GeoCAD: Local Geometry-Controllable CAD Generation with Large Language Models
Authors: Zhanwei Zhang
kaiyuan liu
Junjie Liu
Wenxiao Wang
Binbin Lin
Liang Xie
Chen Shen
Deng Cai
Venue: NeurIPS 2025 poster ()
Keywords: CAD generation
large language model
Primary area: applications

Abstract: Local geometry-controllable computer-aided design (CAD) generation aims to modify local parts of CAD models automatically, enhancing design efficiency. 
It also ensures that the shapes of newly generated local parts follow user-specific geometric instructions (e.g., an isosceles right triangle or a rectangle with one corner cut off).
However, existing methods encounter challenges in achieving this goal.
Specifically, they either lack the ability to follow textual instructions or are unable to focus on the local parts.
To address this limitation, we introduce GeoCAD, a user-friendly and local geometry-controllable CAD generation method. 
Specifically, we first propose a complementary captioning strategy to generate geometric instructions for local parts.
This strategy involves vertex-based and VLLM-based captioning for systematically annotating simple and complex parts, respectively.
In this way, we caption $\sim$221k different local parts in total.
In the training stage, given a CAD model, we randomly mask a local part.
Then, using its geometric instruction and the remaining parts as input, we prompt large language models (LLMs) to predict the masked part.
During inference, users can specify any local part for modification while adhering to a variety of predefined geometric instructions.
Extensive experiments demonstrate the effectiveness of GeoCAD in generation quality, validity and text-to-CAD consistency.

