optimization that fits and validates a sequence of parametric CAD operations, including extrusions, revolutions, fillets, and chamfers, by repeatedly measuring geometric agreement between the current program and the input mesh.

Load-bearing premise

That step-by-step optimization guided by volume-overlap scores will reliably reach valid high-fidelity CAD programs for complex designs without becoming trapped in poor local solutions or needing substantial manual fixes.

What would settle it

A direct comparison on a set of complex CAD benchmark models in which CADFit produces lower volumetric overlap scores or a higher fraction of invalid programs than existing state-of-the-art mesh-to-CAD techniques.

Figures

Figures reproduced from arXiv: 2605.01171 by Eamon Whalen, Faez Ahmed, Ghadi Nehme.

**Figure 1.** Figure 1: Mesh-to-CAD construction sequences generated by CADFit with varying complexity, including extrusions, revolutions, fillets, and chamfers. with program execution. CADFit first extracts candidate sketch profiles from the input mesh, then generates a small set of geometrically consistent operation candidates by analyzing how parametric sweeps affect surface alignment. These candidates are assembled into a co… view at source ↗

**Figure 2.** Figure 2: Executable CadQuery program reconstructed by CADFit, illustrating sketch-based primitives, extrusion and revolution operations, Boolean cut and union, and subsequent fillet and chamfer features. length), and Rt specifies the geometric references required by the operation. Depending on τt, Rt may refer to a sketch profile, a set of edges, or one or more existing solids. We implement CADFit using CadQuery (c… view at source ↗

**Figure 3.** Figure 3: Overview of the CADFit Pipeline for Iterative Mesh-to-CAD Reconstruction Algorithm 2 IoU-Guided Construction Sequence Search Input: sketch profiles G, mesh M Output: CAD program Π, solid S Generate candidates C {Appendix K} Π ← C S ← Solid(Π) repeat o ⋆ ← arg maxo∈Π h IoU(S − o ,M) − IoU(S,M) i where S − o := Solid(Π \ {o}) if IoU(S − o ⋆ ,M) ≥ IoU(S,M) then Π ← Π \ {o ⋆} S ← S − o ⋆ else break end if unti… view at source ↗

**Figure 4.** Figure 4: Qualitative mesh-to-CAD reconstruction results on DeepCAD, Fusion360, and ABC (easy, medium and hard subsets) view at source ↗

**Figure 5.** Figure 5: Qualitative image-to-CAD reconstruction results comparing CADFit with prior methods. exhibit limited generalization beyond their training distributions. While these methods achieve reasonable performance on datasets similar to their training data, their reconstruction quality degrades sharply on more diverse benchmarks such as Fusion360 and on higher-complexity ABC subsets. This behavior is particularly… view at source ↗

**Figure 6.** Figure 6: Qualitative Image-to-CAD results on the ABC Hard subset, showing the input image, intermediate Hunyuan3D mesh, and final CADFit reconstruction. 12 view at source ↗

**Figure 7.** Figure 7: Examples comparing CADFIT outputs with DeepCAD ground-truth construction sequences. While DeepCAD programs may require multiple operations to form the target shape, CADFIT frequently reconstructs the same geometry using fewer operations, yielding more concise CAD programs view at source ↗

**Figure 10.** Figure 10: Failure cases of CADFIT caused by unsupported operations and curved geometries view at source ↗

read the original abstract

Despite recent progress, recovering parametric CAD construction sequences from geometric input, such as meshes or point clouds, is a key challenge for design and manufacturing, as existing CAD reconstruction and generation methods are largely restricted to difficult-to-edit formats like meshes or Breps or editable simple sketch-and-extrude pipelines and low-complexity datasets. We introduce CADFit, a hybrid optimization-based CAD reconstruction framework that recovers complex, editable CAD construction sequences from meshes by incrementally fitting and validating parametric operations using geometric feedback. Our approach is distinguished by formulating reconstruction as an IoU-driven optimization over structured CAD programs and supporting a rich set of operations, including extrusions, revolutions, fillets, and chamfers. Experiments on multiple CAD benchmarks show that CADFit outperforms state-of-the-art mesh-to-CAD methods in volumetric Intersection-over-Union and Chamfer Distance, while substantially reducing the Invalid Ratio of reconstructed CAD programs, particularly for complex designs. We further present a multimodal pipeline that enables end-to-end reconstruction of CAD construction sequences from images by combining image-based geometry reconstruction with CADFit. By enabling accurate reconstruction of higher-complexity CAD models, CADFit provides a practical foundation for generating richer datasets and advancing future learning-based approaches to CAD reverse engineering.

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

CADFit's hybrid IoU optimization adds fillets and chamfers to mesh-to-CAD recovery and reports lower invalid rates, but the abstract leaves convergence on complex cases unexamined.

read the letter

The main thing to know is that CADFit treats reconstruction as incremental optimization over a structured CAD program, using volumetric IoU as the driving signal to fit extrusions, revolutions, fillets, and chamfers from a mesh. This is a step past the usual sketch-and-extrude limits and includes a multimodal image-to-CAD path as well. The reported gains in IoU, Chamfer distance, and especially the drop in invalid program ratio on multiple benchmarks are the concrete results they put forward, and the richer operation set does address a practical need in reverse engineering and dataset building. The formulation itself looks like the real novelty here, since it keeps the output as an editable sequence rather than just geometry. The soft spot is the missing detail on how the optimizer actually behaves. The abstract gives no numbers on convergence speed, how often it lands in bad local solutions, or what fraction of complex cases needed manual intervention, so the claim that it works particularly well on hard designs rests on unshown evidence. That matches the stress-test concern about reliability; until the full paper shows failure modes or ablations on the search procedure, those gains are hard to trust at face value. This is for people working on CAD reconstruction pipelines or generating editable training data. A reader who needs a method that produces valid programs rather than just approximate shapes would get direct value from the approach. It deserves a serious referee because the core hybrid idea is grounded and targets a real gap, even if the experiments need more scrutiny on robustness.

