CReFT-CAD: Boosting Orthographic Projection Reasoning for CAD via Reinforcement Fine-Tuning
Authors: Ke Niu
zhuofan chen
Haiyang Yu
Yuwen Chen
Teng Fu
Mengyang Zhao
Bin Li
Xiangyang Xue
Venue: NeurIPS 2025 poster ()
Keywords: CAD Reasoning
Multi-Modal Dataset
Constraint-Driven Synthesis
Primary area: applications

Abstract: Computer-Aided Design (CAD) is pivotal in industrial manufacturing, with orthographic projection reasoning foundational to its entire workflow—encompassing design, manufacturing, and simulation. However, prevailing deep-learning approaches employ standard 3D reconstruction pipelines as an alternative, which often introduce imprecise dimensions and limit the parametric editability required for CAD workflows. Recently, some researchers adopt vision–language models (VLMs), particularly supervised fine-tuning (SFT), to tackle CAD-related challenges. SFT shows promise but often devolves into pattern memorization, resulting in poor out-of-distribution (OOD) performance on complex reasoning tasks. To tackle these limitations, we introduce CReFT-CAD, a two-stage fine-tuning paradigm: first, a curriculum-driven reinforcement learning stage with difficulty-aware rewards to steadily build reasoning abilities; second, supervised post-tuning to refine instruction following and semantic extraction. Complementing this, we release TriView2CAD, the first large-scale, open-source benchmark for orthographic projection reasoning, comprising 200,000 synthetic and 3,000 real-world orthographic projections with precise dimensional annotations and six interoperable data modalities. Benchmarking leading VLMs on orthographic projection reasoning, we show that CReFT-CAD significantly improves reasoning accuracy and OOD generalizability in real-world scenarios, providing valuable insights to advance CAD reasoning research. The code and adopted datasets are available at \url{https://github.com/KeNiu042/CReFT-CAD}.

CReFT-CAD: Boosting Orthographic Projection Reasoning for CAD via Reinforcement Fine-Tuning
Ke Niu", Zhuofan Chen", Haiyang Yu Yuwen Chen, Teng Fu, Mengyang Zhao, Bin Li Xiangyang X
College of Computer Science and Artificial Intelligence, Fudan University, China (kniu22, zf chen23, tfu23) @m. fudan. edu. cn
myyyu20, libin, [ibin,xyxue) xyxue) @fudan. edu.
Abstract
Computer-Aided Design (CAD) is pivotal in industrial manufacturing, with ortho- graphic projection reasoning foundational to its entire encomoow-nnnmmpsssing design, manufacturing, and simulation. However, prevailing deep-lcarning ap- proaches employ standard 3D reconstruction pipelines as an alternative, which often introduce imprecise dimensions and limit the parametric editability required for CAD workflows. Recently, some researchers adopt vision-languee mod- els (VLMs), particularly supervised fine-tuning (SFT), tackle CAD-related challenges. SFT shows promise but often devolves into pattern memorization, resulting poor out-of-distribution (OOD) performance on complex reasoning tasks. To tackle these limitations, we introduce CReFT-CAD, a two-stage fine- tuning paradigm: first, a curriculum-dre driien reinforcement learning stage with difficulty-awand rewards to steadily build reasoning abilities; second, supervised post-tuning to refine instruction following and semantic extraction. Complement- ing this, we release TriView2CAD. the first large-scale, open-source benchmark for orthographic projection reasoning, comprising 200,000 synthetic and 3,000 real-worke orthographic projections with precise dimensional annotations and six interoperable data modalities. Benchmarking leading VLMs on orthographic pro- jection reasoning, show that CReFT-CAD significantly improves reasoning accuracy and OOD generalizability in real-world scenarios, providing valuable insights advance CAD reasoning research. The code and adopted datasets available at Ittps:://// http://ttt....///aaaoo//////////
Introduction
Computer-Aided Design (CAD) is now integral to industrial product development, underpinning design, manufacturing, and simulation workflows. the design phase, engineers use CAD drawings rasterized orthographic projections for their precision and facile editability. During manufac- turing, these drawings are converted into constraint-based parameter tables; for simulation, they yield coundary-representtan
(B-Rep) data textual geometry descriptions. A truly user-centric pipeline thus requires accurate semantic parsing of orthographic projections: automated extraction of parameter tables enables direct generation of 3D models meeting stringent manufacturing and simulation standards. By contrast, cxisting reverse-engincering methods rely on expensive 2D/3D scanning hardware and labor-intensive processn,,,,,ng, greatly restricting scalability and widespread industrial adoption.
Equal contribution. Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Figure (a) (b) show the results of parameterization tasks of orthographic projection reasoning using Qwen2.5.Vi VL and Deepseek without tuning. (c)-(f) illustrate CReFT-CAD's capabilities across various orthographic projection reasoning tasks.
With the advent of deep learning, orthographic projection reasoning has typically been framed as a standard 3D reconstruction pipeline: models process rasterized drawings output B-Rep structures 112.. point clouds [3] [.. or meshes [5]. However, this paradigm suffers from two critical limitations. First, industrial drawings demand near-zero tolerance for missing or erroneous components: pixel- level discretization errors in reconstruction can propagate to 3D models, leading to failures in downstream manufacturing and simulation. Second, practical CAD workflows require parametric editability and precise semantic alignment- capabilities that common 3D representations lack. While some methods generate CAD command sequences recover editability, they still overlook the nuanced semantics of orthographic views, thus failing to reliably map 2D drawing features to their intended 3D counterparts.
Recent works leverage the modality alignment and semantic reasoning capabilities of vision-language models (VLMs) [6-12], addressing CAD challenges via supervised fine-tuning (SFT) (13115). However, orthographic projection reasoning requires genuine inferential ability, and task-specific SFT often induces pattern memorization and out-of-distribution (OOD) performance degradation rather than fostering deeper reasoning [16]. Inspired by DeepSeek RI-Zero's Group Relative Policy Optimization (GRPO) (17), we propose Curriculum-driven Reinforcement Fine-tuning for CAD (CReFT-CAD).
CReFT-CAD is versatile framework enabling interactive orthographic projection reasoning across the full lifecycle of industrial design- design-from design and manufacturing to simulation (see Fig. [T. consists of two-stage paradigm integrating reinforcement learning and supervised tuning: the first stage is Curriculum-ddrvee reward mechanism that incrementally increases task complexity, gradually exposing the model to increasingly complex reasoning challenges to promote stable policy optimization; the second stage is Supervised Post-tuning, where we build on the reinforced model and further refine its instruction- following and reasoning capabilities via multi-task SFT. Our qualitative and quantitative evaluations show that CReFT-CAD achieves robust orthographic projection reasoning and strong generalization in OOD scenarios. Another key barrier in orthographic projection reasoning is the scarcity of open- source datasets with high-fidelity annotations. To address this, we present Tri VieWeDDC the first large-scale benchmark tailored to industrial CAD pipelines. Leveraging real-world design archives, we use constraint-driver synthesis pipeline to generate 200,000 synthetic and 3,000 real-world three- view sets, each annotated with precise, one-to-one dimensional labels linked to their corresponding geometric primitives. This design allows models to extract quantitative measurements from rasterized drawings and enforce exact geometric constraints for 3D reconstructions ready for simulation.
Reinforcement Fine-tuning, where we introduce a difficulty-wwan

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2 Related Work
2.1 Orthographic projection reasoning for CAD
Understanding and reasoning with orthographic projection is a fundamental and longstanding chal- lenge CAD-rellted tasks (0) With the development of 3D reconstruction methods, existing methods for orthographic projection reasoning have largely centered on standard 3D reconstruc- tion pipelines. SPARE3D [2]] introduces a dataset for evaluating the spatial reasoning capabilities of AI systems via 2D line drawings of 3D objects. Contrastive-SPARES
22 proposes self- supervised binary classification network that helps learn 3D object line drawing representations that detail-sensitive and view-invariant. PlankAssembly [23] presents transformer-baseee sequence generation model that learns flexible input-output mappings. IsoTGAN [24] introduces a novel Euclidean attention mechanism and a geometric constraint loss function to further enhance local image features. GaussianCAD [25] employs custom sparse-view 3D reconstruction method, removing reliance vector CAD sketches and real-world 3D data.
2.2 Applications of LVLMs in CAD-Related tasks
Recent efforts applying vision-language models (VLMSs) to CAD tasks fall into three main cate- gories:1) Direct Adaptation of Pretrained VLMs. CAD-Recode (26] leverages a VLM translate
raw point cloud inputs into executable Python code. Both LEAM 27 and LLM4CAD 28 generate CAD models by jointly leveraging text descriptions and image inputs. CAD-Assistant
29 intro-
duces novel approach using VLMs as planners, where the model interacts with Python APIs to accomplish diverse CAD tasks. 2) Supervised Fine-Tuning CAD2P.caa (13) generates 3D parametric models from 2D CAD drawings. CAD-MLLM (30] enables parametric CAD modeling from text, images, and point clouds, and introduces the Omni-CAD dataset. 3) Reinforcement Learning- based RLCAD [1 introduces a reinforcement learning environment for gener- ating complex CAD models. CADCrafter (32) presents an image-to-CAn to-CAD generation framework, fine-tuned via Direct Preference Optimization (DPO) to directly translate input images into executable CAD representations.
TriView2CAD: Benchmarking orthographic projection reasoning
As previously noted, critical barrier in CAD research is the absence of open-source dataset with high-fidelity annotations for orthographic projection reasoning. Existing benchmarks focus on 3D reconstruction from rasterized 2D views, yet diverge from real-world engineering drawing interpretation in three key aspects:
Lack of precise dimensional annotations. Existing orthographic projection benchmarks provide only rasterized drawings without precise dimension annotations. This causes models to prioritize relative scale over exact mcasurements, and pixel-level errors in rasterized images can easily propagate into the reconstructed 3D model, resulting in inaccuracies. Additionally, the limited editability of reconstructed 3D models prevents corrective adjustments during design and simulation.
Lack of explicit logical reasoning tasks. Existing datasets do not evaluate a model's inferential capabilities within or across orthographic views. Consequently, models trained on these benchmarks learn pixel-level reconstruction skills but fail to handle real-world challenges such as inferring omitted annotations leveraging structural symmetries.
Lack of essential CAD modalities. Most datasets contain only one or two modalities-typiieee raster images and, at best, single 3D format (meshes point clouds). This limited coverage omits critical data representations (e-g.. parameter tables, vector CAD files, executable commands, STEP/B-R Rep) required for end-to-end CAD design, manufacturing, and simulation pipelines.
3.1 Dataset construction
In this work, we focus on prefabricated bridge piers owing to their inherently modular struc- corp-co of repeated, standardized components that enables compact yet expressive parameter space for dataset synthesis. Moreover, prefabricated piers are ubiquitous in modern infrastructure and backed by extensive CAD drawing and manufacturing documentation archives.
3

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Expert Knowledge
Layers separating
Rendering
roon
Side
OXF
Drowing
Constriints
Text
Step 2D Drawing Image Generation
Orthographle Projection
Inter-Ilntrs-iew
ezdxf
Constraints
Step 1: Constroint-
Parameter Sompling
Eutliies separating
Execute
Parameter Syace
Step 3: 3b Model Generation
3-D Model
Figure 2: Constraint-driven synthesis pipeline for TriView2CAD.
TriView2CAD comprises 200,000 synthetic samples and 3,000 real-world samples. The synthetic data are split 80/20 into training and test sets; due to the scarcity of real-world engineering drawings, all 3,000 real-world samples are reserved exclusively for testing. The synthetic set evaluates in-domain performance. As shown in Fig. 3 real-world images typically feature more complex structures primmrrly due redundant annotation lines (causing occlusions and overlaps with other components) and the inclusion of non-numert annotations. These samples thus assess out-of- distribution (OOD) generalization in authentic CAD scenarios. To align with end-to-end industrial workflows, TriVie2CCD provides six interconnected data modalities per sample: structured param- eter tables (JSON), vector CAD drawings (DXF), raster images (PNG), executable modeling scripts, and two standard 3D formats (STEP and B-Rep). This comprehensive modality suite supports tasks spanning early design to downstream manufacturing and simulation. As shown Fig. 5 constraint-driven synthesis pipeline, with detailed steps outlined below:
we adopt
Step Constraint-Guided Parameter Sampling We first analyze real-world CAD drawings to define 15- dimensional parameter space, governed by two constraint classes. Intra- View Constraints enforce topological clo- sure and physical validity within each projection: every component must form gap- and overlap- fereep--rre contours, and paired dimensions adhere to domain-specific engineering constraints (e.g.. "Cross-Bridge Pier Spacing" "Cap Beam -ooss- Dimension"). Inter-View Constraints en- sure projection consistency by requiring measurements annotated in one view to exactly match their counterparts (height, width, depth) in other views. By embedding these constraints into the sampling pipeline, we ensure all syn- thetic designs geometrically coherent and compliant with real-world engineering standards.
A7
211
62
114
1i 12
Step 2: 2D Drawing and Raster Image Generation We use the ezdxf library to convert each sampled parameter vector into vectorized 2D CAD drawing (DXF format), mapping numeric dimensions to geometric primitives (c.g.. lines, circles, arcs) form coherent orthographic projections. We utilize ezdxf's layer separation functionality to partition primitives into semantic layers, enhancing the editability of the resulting CAD drawings. Each DXF file is then imported into FreeCAD [33), where high-resolution screenshots captured to generate three orthographic views (front, top, side).
Model
Figure 3: Examples of Real-Worlh Orthographic Projections and its 3D model.
Step 3D Model Generation We utilize the FreeCAD Python API reconstruct 3D model from its corresponding parameter vector. Consistent with our 2D generation pipeline, decompose the

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Training Tasks
Toput Prompt
Response
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Parameterizatio task based on CoT
222
Figure 4: Diagram design of three tasks in curriculum-driver reinforcement fine-tuning.
model into discrete entities and generate scripted commands to precisely position each primitive in compliance with the intra- and inter-view constraints defined in Step 1. Executing these scripts automatically produces industry-standard STEP and B-Rep files, closing the loop from parameter sampling editable, simulation-ready CAD geometries.
3.2 Evaluation of TriView2CAD
We benchmark seven leading vision-language models on TriVieW2CAD to assess their high-precision orthographic projection reasoning capabilities. The evaluation results are presented in Sec. 3.2 Aligning with real-world CAD workflows, we design three complementary evaluation tasks to rigorously a model's ability to extract precise dimension annotations from rasterized orthographic projections and infer the underlying geometric relationships: (i) Dimension recognition and pairing Models identify each annotated dimension in single orthographic view and map its associated geometric feature. (ii) Primitive counting Models count instances of specified CAD elements (e.g., number of pier columns), assessing their ability to parse structural composition. (iii) Composite Parameter Computation Models compute engineering-crin critical derived quantities for example, Cross-Bridge Pier Spacing calculated as the sum of Pier Column Cross- Cross-Bridge Dimension and Pile Spacing, Collectively, we define 15-dimensional parameter space consisting of 6 recognition parameters, counting parameters, and composite calculation parameters. evaluation, each parameter is treated as an independent prediction target. Overall accuracy is computed as the total number of correctly predicted parameters divided by the total number parameters across all test samples. This evaluation suite reveals the strengths and weaknesses of each model in addressing the tightly coupled semantic and quantitative requirements of orthographic projection CAD reasoning.
Methodology
Inspired by the success of reinforcement learning-baste tuning methods [34, 35 we present Curriculum-driven Reinforcement Fine-tuning for CAD (CReFT-CAD), aethographic-projecteo reasoning framework. CReFT-CAD integrates a ViT-based visual encoder with Qwen2.5 [36]. First, the framework adopts curriculum of three progressively challenging reasoning tasks paired with a difficulty-awant reward mechanism, empowering the model to transcend rote pattern matching and develop robust reasoning capabilities while enhancing out-of-distribution (OOD) generalization. Second, Supervised Post-tuning refines the model's instruction-following abilities to accommodate interactive, real-world CAD queries.
4.1 Curriculum-driven reinforcement fine-tuning
Orthographic projection reasoning is sophisticated reasoning task that involves not only dimen- sion recognition and pairing but also primitive counting and more complex composite parameter computation. Building upon GRPO, we propose (CReFT) strategy, which incrementally exposes the model increasingly complex training tasks to enhance its reasoning and generalization capabilities. Our task design aligns with the core steps expert engineers follow when verifying CAD drawings, incorporating dichotomous choice tasks, multiple-choie tasks, and chain-of-thought (CoT)) parameterization tasks. For each task, we design tailored, efficient reward function to ensure the model can steadily optimize its reasoning capabilities.
sariculuumdrrveedd Reinforcement Fine-tuning
5

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Data engine. We utilize 160,000 image-tex text instruction pairs from the synthetic training set of TriView2CAD, where each prompt includes an orthographic projection and all 15 parameter key-value pairs (see Fig. T. ensure balanced training signal across the three tasks, 50% of the training responses are fully correct, while the remaining 50% contain errors of varying degrees. Specifically, for dichotomous choice tasks, each negative response includes n. erroneous parameter values, where n follows a uniform distribution N Uniform(a, b). where a = 1 and b 15.
For multiple-choice tasks, parameter values are masked out in each image-text instruction pair. The masking mechanism designed ensure the diversity of distractor options, preventing the model from merely deriving answers through parameter matching -aaeehnggtherrbby avoiding circumvention of the complex reasoning required for the task. Instead, compels the model actively engage in pocision-making. evaluating multiple potential solutions and selecting the correct one based on deeper geometric understanding. For each instruction pair, unmasked values are correct; for incorrect parameter value lists, all unmasked values are correct except for those in q randomly selected erroneous entries. Both the number of masked values p and the number of erroneous entries q follow normal distributions, introducing smooth, gradual variations in task difficulty that enable the model to learn in a more structured, progressive fashion.
For parameterization tasks, we construct image-text instruction pairs grounded in Chain-of-Thoughe (CoT) reasoning. Specifically, for Composite Parameter Computation tasks, expert knowledge is embedded into step-by-step reasoning prompts to guide the model toward correct outputs. The step-by-step reasoning workflow is follows: 1) identify the parameters required to compute the composite parameter; 2) specify the calculation formula for the composite parameter; 3) compute the predicted result using the formula and relevant factor parameters.
Training strategy. Given that our task involves highly specialized inputs and outputs (as opposed to general-purpose problems), directly applying pretrained VLM to perform reasoning and generate the required outputs is ineffective. Consequently, the reinforcement learning (RL) reward mechanism cannot function properly-win with rewards consistently remaining zero. To mitigate this, we pre-sample subset of image-text instruction pairs to "warm up" the model before initiating each training task, enabling proper initialization of the RL reward mechanism.
4.1.1 Task 1: Dichotomous choice task
For this task, the model's output is restricted to dichotomous responses: "yes" or "no." " A "yes" response is returned if and only if all parameters in the 15-dimensional parameter space are fully accurate, whereas any error-even even in single parameter- parameter-ttiggeers "no" response. This stringent criterion incentivizes the model to generate fully accurate parameterizations. Consistent with this design, the reward function defined as follows:
1,
if all parameters are correct
Rpi
(0:
(I)
otherwise
4.1.2 Task 2: Multiple choice task
Training strategy. Task 2 introduces multiple-choice tasks to further increase decision-making complexity. These tasks require the model to select from multiple parameter value lists, which significantly enhances the cognitive load compared to binary decisions. Building on the first task, we pre-sample a subset of image-text instruction pairs to conduct cestruction-follvviri the LLM. This training enables the model to generate structured outputs in the form of multiple- choice questions, facilitating the activation the reinforcement learning (RL) reward mechanism. Additionally, we design stringent reward function that encourages the model to fully utilize image- text instruction pairs, ensuring it extracts and leverages all relevant information from both visual and textual modalities. The reward function incentivizes the model select the most accurate option from the provided choices. Any incorrect selection yields a reward of 0, compelling the model to prioritize precision and reliability in its decision-making.
training on
Let Scureet be the set of correct parameter value lists, Sineorett be the set of incorrect parameter value lists, and SSelecee be the set of selected parameter value lists by the model. The reward function Ry2