Referee Report

3 major / 2 minor

Summary. The paper introduces CADFit, a hybrid optimization framework for reconstructing editable parametric CAD construction sequences from input meshes (or point clouds). It formulates the task as incremental IoU-driven fitting of a rich set of operations including extrusions, revolutions, fillets and chamfers, and reports that the resulting programs outperform prior mesh-to-CAD methods on volumetric IoU and Chamfer Distance while lowering the Invalid Ratio, especially on complex designs. A multimodal extension that first reconstructs geometry from images and then applies CADFit is also described.

Significance. If the performance claims are substantiated by rigorous experiments, CADFit would constitute a meaningful advance in CAD reverse engineering. By producing higher-fidelity, directly editable programs rather than meshes or B-reps, the method could improve downstream design, manufacturing and dataset generation pipelines and provide a stronger foundation for learning-based CAD approaches.

major comments (3)

Experiments section (and abstract): the headline claims of improved volumetric IoU, Chamfer Distance and substantially reduced Invalid Ratio (particularly for complex designs) are stated without any description of the experimental protocol, choice of baselines, number of runs, statistical significance tests, or failure-mode analysis. This absence prevents verification that the reported gains are robust rather than artifacts of particular test cases or hyper-parameter choices.
Method section on the hybrid optimizer: the incremental IoU-driven fitting procedure is presented as reliably converging to valid high-fidelity programs, yet no convergence analysis, escape-from-local-minima strategy, or empirical study of cases that require manual intervention is supplied. The skeptic's concern that the optimizer may routinely terminate in poor local solutions for complex topologies therefore remains unaddressed and directly affects the central performance claims.
Results tables/figures: while the abstract asserts outperformance on “multiple CAD benchmarks,” the manuscript provides no quantitative breakdown by model complexity, no comparison against the strongest recent learning-based or optimization-based baselines, and no ablation isolating the contribution of each parametric operation (fillets, chamfers, etc.).

minor comments (2)

Notation for the IoU objective and the structured program representation should be introduced with explicit mathematical definitions early in the method section to improve readability.
The multimodal image-to-CAD pipeline is mentioned only briefly; a short diagram or pseudocode would clarify the interface between the image-based geometry stage and CADFit.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. The comments highlight important areas where additional rigor and transparency will strengthen the manuscript. We address each major comment below and commit to the corresponding revisions.

read point-by-point responses

Referee: Experiments section (and abstract): the headline claims of improved volumetric IoU, Chamfer Distance and substantially reduced Invalid Ratio (particularly for complex designs) are stated without any description of the experimental protocol, choice of baselines, number of runs, statistical significance tests, or failure-mode analysis. This absence prevents verification that the reported gains are robust rather than artifacts of particular test cases or hyper-parameter choices.

Authors: We agree that the current description of the experimental protocol is insufficient for full verification. In the revised manuscript we will expand the Experiments section with a complete protocol description, explicit justification for baseline selection, the number of independent runs, statistical significance tests, and a dedicated failure-mode analysis. The abstract will be updated for consistency with these additions. revision: yes
Referee: Method section on the hybrid optimizer: the incremental IoU-driven fitting procedure is presented as reliably converging to valid high-fidelity programs, yet no convergence analysis, escape-from-local-minima strategy, or empirical study of cases that require manual intervention is supplied. The skeptic's concern that the optimizer may routinely terminate in poor local solutions for complex topologies therefore remains unaddressed and directly affects the central performance claims.

Authors: We acknowledge that convergence properties and robustness to local minima were not analyzed in sufficient depth. We will add a dedicated subsection to the Method section that includes convergence analysis, the escape-from-local-minima mechanisms already present in the optimizer (multi-start initialization and perturbation), and empirical statistics on cases that required manual intervention or produced suboptimal programs. This addition will directly support the reliability claims for complex topologies. revision: yes
Referee: Results tables/figures: while the abstract asserts outperformance on “multiple CAD benchmarks,” the manuscript provides no quantitative breakdown by model complexity, no comparison against the strongest recent learning-based or optimization-based baselines, and no ablation isolating the contribution of each parametric operation (fillets, chamfers, etc.).

Authors: We agree that the results would benefit from greater granularity and completeness. In the revision we will add quantitative breakdowns by model complexity, ensure the baseline comparisons include the strongest recent methods, and introduce an ablation study that isolates the contribution of each operation type (extrusions, revolutions, fillets, chamfers). These will appear as new tables and figures in the Results section. revision: yes

Circularity Check

0 steps flagged

No circularity: CADFit presents an algorithmic optimization framework validated by external benchmarks

full rationale

The paper introduces a hybrid IoU-driven optimization procedure for recovering parametric CAD programs from meshes, relying on incremental fitting of operations such as extrusions and fillets. No mathematical derivations, predictions, or first-principles results are presented that reduce by construction to fitted inputs or self-referential definitions. Performance claims rest on comparisons against state-of-the-art methods using independent CAD benchmarks, with no load-bearing self-citations or ansatzes that collapse the central method to its own outputs. The approach is self-contained as an engineering pipeline rather than a closed deductive chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

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

pith-pipeline@v0.9.0 · 5516 in / 1074 out tokens · 32268 ms · 2026-05-09T14:08:25.782887+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

CADBench: A Multimodal Benchmark for AI-Assisted CAD Program Generation
cs.CV 2026-05 conditional novelty 6.0

CADBench is a multimodal benchmark for CAD program generation that shows specialized mesh-to-CAD models outperform general vision-language models but degrade with complexity and modality shifts.

Reference graph

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