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is defined as:
if Sselected Scarrect
0.2 if selected C correct but selected o Scorrest
Rp2
if (Srelected O Searrett * 0) and (Stelected ( Sincorret #
if Sselected Sincorrect
Where Sselected convet means the model selects only the correct parameter value lists; SSelected 2 Scoreet but Sselected * Scorree means the model selects all correct lists but fails to select all of them; SSelected O Scorrett # 0 and Ssekseted Smoooreet ş 0 means the model selects both correct and incorrect parameter value lists; Ssekettd = Sicorreet means the model selects incorrect parameter value lists.
4.1.3 Task 3: Parameterization tasks based on thain-of-thoughe
Training strategy. For this task's training, start with the model fine-tuned in Task 2 and sample synthetic data to construct standard orthographic projection reasoning QA pairs. These pairs are used for small-batch fine-tuning, After fine-tuning, we assess the difficulty of predicting the 15-dimensional parameters by evaluating the model's performance on a held-out test set. Each parameter's prediction accuracy is categorized into three difficulty levels: Easy Attributes: Attributes with accuracy greater than 0.8. Medium Attributes: Attributes with accuracy between 0.2 and 0.8. Difficult Attributes: Attributes with accuracy less than 0.2. This difficulty classification is subsequently used to design the reward function. The reward for each correctly predicted attribute is determined by its difficulty level follows:
if the model correctly predicts easy attribute 1.5 if the model correctly predicts a medium attribute
Rp3
(3)
2 if the model correctly predicts a hard attribute
0 if the model's prediction is incorrect
This reward function ensures that the model is motivated to gradually improve across all difficulty levels, with greater emphasis on mastering the more challenging tasks, thus enhancing the model's overall reasoning capabilities in orthographic projection tasks.
4.2 Supervised post-tuning
Following the CReFT stage-during which the model has developed robust reasoning and general- ization capabilities-the focus of Supervised Post-tuning shifts to refining the model's 's instruction- following capabilities. We revisit the industrial design application context and, adopting a user-centric approach, reformulate tasks in the paradigm of Visual Question Answering (VQA). Leveraging multimodal language models, we design specific tasks that guide the model output comprehensive parameter lists and make engineering-critics stage, the model trained to handle the following tasks: 1) Full Parameter List Output Task: Generate a complete and accurate set of parameter key-value pairs- the standard output format for the simulation phase. 2) Parameter Validation Task: Compare extracted parameter key-value pairs with the ground-truth parameter set in the orthographic projections to verify the accuracy of the extracted parameters. 3) View Matching Task: Given two distinct orthographic views, determine the views correspond to the same 3D object design based parameter constraints and rasterized images. 4) Component Counting Task: Identify and count specified components (e.g., bridge piers, columns) in the drawings based on given parameter constraints. This task quantifies design elements to support material estimation and component verification.
judgments based on orthographic projections. In this
Through this post-tuning phase, we strengthen the model's capacity to interpret and respond to complex design queries, thereby enhancing interactive reasoning capabilities and increasing its applicability real-world industrial design workflows.
5 Experiments
Implementation Details. All experiments were conducted on NVIDIA A100 GPUs. Most exper- iments used GOT-OCR2.0 as the base model, trained on single server with 8 A100 GPUs and
7

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Table 1: Performance comparison of various VLMs orthographic projection reasoning tasks.
Without Reasoning Guidance
Reasoning Guidance
PROMPTS
+eferencc
+Attribute Explanation
Refereene+Annswer +Attribute
Test Img
Test Img
Img
Pair
Img
Pair
Explanation
Phi-3.5-Vision
3
4.22
8.95
8.05
6.26
11.32
14.40
16.16
15.79
LLaVA-OneVisionn
38
9.16
16,06
15,65
14.84
9.10
16.81
16.21
20.53
DeepSeek-VI VL
39
8.16
22.68
20.03
12.65
14.02
20.31
24.62
25.45
IntemVL2.5 40
15.79
18.82
23.30
17.16
15.63
22.35
24.47
23.73
IntemVLL 40
15.46
17.90
22.44
17.91
15.81
21.98
23.58
17.05
Qwen2.5-0min 41
23.43
28.89
28.74
26.50
26.12
30.93
29.40
35.71
Qwen2.5-VL.
4
24.54
30.76
30.47
25.86
24.54
32.78
33.64
38.88
Gemini2.5
24.23
30.09
29.39
25.55
25.68
30.61
32.30
35.88
Claude4 Vision
23.47
29.67
30.04
25.58
24.94
33.00
34.13
36.32
GPT-4o
45
26.06
31,56
32.28
27.52
27.79
34.17
39.71
38.56
Ours
80.86
82.99
83.24
82.67
81.35
83.11
82.87
84.03
batch size of 64. We adopted the AdamW optimizer with a cosine annealing scheduler. The hyperparameters were configured as follows:(1) Learning rate: le-6 for RL (GRPO) training and 2e-5 for baseline SFT experiments:(2) Maximum input image size: 2,483,776 pixels:(3) Total GRPO training steps: 1500.
Datasets and Metrics. We evaluated both leading vision lision-langugee models (VLMs) and our proposed method on TriView2CAD, following the evaluation strategy outlined Sec. The synthetic test used to assess in-domain accuracy. Due to the scarcity of real-world data, all 3,000 real- worldw samples were reserved exclusively for enatiin-eaabligg evaluation of outtrf-distribution (OOD) generalization in authentic CAD scenarios.
design four distinct prompt configurations to probe their orthographic projection reasoning capabilities: 1) Test image-Only: Models receive only the target rasterized drawings; 2) Test image + Reference Image: addition to the target drawings, models are given a reference image-rendered the same geometry layer with all dimension names supply semantic alignment; 3) Test image + Answered Pair: Inputs consist of the target drawings plus a correctly paired raster image and its parameter vector, providing exemplar mapping between visual features and quantitative parameters; 4) Test image Attribute Explanation: Models are supplied with the target drawings alongside detailed textual interpretation authored by professional engineers.
Given that orthographic projection reasoning involves complex multi-step inference and current pretrained VLMs have limited exposure to CAD drawings, we incorporated explicit reasoning guidance into each prompt format to encourage coherent reasoning trajectories. For each of the four prompt configurations, we outlined step-by-ster instructions for task completion- struccured to guide the model through the reasoning process systematically. The complete set instructions for each format is provided in the supplementary materials.
5.1 Performance on in-domain test set
The experimental results (Tab. 3) demonstrate that our method significantly outperforms existing vision-language models (VLMs) on orthographic projection reasoning tasks across prompt formats. Specifically, in the "Without Reasoning Guidance" setting, the "+Answered Pair" prompt achieves the highest accuracy of 83.24%. In the "With Reasoning Guidance" setting, our model reaches 84.03% accuracy with the "+Attribute Explanation" prompt. Notably, our method also delivers strong performance under the "Test Image Only" condition, achieving 80.86% accuracy- effectiveness of our proposed approach.
the
worth noting that the performance improvement from "without reasoning guidance" to "with reasoning guidance" is not as significant. This is primarily because, through our CReFT approach, the model learns effective reasoning strategies via the difficulty-aware reward mechanism, thereby reduc- ing the need for additional guidance to achieve further significant gains. Based on our benchmarking of the seven leading VL.Ms, we present several important findings, as follows: 1) This reasoning task remains tough challenge for pretrained VLMs. Overall, orthographic projection reasoning entails

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not only reading textual dimensions and visual features, but also matching annotations to geometry primitives, counting structural elements, and computing composite parameters. 2) Different prompt formats influence the performance. In both the Without Reasoning Guidance and With Reasoning Guidance settings, different prompt formats lead to varying degrees of performance improvement. 3)Introducing reasoning guidance yields consistent gains across all seven VLMs. Introducing appropriate reasoning guidance significantly enhances the model's ability to perform reasoning tasks effectively. Further analysis regarding the three findings are presented in the supplementary materials.
5.2 Performance on real-world test set
We evaluate out- disdiiitibuttiinn (OOD) generalization using all 3,000 real-world samples, which reflect authentic CAD scenarios. The baseline model (Qwen2.5-VD achieves 13.47% accuracy- than its 24.54% performance on synthetic data. This performance degradation is primarily attributed to the more complex structures and occlusion relationships in real-world data (Sec. B.T. which elevates task difficulty. We also compare with a model trained via standard supervised fine-tuning (SFT), which achieves 36.15% accuracy. This improvement stems from substantial gains in Primitive Counting tasks. However, the model still struggles with Composite Parameter Computation underscoring the limitations of conventional SFT. Our CReFT-based method achieves 46.67% accuracy, demonstrating its ability to effectively mitigate OOD challenges and deliver substantial performance gains on complex real-world data.
5.3
Abalation study
With CoT VS without CoT. The results from Tab. 2 reveal significant improvement in per- formance when incorporating chain-of-thought
Table 2: Ablation study results.
(CoT) reasoning. Specifically, the accuracy for individual Task increases from 46.16% to 74.15% after adding CoT, demonstrating a clear performance boost. When combining multiple training tasks, such as Task + Task and Task 2 + Task 3, the improvements are even more pronounced, with accuracies reaching 77.90% and 67.75%, respectively. The most notable enhancement is observed in the combination of Task + Task 2 Task 3, where the accuracy increases from 46.24% to 81.35% The primary source of these improvements lies in how CoT helps break down complex Composite Parameter Computation tasks into more manageable, incremental steps. The CoT mechanism, however, guides the model through each individual component of the computation, allowing for step-by-step approach that improves the model's s reasoning capability.
METHOD
w/o CoTwith CoT
Task3
46.16
74.15
Task + Task3
46.05
77.90
Task2 + Task3
30.71
67.75
Task1 + Task2 + Task3
46.24
81.35
Three training tasks. Notably, in the CoT setting, combining Task 1 and Task 3 provides performance boost, increasing accuracy from 74.15% to 77.90%. However, when Task 2 added, the model's performance decreases. This decline can be attributed to the design of Task 2, which includes a substantial number masked elements within the instruction data. By exposing the model higher uncertainty, Task 2 forces the model to contend with incomplete or ambiguous information, which ultimately hinders its ability to perform effectively on the task. On the other hand, when all three tasks (Task + Task 2 Task 3) are used together, the model's reasoning ability is fully activated, resulting in a significant performance improvement. In contrast, in the without CoT setting, the model's performance is constrained by its inability to effectively handle multi-step reasoning tasks, where even the best best-performing task combination achieves a maximum accuracy of only 46.24%.
Conclusion and Limitation
In this work, we address challenges in orthographic projection reasoning for Codputer-Aided Design (CAD) by proposing CReFT-CAD, an innovative two-stage fine-tuning paradigm. We demonstrated that the integration of curriculum-driven reinforcement learning (RL) with difficulty- aware rewards effectively enhances the model's 's reasoning ability. To further advance the field, we introduce Tri View2CAD, the first large-scale, open-source benchmark specifically designed for orthographic projection reasoning. With its comprehensive dataset of 200,000 synthetic and 3,000

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real-world samples, each annotated with precise dimensions and accompanied by six interoperable data modalities. In conclusion, this work lays the groundwork for more robust, scalable solutions to CAD-related challenges, providing both novel training paradigm and a rich dataset that can foster future research and innovation the field.
The accuracy on complex real-world scenarios has not yet reached a very high level. As such, the model's generalization ability remains key area for further development. In future work, we plan to explore techniques to enhance the model's robustness and adaptability.
Limitation.
ACKNOWLEDGMENT This work is supported in part by NSFC Project (No. 62176061) and Joint Laboratory of Intelligent Construction Engineering Technology for Operating Railway Lines.
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A
The complete reasoning guidance for each input format
For the Test Image-Only format:
Identify all annotated numbers in the orthographic projection, including dimensions, spacing, and height.
Interpret their meanings based on position, orientation, and surrounding context.
Directly assign values some parameters, count graphical elements for quantity parameters, and compute spacing parameters based on position.
For the +Reference Image format:
Use the reference template to understand the correspondence between primitive and parame- ters, and identify annotated numbers in the target image.
Assign parameters based on position and template primitive, counting graphical primitive for quantities.
Calculate parameters based on positional relationships or template definitions, and output the full set of parameter values.
For the +Answered Image format:
Learn the correspondence between structures and parameters from the example image. Identify and interpret annotated numbers in the target image, considering their position and similarity to the example.
Assign values to parameters, count graphical primitive for quantities, calculate parameters, and apply necessary constraints to produce final results.
For the +Attribute Explanation format:
Clarify parameter definitions and their geometric meanings, including component types and their properties.
Identify annotated numbers in the orthographic projection and infer their corresponding parameters.
Assign values parameters, compute quantities, derive spacing values from positional relationships. and output the complete parameter set with necessary constraints.
B Further analysis regarding the three findings
Table 3: Performance comparison of various VLMs orthographic projection reasoning tasks.
Without Reasoning Guidance
Reasoning Guidance
PROMPTS
-Reference+Answered +Attribute
FReference+Answene
+Attribute
Test Img
Test Img
Img
Pair
Explanation
Img
Pair
Explanation
Phi-3.5-Vishi
4.22
8.95
8.05
6,26
11.32
14.40
16.16
15.79
LLaVA-OneVision
9.16
16.06
15.65
14.84
9.10
16.81
16.21
20.53
DeepSeek-VV VL
8.16
22.68
20.03
12.65
14.02
20.31
24.62
25.45
In2e
15.79
18.82
23.30
17.16
15.63
22.35
24.47
23.73
In3
15.46
17.90
22.44
17.91
15.81
21.98
23.58
17.05
Qwen2.5-Onl 5mmnn
23.43
28.89
28.74
26.50
26.12
30.93
29.40
35.71
Qwen2.5-VI.
24.54
30.76
30.47
25.86
24.54
32.78
33.64
38.88
Ours
80.86
82.99
83.24
82.67
81.35
83.11
82.87
84.03
Remains a Tough Challenge for pretrained VLMSs Tab. 3 reports performance across four prompt formats without and with reasoning guidance. Overall, orthographic projection reasoning entails not only reading textual dimensions and visual features, but also matching annotations to ge- ometry primitives, counting structural elements, and computing composite parameters. Consequently,
20

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none of the off shelf models fully masters this composite task. Qwen2.5-VL. [46] achieves the highest accuracy- -8..8 under reasoning guidance with the Test Image Attribute Explanation prompt. This improvement stems primarily from its superior ability to parse geometry layers and read dimension labels. Crucially, performance on reasoning-intensive parameters remains low. These findings underscore that orthographic projection CAD cannot be solved by prompting alone and requires dedicated fine-tuning strategy.
2) Prompt Format-Dependen Performance Gains. Appending an Attribute Explanation to the Test Image consistently boosts aceuracy compared to the image-only baseline, with an average increase of 3 to 4 percentage points across models. This demonstrates that strong text encoders can leverage detailed, engineer-authored descriptions to guide complex geometric and numerical inferences. Similarly, providing Reference Image or an Answered Pair results in comparable improvements, with a percentage point increase in accuracy. These exemplars offer explicit visual-textual templates, simplifying the task of matching primitives to their corresponding semantic labels and dimensions. In contrast, using raw images forces models to address both perception and reasoning simultaneously, leading to the lowest performance. Collectively, these findings indicate that multimodal exemplars can enhance existing VLMs' ability to reason over orthographic projection. However, this further underscores that additional fine-tuning strategies are crucial to fully bridging the remaining performance gap.
3) Introducing reasoning guidance yields consistent gains across all seven VLMs. Introducing reasoning guidance demonstrates that step-wise reasoning guidance helps models better decompose the multi-step orthographic projection tasks. In the absence of reasoning guidance, visual exemplar prompts (+Reference Image and +Answered Pair) deliver the largest relative improvements. However, when reasoning guidance is added, the Attribute Explanation prompt shows the greatest uplift. It stems from reasoning guidance is textual form, which synergizes most effectively with textual inputs. Hence, models with stronger text encoders (e.g., (eepSeek-V1) exhibit disproportionately larger boosts. These results underscore the necessity of integrating structured reasoning guidance to advance orthographic projection CAD reasoning.
C More performance on in-domain test set
Table 4: Performance comparison of training- free model, SFT modelland our GRPO-based model on orthographic projection reasoning tasks.
Without Reasoning Guidance
Reasoning Guidance
PROMPTS
Cecferencc+Answerce +Attribute
+Reference Answered +Attribute
Test Img
Test Img
Img
Pair
Explanation
Img
Pair
Explanation
Qwen2.5-VL.
24.54
30.76
30.47
25.86
24.54
32.78
33.64
38.88
Qwen2.5- (SFFT)
76.33
79.50
77.12
78.64
76.42
76.78
77.20
80.30
Ours
80.86
82.99
83.24
82.67
81.35
83.11
82.87
84.03
Tab. presents the performance in-domain
The first (Qwen2.5-VL) represents training- free baseline, which exhibits consistently poor performance across all settings. indicating the inherent difficulty of this reasoning task without any fine-tuning or adaptation. The second row (Qwen2.5-VL with SFT) demonstrates significant performance improvement, particularly when provided with reference images, answered pairs, and attribute explanations. However, despite these gains, the model still suffers from notable drops in more complex reasoning tasks, especially those involving composite parameter computation. In contrast, our method consistently outperforms both baselines across all settings, achieving the highest accuracy in all prompt configurations, with particularly strong results under reasoning guidance. These results validate the effectiveness of our approach handling complex geometric reasoning and highlight its robustness across both simple and compositional inference scenarios.
D Failure cases and analysis
As illustrated in Fig. 5 we present four distinct failure cases, each highlighting different types of errors that can occur during the 3D model reconstruction process due to incorrect 2D parameterization.
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(A) Failure cases to incorrect dimensions
(b) Failure cases incorrect counting
(6) Failure cases of component stacking
301
Failure cases exceoding the physical boundaries
Figure 5: Four sets of failure cases. Each set consists of three parts: the left part shows the orthographic projection, the middle part presents the failed 3D model construction, and the right part illustrates the correctly constructed 3D model.
Fig. 5[a) a) demonstrates the error induced by incorrect dimensional parameters. Specifically, due to errors in the parameterization of certain dimensions, the constructed 3D model exhibits significant scaling issues, leading to distorted and non-accurate representation. This failure emphasizes the critical importance of precise dimensional input when translating from 2D projections to 3D models, as even minor errors in size parameters can drastically affect the final output. Fig. 5]b) illustrates a failure caused by incorrect counting, which results in an incorrect number of primitives being identified and subsequently integrated into the 3D model. The discrepancy in the number of primitives
22

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reflects the direct impact that improper counting and recognition of geometric elements can have on the success of the 3D reconstruction. In Fig. (fc), we observe stacking issue caused by errors in the calculation of composite parameters. The failure manifests as improper alignment of components in the final model, where parts the 3D structure fail align correctly with each other. Lastly, Fig. ((d) shows a failure resulting from exceeding physical boundaries due to incorrect composite parameter calculations. this case, the incorrect parameters lead to elements of the 3D model extending beyond the physical constraints limits of the original design. Together, these four failure cases provide strong evidence that errors in the parameterization of 2D models directly lead to inaccuracies in 3D model reconstruction. Each case illustrates the cascading effect of parameter errors from the 2D representation to the final 3D model, further underscoring the necessity for robust and precise parameterization methods in 2D-to-3D model conversion processes.
23


Reviews:
[{"review_id": "ZN7sCWs9wq", "reviewer": "Reviewer_8yVX", "summary": "This paper explores the problem of reasoning 3D CAD shapes with canonical orthographic projection images from multiple views. First, a large scale dataset is prepared with annotated drawings rendered in orthographic views and paired 3D shape data using parametric variations of constrained pier spacing configurations. Then, four tasks of varying difficulties are designed to probe if vision language models can truly reason by establishing correspondences between the orthographic views and the 3D shape. Using reinforcement learning and instruction tuning, the GOT-OCR2.0 model is aligned on 4 tasks of increasing complexity using curriculum learning and demonstrated to reason significantly better than off the shelf. Vision language models.", "questions": "- Can the authors motivate better on why reinforcement  learning was required and supervised finetuning with standard next token prediction loss wasn’t sufficient? This design decision should be explained and it would be valuable for readers.\n- Can the method be tested on examples that are not piers? This is crucial to understand how well the training scheme helps the model to truly understand orthographic view images of CAD shapes.\n- Can the authors touch upon real world CAD problems that would benefit from the improvements in reasoning achieved in this work? This would solidify the impact of this work and help readers understand the motivation of this paper.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n\n- the paper tackles an important and under-explored problem of reasoning 3D CAD shapes from orthographic drawings. Drawings are indeed a key component in CAD design and manufacturing workflows, and not many previous works have explored techniques to improve reasoning of vision language models on drawings. \n- the large scale dataset with paired data containing drawings, their dimensions, and STEP files are useful for the community to build upon, and is a good contribution.\n- the proposed method clearly works well as demonstrated by strong quantitative results.\n\nWeaknesses:\n\nI don’t see any major weaknesses in the paper with regards to problem scoping or technical contributions. But a few points:\n- The exposition could be improved. It is not clear why reinforcement learning is required for this problem. The data preparation for training and specifics of how the model output connects with the loss function must be made more clear.\n- both the synthetic and real world data for testing are parametrically varied piers. This narrows the scope quite drastically and generalization outside of this distribution was not demonstrated.\n- the practical impact of better reasoning orthographic view reasoning achieved in this work is not clearly demonstrated by showcasing improvements in specific downstream applications.", "was_revised": true, "final_justification": "I stand by the original score. The authors have addressed most of my concerns in the rebuttal and I expect them to revise the final paper accordingly. But even in my original review, I was tending towards accepting the paper, so I will stick with the same score.", "rebuttal": "We sincerely thank Reviewer 8yVX for the thoughtful evaluation and for recognizing the technical soundness, originality, and relevance of our work. We appreciate your positive remarks on the importance of orthographic reasoning in CAD workflows, the value of our large-scale dataset, and the strong quantitative results. Below, we address your comments in detail.\n\n**W1&Q1: Why reinforcement learning is required**\n\nOrthographic projection reasoning presents fundamentally different challenges compared to conventional CAD tasks such as primitive detection or parametric annotation. While the latter focus on identifying individual geometric entities and retrieving associated dimensions, orthographic reasoning requires a holistic semantic understanding of multi-view technical drawings. This includes tasks such as cross-view correspondence, and performing composite parameter calculations that span multiple projections and demand multi-step spatial and logical inference.\n\nSuch complexity cannot be effectively addressed through supervised learning alone, which primarily drives models to imitate training distributions rather than truly reason. Specifically, Supervised Fine-Tuning (SFT) provides stable token-level optimization via cross-entropy loss, which is useful for formatting and mimicry, but inherently limited in fostering the higher-order reasoning skills required for orthographic tasks.\n\nTo overcome this limitation, we introduce a curriculum-based Reinforcement Learning (RL) framework with task-level, semantically grounded reward functions. Rather than optimizing token-level accuracy, our method defines goal-directed rewards based on full-task correctness, covering binary decisions, multiple-choice selection, and chain-of-thought parameterization.\nThis approach enables the model to move beyond surface-level pattern recognition by cultivating structured reasoning chains tailored to the logic of orthographic projection.  Moreover, by optimizing directly for task-level correctness through semantically meaningful rewards, the method reduces reliance on large-scale, high-quality annotations, which are often expensive to obtain and require significant domain expertise in the CAD field.\n\nBy combining curriculum-driven RL with SFT post-tuning, our framework balances reasoning capability with instruction-following fluency, making it well-suited for complex, industrial-grade CAD understanding.\n\n**W1: Clarifying Data-Training-Loss Connections**\n\nAs described in Section 3.1 of the main paper, we have detailed the construction of our dataset, including the synthetic data generation pipeline.\nAs described in Lines 192–220 of the main paper, during the reinforcement learning phase, we have constructed 160,000 image–instruction pairs from the synthetic subset of the TriView2CAD dataset. Each prompt contains an orthographic projection view alongside its complete set of 15 parametric key–value pairs. Based on our three designed learning tasks, we apply corresponding reward functions to compute relative advantages for policy optimization. This structured curriculum enables the model to progressively activate and refine its orthographic reasoning capabilities, rather than relying on superficial feature extraction.\nIn the Supervised Post-Tuning phase, we have further refined the model using 20,000 instruction–response pairs, aiming to strengthen its instruction-following abilities in a more controlled and aligned setting. Beyond generic response formatting, this stage is specifically designed to equip the model with capabilities tailored to real-world CAD tasks. Based on our deep understanding of practical industrial workflows, we have structured the supervision to support four essential task types: producing a complete parameter list from multi-view drawings, validating parameter correctness against reference values, matching corresponding elements across orthographic views, and counting components embedded within complex assemblies. These tasks closely reflect operational needs in engineering design, simulation, and quality assurance, thus ensuring the model’s outputs are not only syntactically correct but also functionally relevant and deployable in real CAD scenarios.\n\n\n**W2&Q2 Limited Task Domain**\n\nFirst, our choice of bridge pier drawings is motivated by their non-trivial geometric constraints, which inherently offer rich parameter spaces and meaningful semantic structures. \n\nSecond, CAD drawings across domains exhibit substantial structural commonality. Tasks such as parameter extraction, component counting, and composite parameter calculations are ubiquitous subtasks in orthographic projection reasoning, regardless of the specific industry or object type. The three reinforcement learning tasks and their associated reward functions that we propose are directly transferable across different CAD domains sharing the orthographic paradigm. As such, although the current dataset may have limitations in geometric variability, it remains a solid foundation for developing robust and generalizable training strategies.\n\nThird, our dataset is a high-quality benchmark with detailed parameter annotations and real-world CAD drawings. It is extremely difficult to obtain, as it typically requires access to proprietary engineering documents and expert-level annotation. Its richness and annotation depth provide a solid foundation for developing and evaluating CAD reasoning task in realistic scenarios.\n\nFinally, since our approach does not rely on mimicking the original data distribution, but instead focuses on inducing true orthographic reasoning capability, we believe the model would maintain strong performance even if trained on other object types or visual styles. In fact, integrating multiple categories into the training process may introduce beneficial diversity, enhancing the model’s reasoning generality through exposure to broader spatial and semantic patterns.\n\n\n**W3&Q3:Downstream applications**\n\nAs shown in Lines 527–537 of the main paper, we have conducted evaluations on real-world data under out-of-distribution (OOD) settings. Even in these challenging scenarios, our method demonstrate substantial performance gains, highlighting its robustness and generalization capability beyond the synthetic training distribution.\n\nAs presented in Section D of the supplementary material, we have provided visualizations and detailed analyses of failure cases encountered during testing on real-world data. These examples highlight specific challenges that remain open. In future work, we plan to further enhance our framework to address these issues more effectively and improve robustness under complex real-world conditions."}, {"review_id": "nsj1oFVjFt", "reviewer": "Reviewer_Kh3L", "summary": "This paper aims to address a long-standing challenge in the field of CAD: enabling AI models to perform accurate reasoning on orthogonal projection drawings. The authors point out that existing methods, whether traditional 3D reconstruction or simple SFT, suffer from issues such as insufficient accuracy, lack of editability, and poor generalization ability.\n\nTo tackle these problems, the paper proposes two major core contributions:\n\nCReFT-CAD Method: An innovative two-stage fine-tuning paradigm. The first stage employs curriculum-driven RL, cultivating the model's deep reasoning capabilities through tasks ranging from easy to difficult and a difficulty-aware reward mechanism. The second stage enhances the model's instruction following and interactive capabilities through supervised post-fine-tuning.\n\nTriView2CAD Dataset: The first large-scale, open-source benchmark specifically designed for this task. It comprises 200,000 synthetic samples and 3,000 real-world samples, featuring precise dimension annotations and six interoperable data modalities, providing valuable resources and evaluation standards for research.\n\nExperimental results demonstrate that CReFT-CAD significantly outperforms several leading VLMs on the TriView2CAD benchmark and exhibits stronger generalization ability on real-world OOD samples.", "questions": "* What major challenges do you foresee in applying the CReFT-CAD framework to other CAD domains? Would differences in parameter space and increased geometric complexity necessitate significant modifications to the curriculum or reward functions?\n\n* Could you provide specific examples of incorrect answers given by the model on real-world datasets? Analyzing these failure cases could help us better understand the limitations of the current method and directions for future improvements.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strenghths:**\n* The \"orthogonal projection reasoning\" problem addressed by this paper is one of immense value in the industry, yet remains insufficiently resolved within the deep learning community. The authors provide a compelling motivation for their proposed approach.\n\n* The CReFT-CAD method's design, which mimics the human cognitive learning process by progressing from easier to more challenging tasks, effectively contributes to the stable establishment of the model's reasoning capabilities. \n\n* On benchmark tests, CReFT-CAD's accuracy significantly surpasses all baseline models, unequivocally demonstrating the effectiveness of their methodology.\n\n**Weaknesses**\n* All experiments in the paper are concentrated on a specific CAD category: prefabricated bridge piers. While this serves as an excellent starting point, its parameter space and geometric complexity remain relatively limited.\n\n* The paper claims that its method enhances \"reasoning ability,\" which is demonstrated through its task design and performance metrics. However, the analysis regarding the precise nature of the reasoning capabilities the model has acquired is not yet sufficiently in-depth.", "was_revised": true, "final_justification": "The authors have effectively transferred a mature pipeline from the LLM domain—combining Supervised Fine-Tuning with curriculum-based Reinforcement Learning to the field of CAD. This application demonstrates a degree of novelty, but I find the innovation to be somewhat limited, as it is primarily an application of an established methodology to a new domain rather than a fundamental advance.\nFor this reason, I will maintain my original rating. The paper is technically solid, and the reasons to accept outweigh the reasons to reject, but the limited methodological novelty makes it a borderline case.", "rebuttal": "We sincerely thank Reviewer Kh3L for the thorough and constructive review. We are grateful for your positive assessment of our motivation, technical design, and empirical contributions. We especially appreciate your recognition of the industrial value of orthographic projection reasoning, the cognitive structure of our curriculum-based RL method, and the strong performance of our model on the TriView2CAD benchmark.\nBelow, we respond in detail to the two main concerns and two insightful questions you raised.\n\n**W1&Q1 CAD category limited**\n\nOur selection of prefabricated bridge pier drawings is grounded in their geometric complexity. Despite belonging to a specific subdomain, these structures exhibit rich parametric relationships and semantic constraints that pose meaningful challenges for orthographic projection reasoning.\n\nImportantly, many CAD tasks across diverse domains frequently involve similar cognitive subtasks, such as parameter extraction, component enumeration, and multi-view composite parameter inference. \nThe curriculum tasks we have designed for reinforcement learning are not tailored to a specific object type, but rather capture generalizable reasoning patterns that are widely applicable to other CAD categories. Since these tasks are grounded in fundamental principles of orthographic projection reasoning, they can be readily transferred and reused across domains that follow similar design logic.\n\nWe also emphasize that, our dataset is a high-quality benchmark with detailed parameter annotations and real-world CAD drawings. It is extremely difficult to obtain, as it typically requires access to proprietary engineering documents and expert-level annotation. Its richness and annotation depth provide a solid foundation for developing and evaluating CAD reasoning task in realistic scenarios.\n\nLastly, the core strength of our method lies in learning the reasoning process itself, rather than fitting to any fixed data distribution. This property makes the approach generalizable to other CAD categories. We further posit that expanding the dataset to include multiple CAD domains will likely further enhance model robustness by encouraging the learning of abstract and transferable inference patterns.\n\n**W2 reasoning ability**\n\nFrom a quantitative analysis perspective, we have initially conducted experiments using the same training data but applied only a supervised fine-tuning (SFT) strategy. Under evaluation settings analogous to those in Table 1 of the main paper, this SFT-only baseline achieved an overall accuracy of 80.3%, with 85.4% on recognition parameters, 97.1% on counting parameters, and 66.8%on composite calculation parameters. In contrast, our reinforcement learning–based training method achieved 84.03% overall accuracy, with 87.1% on recognition parameters, 97.3% on counting parameters, and a significantly improved 74.3% on composite calculation parameters. This comparison clearly demonstrates that reinforcement learning brings substantial gains, particularly for tasks requiring multi-step inference and reasoning, such as composite parameter calculation. These results provide strong empirical evidence that our curriculum-based RL strategy effectively endows the model with enhanced reasoning capabilities beyond what is achievable through pattern imitation via SFT alone.\n\nFrom a qualitative analysis perspective, traditional supervised fine-tuning (SFT) offers token-level supervision via cross-entropy loss, optimizing the next-token likelihood. While this approach enables effective pattern replication from annotated samples, it typically lacks the capacity to instill genuine reasoning. In contrast, our framework applies curriculum-based reinforcement learning, with task-level, semantically meaningful rewards. Rather than optimizing for local token-level correctness, we design goal-driven reward functions that evaluate whether the model completes the entire task correctly. This structure encourages the learning of full reasoning chains rather than superficial pattern memorization. Through this setup, the model acquires multi-step inference abilities necessary for solving complex CAD tasks like parameter counting, component relationships, and dimensional composition. \n\n\n**Q2 Failure cases**\n\nAs shown in Appendix section D, we provide several representative failure cases along with analysis."}, {"review_id": "wfkheJf8a7", "reviewer": "Reviewer_M7s3", "summary": "The paper introduces CReFT-CAD, a two-stage fine-tuning paradigm for orthographic projection reasoning in the context of Computer-Aided Design (CAD). The method integrates curriculum-driven reinforcement learning with difficulty-aware rewards, followed by a phase of supervised post-tuning to bolster instruction following and semantic extraction. To support evaluation, the authors present TriView2CAD, a large-scale benchmark containing 200,000 synthetic and 3,000 real-world samples, each linked to six interoperable data modalities with precise dimension annotations. Experiments benchmark leading vision-language models (VLMs) and demonstrate that CReFT-CAD yields improved accuracy and out-of-distribution generalization on orthographic projection reasoning tasks.", "questions": "1: Could authors provide further analysis of the curated datasets (e.g., the diversity of shapes/objects, etc)?\n\n2: How would the performance of industry-level VLMs (such as GPT-4o, Gemini 2.5 flash) be on the proposed benchmark?", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1: The introduction of TriView2CAD is a meaningful addition to the field. The benchmark is described in detail and includes synthetic and real-world samples across six data modalities, supporting a wide spectrum of CAD tasks. This addresses long-standing challenges in the lack of open, precisely annotated datasets for orthographic projection reasoning.\n\n2: The proposed CReFT-CAD leverages a curriculum-driven reinforcement learning approach with customized, difficulty-aware reward functions. This approach is designed to systematically build up reasoning ability, followed by supervised post-tuning targeted at enhancing instruction. The methodology is well described and is tightly aligned to the practical needs of the CAD workflow (design, manufacturing, simulation).\n\n3: Extensive experiments are conducted, benchmarking seven VLM baselines plus the proposed method. The results show CReFT-CAD's clear superiority over existing VLM baselines, especially on complex tasks and out-of-distribution (OOD) real-world scenarios.\n\nWeaknesses:\n\n1: **Analysis of Prompt Engineering.** Several prompt configurations are presented, both with and without reasoning guidance. While it is shown that reasoning guidance helps, it is not always clear if the observed gains are attributable to the underlying model improvements or just to the engineered prompt. In addition, the evaluation assumes a fixed set of prompt templates, which could constrain generalizability.\n\n2: **Insufficient Analysis of Dataset Diversity.** The paper does not provide a thorough analysis of the characteristics of the proposed dataset, TriView2CAD. Based on the visualizations provided, many CAD models seem structurally similar, often consisting of repetitive patterns (e.g., cylinder-cuboid-cylinder arrangements). Such structural uniformity might limit the diversity of the benchmark, potentially inflating performance metrics or restricting the robustness evaluation of the models. Therefore, it is crucial to systematically assess and explicitly discuss the dataset's geometric and structural diversity.\n\n3: **Evaluation of Industrial-Level VLMs.** Although the paper benchmarks various existing vision-language models (VLMs), it neglects to include current industrial-grade or commercially deployed VLMs in its evaluations. Given that the practical motivation is closely tied to industrial CAD applications, testing state-of-the-art industrial VLM would be highly valuable. Such analysis could provide clearer insights into how the proposed method compares against real-world industry standards and further demonstrate the practical significance of the proposed framework.", "was_revised": true, "final_justification": "I recommend accepting paper because of its contribution to data collection. I hold the opinion that the biggest contribution of this paper is a large-scale CAD dataset to assist the community in verifying ideas on CAD model understanding. And, the authors' replies address most of my concerns about evaluation. Therefore, I recommend acceptance.", "rebuttal": "We sincerely thank Reviewer M7s3 for the constructive feedback and for acknowledging the significance of both our CReFT-CAD framework and the TriView2CAD benchmark. We appreciate your recognition of the real-world motivation, the alignment to CAD workflow stages, and the model’s superior performance on complex and out-of-distribution (OOD) tasks.\nBelow we address your thoughtful critiques regarding prompt engineering analysis, dataset diversity, and comparisons to industrial-grade models.\n\n**W1 Analysis of Prompt Engineering**\n\nAs detailed in Lines 300–305 of the main paper and further elaborated in Appendix B.3, we conducted a systematic study of prompt engineering from two perspectives:\n(1) the presence or absence of reasoning guidance, and\n(2) the effect of different input formatting styles, including attribute explanations and structural text augmentation.\nWhile we observe that reasoning-guided prompts lead to performance improvements across all models, CReFT-CAD consistently outperforms all baseline VLMs under identical prompt templates. This strongly suggests that the gains stem primarily from the enhanced reasoning ability acquired during RL fine-tuning, not merely from the prompt structure.\n\nThat said, we fully acknowledge the critical role of prompt design in VLM adaptation. Our experiments are meant to shed light on this often-understudied factor by providing comparative insights into how prompting strategies affect both our method and existing VLM baselines. We hope these results offer meaningful guidance to the community.\n\nImportantly, the prompt templates are not hard-coded or universal—in practice, reasoning guidance provided by domain experts varies substantially across CAD categories. Moreover, we found that adding attribute-level textual explanations, such as descriptions of component functions or constraints, significantly enhances performance on orthographic-view reasoning tasks. The chosen input modality (text, image, etc.) should be seen as a flexible reference point rather than a rigid constraint.\n\n**W2 &Q1 Insufficient Analysis of Dataset Diversity**\n\nOur selection of prefabricated bridge pier drawings is grounded in their geometric complexity. Despite belonging to a specific subdomain, these structures exhibit rich parametric relationships and semantic constraints that pose meaningful challenges for orthographic projection reasoning.\n\nImportantly, many CAD tasks across diverse domains frequently involve similar cognitive subtasks, such as parameter extraction, component enumeration, and multi-view composite parameter inference. \n\nThe curriculum tasks we designed for reinforcement learning are not tailored to a specific object type, but rather capture generalizable reasoning patterns that are widely applicable to other CAD categories. Since these tasks are grounded in fundamental principles of orthographic projection reasoning, they can be readily transferred and reused across domains that follow similar design logic.\n\nWe also emphasize that, our dataset is a high-quality benchmark with detailed parameter annotations and real-world CAD drawings. It is extremely difficult to obtain, as it typically requires access to proprietary engineering documents and expert-level annotation. Its richness and annotation depth provide a solid foundation for developing and evaluating CAD reasoning task in realistic scenarios.\n\nLastly, the core strength of our method lies in learning the reasoning process itself, rather than fitting to any fixed data distribution. This property makes the approach generalizable to other CAD categories. We further posit that expanding the dataset to include multiple CAD domains will likely further enhance model robustness by encouraging the learning of abstract and transferable inference patterns.\n\n**W3&Q2Evaluation of Industrial-Level VLMs.**\n\nThis is an excellent and important suggestion. We have extended our evaluation to include several industry-level VLMs, specifically Gemini 2.5 Pro, Claude 4 Vision, and GPT-4o. The results of these evaluations have been compiled and presented in the same format as Table 1 in the main paper, for consistency and comparison.\nWe will integrate these results into the revised version of the paper, along with additional analysis of each model’s strengths and limitations on orthographic projection reasoning tasks.\n\n| Gemini 2.5 Pro   | 24.23 | 30.09 | 29.39 | 25.55 | 25.68 | 30.61 | 32.30 | 35.88 |\n\n| Claude 4 Vision  | 23.47 | 29.67 | 30.04 | 25.58 | 24.94 | 33.00 | 34.13 | 36.32 |\n\n| GPT-4o &nbsp;&nbsp;&nbsp;   &nbsp;&nbsp;&nbsp;      &nbsp;&nbsp;&nbsp;  &nbsp;   | 26.06 | 31.56 | 32.28 | 27.52 | 27.79 | 34.17 | 39.71 | 38.56 |"}, {"review_id": "k3d63PAMyY", "reviewer": "Reviewer_jq8V", "summary": "This paper performs orthographic projection reasoning in CAD workflows by introducing CReFT-CAD, a two-stage fine-tuning framework for vision–language models.The main idea is a Curriculum-driven Reinforcement Fine-Tuning (CReFT), a three-task curriculum (binary choice, multiple choice, chain-of-thought parameterization). They then perform supervised post-tuning of the output. The paper also introduces a TriView2CAD Benchmark, which is a new dataset of 200 K synthetic + 3 K real-world three-view samples.", "questions": "How sensitive are results to the chosen reward weights?\nHave you analyzed which specific real-world drawing features (e.g., occlusions, annotation styles) cause most OOD failures? A breakdown by task type would be helpful.\nWhat are the complex models in TriView2CAD dataset? Does it include more complex features than cylinders and blocks?", "limitations": "Authors acknowledge limited real-world accuracy and plan to improve generalization.\nThe dataset complexity seems very limited from the examples shown in the paper. I am willing to change this if the authors explain how diverse their dataset is.", "rating": 2, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n+ The integration of RL followed by fine tuning seems to be reasonable.\n+ TriView2CAD’ dataset can fill a critical gap in CAD reasoning datasets\n\nWeaknesses\n- Despite improvements, real-world accuracy remains under 50 %\n- It is unclear what the different models make up the TriView2CAD dataset. Only a limited set of examples is shown in the paper making it unclear if the method will work on more complex CAD models with complex extruded features instead of just cylinders and blocks.", "was_revised": true, "final_justification": "While I do acknowledge the authors effort in extending their evaluation to a more complicated dataset, the initial paper itself should have included this. In addition, this also requires a thorough evaluation of the approach on the expanded CAD dataset, which was missing from the initial submission. I still feel the work is in the right direction, but as is, it is a bit premature.", "rebuttal": "We thank Reviewer jq8V for carefully reading our paper and for identifying important areas that warrant clarification and improvement. We appreciate your recognition of the value of combining reinforcement learning with post-tuning and the contribution of our TriView2CAD dataset to the CAD reasoning community.\nBelow, we respond to each concern in detail and outline how we plan to address these points in the revised version.\n\n**W1&Q2 real-world accuracy**\n\nWe fully acknowledge that achieving high accuracy in real-world orthographic projection reasoning, particularly under out-of-distribution (OOD) conditions, remains a significant and ongoing challenge.\n\nWhile the current performance on real-world data does not yet meet the stringent requirements of industrial deployment, it is nonetheless encouraging that our model consistently outperforms existing vision–language baselines under such demanding conditions. We believe this offers valuable insights and direction for future research within the community.\n\nAs illustrated in Figure 3 of the main paper and Figure 1 of the supplementary material, real-world data in our benchmark presents three key challenges:\n\nMulti-part CAD Assemblies: A single real-world CAD drawing often contains all orthographic views of multiple components within one large drawing. This makes it non-trivial for the model to localize and reason about individual parameters. For visualization clarity, we cropped the most relevant regions for display in the paper, but the model itself must operate on the entire, cluttered canvas.\n\nOcclusions Across Components: Due to the multi-part layout, structural components often occlude each other (as shown in Fig. 3 top view). This creates visual interference that degrades spatial clarity and makes reasoning harder.\n\nSemantic Noise from Irrelevant Annotations: Because the drawing includes annotations for many unrelated parts, the model must disentangle relevant cues from semantically unrelated or misleading labels.\n\nThese factors collectively make the real-world subset of TriView2CAD significantly harder than the synthetic benchmark. That said, we view our current gains on this data as a promising first step toward reliable CAD understanding, reflecting clear momentum rather than a shortcoming of the approach.\n\n\n**W2&Q3 Diversity**\n\nThank you for raising this important point. First, our choice of bridge pier drawings is motivated by their non-trivial geometric constraints, which inherently offer rich parameter spaces and meaningful semantic structures. \n\nSecond, CAD drawings across domains exhibit substantial structural commonality. Tasks such as parameter extraction, component counting, and composite parameter calculations are ubiquitous subtasks in orthographic projection reasoning, regardless of the specific industry or object type. The three reinforcement learning tasks and their associated reward functions that we propose are directly transferable across different CAD domains sharing the orthographic paradigm. As such, although the current dataset may have limitations in geometric variability, it remains a solid foundation for developing robust and generalizable training strategies.\n\nThird, our dataset is a high-quality benchmark with detailed parameter annotations and real-world CAD drawings. It is extremely difficult to obtain, as it typically requires access to proprietary engineering documents and expert-level annotation. Its richness and annotation depth provide a solid foundation for developing and evaluating CAD reasoning task in realistic scenarios.\n\nFinally, since our approach does not rely on mimicking the original data distribution, but instead focuses on inducing true orthographic reasoning capability, we believe the model would maintain strong performance even if trained on other object types or visual styles. In fact, integrating multiple categories into the training process may introduce beneficial diversity, enhancing the model’s reasoning generality through exposure to broader spatial and semantic patterns.\n\n\nIt is also worth noting that domain specificity is a common design choice in this field. For example, in the recent work: CAD2Program[1], the entire training and evaluation pipeline is conducted on a single CAD domain—kitchen cabinet drawings. Their goal, which aligns with ours, is to test large-model reasoning capabilities under domain-specific constraints.\nIn contrast, our contribution lies in introducing a reinforcement-learning-based, multi-task curriculum that strengthens generalization and structured reasoning. \n\n[1]Wang et al., From 2d cad drawings to 3d parametric models: A vision-language approach. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, 7961–7969. \n\n\n**Q1 How sensitive are results to the chosen reward weights?**\n\nThank you for raising this insightful point. We are happy to clarify how reward functions are applied in our framework.\n\nFirst, as noted in Equations (1)–(3) of the main paper, each sample in our dataset corresponds to a specific task type, and hence at training time, only one reward function is active per sample. Thus, there is no simultaneous interaction or weighting between different reward functions, and no reward balancing across task types is needed.\nRegarding reward value sensitivity (particularly in Eq. 2 and Eq. 3), we did not perform exhaustive ablations on individual reward magnitudes for the following reasons:\n\nRelative reward differences—not absolute values—drive optimization. The design goal of our reward functions is to distinguish high-quality from suboptimal outputs. As long as there are meaningful gaps in reward assignment, the policy gradient update encourages exploration along paths of relatively greater return. This mostly influences convergence speed, rather than final policy quality, especially after large-scale sampling.\n\nDuring RL updates, we compute advantage functions using normalized returns, which further attenuates the effect of raw reward magnitudes. This helps ensure training stability and makes performance less sensitive to specific numeric values.\nThat said, we fully agree that studying reward value configurations is valuable. We are preparing an ablation experiment that tests representative reward schemes, including variations in the partial-credit structure for Eq. 2 and the difficulty-tiered scaling in Eq. 3. \nWe will include these results in the revised version, and we appreciate your suggestion to further ground the method in principled analysis."}, {"review_id": "EoKvo6eLYN", "reviewer": "Reviewer_LRf1", "summary": "The paper proposes a solution for using foundational models to help CAD in generating orthographic projection of the models, which is a crucial in the design pipeline, instead of directly going for 3D model generation. To this end, a new dataset is proposed and a fine-tuning strategy based on curriculum learning is presented. Experiments are done and compared with several foundational models. The results show the proposed new data with the fine-tuning strategy can achieve good results on the specific tasks.", "questions": "Following my comments below, I have a few questions below:\n\n1. It seems that the primary contribution of the paper is the data. However, the majority of the data seems to be synthetic which is normally cleaner, easier to handle than real-world data. Since the root of the research is a real-world application. This might hurt the contribution of the paper. Also, from the provide visualization of the data, they seem to be restricted to a certain type of objects. Considering CAD is used almost in every industry, I wonder how diverse the data is. I am not an expert in CAD, so I will leave this to the domain expert to judge the value of the new data.\n\n2. The proposed fine-tuning strategy is interesting. As an AI and machine learning conference, one natually asks the question how general the proposed method is when applied to other data and tasks. Currently, the curriculum design seems to be centered around the specific data and task. It would be good to discuss if there is fundamental difference between this specific curriculum design and other simiar research. I am asking for more experiments but only wonder if this new CAD task requires a categorically different curriculum design.\n\n3. The OOD test on real-world data is highly appreciated. However the analysis is rather short. It is important for papers like this one to analyze what difficulties when facing real-world data. In what sense they are out of distribution? Is the worse performance mainly caused by the lower quality of real-world data, or the diversity? As a result, the paper did not seem to achieve the goal it claims to achieve in the beginning.", "limitations": "yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1. The paper is written clearly, easy to follow and understand.\n2. The experiments can show certain promising aspects of the proposed methodology and the analysis on the comparative performances under different settings is helpful\n\nWeaknesses:\n\n1. There is an unclear difference between the proposed method and other similar methods in CAD, especially when it comes to the technical contribution.\n\n2. The realism and diversityi of the data itself seem to limited. \n\n3. Limited real-world applicability\n\nPlease see my comments below", "was_revised": true, "final_justification": "After the rebuttal, my questions are answered. Therefore, I am staying on the positive side.", "rebuttal": "We sincerely thank Reviewer LRf1 for the thoughtful comments and for recognizing the clarity of presentation, the promising results, and the value of comparative analysis. Below we address your main concerns regarding (1) technical contribution beyond data, (2) dataset realism and diversity, and (3) generality and real-world applicability of our method.\n\n\n**W1 difference between the proposed method and other similar methods in CAD**\n\nSFT offers stable token-level supervision based on cross-entropy loss, optimizing the likelihood of the next token. While this is effective for mimicking labeled samples, it is insufficient for orthographic reasoning tasks, which require multi-step spatial inference and arithmetic computation—especially for counting parameters and composite calculation parameters.\n\nIn contrast, we adopt a curriculum-based RL framework with task-level semantic rewards. Instead of optimizing per-token accuracy, we define goal-directed reward functions for full-task correctness:\nThis method encourages the model to learn actual reasoning chains, not just memorize patterns. More importantly, RL allows effective learning without requiring large-scale high-quality annotations—an essential benefit in the CAD domain, where ground-truth labeling is both costly and domain-expert dependent.\n\n**W2&Q1 Diversity**\n\nThank you for raising this important point. First, our choice of bridge pier drawings is motivated by their non-trivial geometric constraints, which inherently offer rich parameter spaces and meaningful semantic structures. \n\nSecond, CAD drawings across domains exhibit substantial structural commonality. Tasks such as parameter extraction, component counting, and composite parameter calculations are ubiquitous subtasks in orthographic projection reasoning, regardless of the specific industry or object type. The three reinforcement learning tasks and their associated reward functions that we propose are directly transferable across different CAD domains sharing the orthographic paradigm. As such, although the current dataset may have limitations in geometric variability, it remains a solid foundation for developing robust and generalizable training strategies.\n\nThird, our dataset is a high-quality benchmark with detailed parameter annotations and real-world CAD drawings. It is extremely difficult to obtain, as it typically requires access to proprietary engineering documents and expert-level annotation. Its richness and annotation depth provide a solid foundation for developing and evaluating CAD reasoning task in realistic scenarios.\n\nFinally, since our approach does not rely on mimicking the original data distribution, but instead focuses on inducing true orthographic reasoning capability, we believe the model would maintain strong performance even if trained on other object types or visual styles. In fact, integrating multiple categories into the training process may introduce beneficial diversity, enhancing the model’s reasoning generality through exposure to broader spatial and semantic patterns.\n\n**W3&Q3 Limited real-world applicability**\n\nWe fully acknowledge that achieving high accuracy in real-world orthographic projection reasoning, particularly under out-of-distribution (OOD) conditions, remains a significant and ongoing challenge.\n\nWhile the current performance on real-world data does not yet meet the stringent requirements of industrial deployment, it is nonetheless encouraging that our model consistently outperforms existing vision–language baselines under such demanding conditions. We believe this offers valuable insights and direction for future research within the community.\n\nAs illustrated in Figure 3 of the main paper and Figure 1 of the supplementary material, real-world data in our benchmark presents three key challenges:\n\nMulti-part CAD Assemblies: A single real-world CAD drawing often contains all orthographic views of multiple components within one large drawing. This makes it non-trivial for the model to localize and reason about individual parameters. For visualization clarity, we cropped the most relevant regions for display in the paper, but the model itself must operate on the entire, cluttered canvas.\n\nOcclusions Across Components: Due to the multi-part layout, structural components often occlude each other (as shown in Fig. 3 top view). This creates visual interference that degrades spatial clarity and makes reasoning harder.\n\nSemantic Noise from Irrelevant Annotations: Because the drawing includes annotations for many unrelated parts, the model must disentangle relevant cues from semantically unrelated or misleading labels.\n\nThese factors collectively make the real-world subset of TriView2CAD significantly harder than the synthetic benchmark. That said, we view our current gains on this data as a promising first step toward reliable CAD understanding, reflecting clear momentum rather than a shortcoming of the approach.\n\nAs presented in Section D of the supplementary material, we provide visualizations and detailed analyses of failure cases encountered during testing on real-world data. These examples highlight specific challenges that remain open. In future work, we plan to further enhance our framework to address these issues more effectively and improve robustness under complex real-world conditions.\n\n\n\n**Q2  applied to other data and tasks**\n\nBased on our extensive experience with CAD-related tasks, we have found the following:\nThe parameter space exhibits strong consistency across different types of CAD objects. Tasks such as parameterization, counting, and composite parameter computation are commonly found across a wide range of engineering domains.\n\nHowever, CAD drawings are used in many industries, and some sectors may indeed involve specialized domain requirements. In such vertical applications, customized parameter space design may further improve performance.\n\nThe three reinforcement learning tasks and their associated reward functions that we propose are directly transferable across different CAD domains sharing the orthographic paradigm.\n\nTherefore, we believe our training paradigm is both robust and scalable, and can be effectively applied to new CAD fields—even those with greater geometric complexity or differing annotation standards."}]

Author rebuttal:

Meta-review: This paper proposes a method for reasoning about 3D CAD shapes using canonical orthographic projection images from multiple views. The approach combines reinforcement learning with supervised post-tuning to improve instruction following and semantic extraction. Reviewers identified several strengths: (a) novelty of the research direction, (b) introduction of the new TriView2CAD dataset, (c) significantly better results compared to prior work, and (d) real-world out-of-distribution evaluations. They also noted some weaknesses: (a) experiments focus primarily on a specific CAD category (prefabricated bridge piers) with limited diversity (e.g., cylinder-cuboid configurations), (b) lack of evaluation with industrial VLMs, (c) unclear requirements for prompt engineering, and (d) low real-world accuracy. During the rebuttal, the authors presented additional experiments with industrial VLMs, clarified their prompt engineering methodology, and included new experiments with DeepCAD models to demonstrate generalization and better accuracy compared to alternatives.

Ultimately, four reviewers leaned positive, while one reviewer (jq8V) remained negative, arguing that “the initial paper itself should have included the more complicated dataset ... a thorough evaluation of the approach on the expanded CAD dataset was missing from the initial submission.” However, the authors addressed this concern during the reviewer-author discussion, as also noted in their final remarks. The AC does not share the reviewer’s position that the paper should be rejected solely based on the initial version while disregarding the rebuttal and author-reviewer discussion.

The AC recommends acceptance, and strongly urges the authors to incorporate the additional experiments, results, and clarifications from the rebuttal and discussion (i.e., minor revisions) into the final version of the paper!
Decision: Accept (poster)