GeoCAD: Local Geometry-Controllabee CAD Generation with Large Language Models
Zhanwei Zhang'; Kaiyuan Liu', Junjie Liu?, Wenxiao Wang', Binbin Lin
Liang Xie Chen Shen', Deng Cai
1 State Key Lab of CAD&CG, Zhejiang University
A Alibaba Cloud Computing, "Zeejiang University of Technology 4 School of Software Technology, Zhejiang University zhanveizhangozji edu. cn
inresular quadrilaberal
right trapecaid
isosceles truppzoid
parallelogram
rhombus
rectangle
square
quartar-cincle
abtyse Griangle
acutte triangle
right triongle
en isosceles, right triangle
rounded rectangle
shope like letter
quadrilatarol curved side
shape that looks like rectangle
a reckangle with corner Cut off sector one of i6s sides
annular
crescent shape
Figure I: Local ccometry-controllable CAD generation achieved by GeoCAD, The input comprises:
(1) original CAD model (the left side), (2) the local part to be modified (highlighted in blue), and (3) user-specific geometric instructions. Subsequently, GeoCAD outputs the revised CAD models where only the target part is altered while adhering to the provided geometric instructions.
Abstract
Local geometry-controllan computer-aided design (CAD) generation aims modify local parts of CAD models automatically, enhancing design efficiency. It also ensures that the shapes of newly generated local parts follow user-specific geometric instructions (e.g., an isosceles right triangle or a rectangle with one corner cut off). However, existing methods encounter challenges in achieving this goal. Specifically, they either lack the ability to follow textual instructions unable to focus the local parts. To address this limitation, introduce GeoCAD, a user-friendly and local geometry-controllabbe controllable CAD generation method. Specifically, first propose complementary captioning strategy generate geometric instructions for local parts. This strategy involves vertex-based and VLLL--based captioning for systematically annotating simple and complex parts, respectively. In this way, we caption ~221 different local parts in total. In the training stage, given a CAD model, randomly mask a local part. Then, using its geometric instruction and the remaining parts as input, we prompt large language models (LLMs) to predict the masked part. During inference, users can specify any local part for modification while adhering variety of predefined geometric
Internship work Hangzhou YunQi Academy Engincering and Alibaba Cloud Computing. Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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instructions. Extensive experiments demonstrate the effectiveness of GeoCAD in generation quality, validity and teD consistency. Code will be available at https: https::///tthiee gigh....c.. cttpeab..oommiiaaa wtw.:euw..coomaaaaeeee/////////e wtwhaaawweii//Gooaoaaae
1 Introduction
Computer-Aided Design (CAD) pivotal in industrial design. driving innovation and efficiency across diverse domains such as mechamical manufacturing [8] 17] . In CAD tools (such as SolidWorks and AutoCAD), the boeech-extruee-modllin enabling users to control the parametric design process effectively. During this process, users sequentially extrude each 2D sketch into 3D shapes to construct complex solid CAD models, with each sketch comprising one or multiple local loops. Each local loop typically represents pattern or geometric shape, serving as the fundamental closed-path element of a CAD model (50) 48).
(SEM) workflow B5
45
50 48 is commonly employed.
In practice, any minor mistake in local parts (i.e., local loope) of a CAD model can potentially result in significant systemic errors. Thus, after drawing draft CAD model, users generally need to modify its local parts to ensure that the final CAD product meets the expected functional or aesthetic requirements. Compared to manual modifications, if a deep-learning method can automatically adjust the shapes of local parts according to user-defined geometric instructions (e.g., an isosceles right triangle or rectangular shape with one corner removed), would significantly reduce labor costs in CAD product optimization. Moreover, such a method must retain the remaining CAD parts unchanged while ensuring that the newly generated local parts integrate with them without conflict. We refer to these capabilities as local geometry-controllaat CAD generation.
Unfortunately, existing controllable CAD generation methods face challenges in achieving local geometry-control. Specifically, [50] 48
46 23 (57 typically take partial CAD parts or attributes (e.g., incomplete sketches, topological or geometric parameters) as input and automatically generate new CAD models. Yet, they lack the ability to follow textual instructions, which hinders users from expressing their requirements intuitively and conveniently. To resolve this limitation, some text-to- CAD methods based on LLMs or transformers [1 have demonstrated meaningful progress [24] 18 2 [1]. However, these methods are not applicable for local geometry-controe I8 47
47
44 43 [2 typically generate new CAD model from scratch based on textual instructions, making difficult to fully focus on the required local parts. In primarily collect textual descriptions of CAD models from global 3D views rather than local 2D views. These 3D views are generally oblique, which prevents capturing accurate geometric attributes (such as length and angle) of local parts for training. [61] can focus on local parts well but incorporates little geometric constraint, thereby struggling to follow geometric instructions.
generation. Specifically, 24
addition, [88 55 43 44
In this paper, we propose GeoCAD, user-friendly and local ceotllty CAD generation method. As shown in Fig. [ GeoCAD takes the original CAD model, the local parts (highlighted in blue), and eperifieci geometric instructions as inputs. The local parts are then generated by GeoCAD to align with the instructions, and are combined with the remaining parts to create new CAD models. To achieve this objective, the primary challenge is addressing the insufficiency of training data, specifically the geometric instructions for local parts. Given that manual captioning is prohibitively costly and labor-intensive, we introduce complementary captioning strategy. Specifi- cally, we categorize local parts into simple and complex groups based on their internal side types and numbers. Simple parts correspond to common geometric shapes (e.g., triangles with three lines, quadrilaterals with four lines), while complex parts typically represent more intricate visual patterns. For complex parts, we render them as 2D images and then employ advanced vision large language models (VLLMs) I to derive descriptive captions. However, for simple parts, VLLMs fail to achieve accurate meeiiiideed captioning. For example, VLLMs do not reliably distinguish whether quadrilateral is a rhombus based solely on an image. To overcome this limitation, we introduce a nartex-based captioning method for simple parts. This involves extracting vertex coordinates from the original CAD model and then analyzing geometric attributes for accurate classification. For instance, if quadrilateral has four lines of equal length, it is a rhombus; if it contains right angles, is further categorized as a square. Utilizing the complementary strategy, we have successfully captioned approximately 22 different local parts, comprising 116k complex parts and 105k simple
In the following, local parts refer to local loops, the finest- gresiegr closed- path elements of sketch. In this paper, geometric instructions denote textual captions of loop shapes.

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61], during parts. Inspired by the success of LLMs in text-to-CAD generation 47 2 55 training, given a CAD model, we randomly mask local part and prompt LLMs to predict this part using the corresponding geometric instruction and the remaining visible parts as inputs. Once trained, in real-world applications, users can mask any local part for modification based on various geometric instructions. The new local parts generated by GeoCAD are then integrated with the remaining parts of the original CAD model to form new CAD models. Overall, our contributions are:
43
We propose GeoCAD, local geomelry--controlabbe CAD generation method, enabling users to express design intent for specific parts through geometric instructions. To the best of our knowledge, GeoCAD is the first to achieve local geometry- control in the CAD generation field. To achieve this, we propose a complementary captioning pipeline to annotate ~21k distinct local parts for the following two-stage LLM fine-tuning.
Extensive experiments demonstrate that GeoCAD significantly enhances generation quality, validity, and text-to-CAD consistency in local cootetry-contoollable CAD generation.
2 Related Work
CAD Model Generation. Existing CAD generation methods can be categorized into three types:
constructive solid geometry (CSG). boundary representation (B-rep) and sketch-and- and-extrude modeling
(SEM).
CSG constructs CAD models by combining primitives (e.g., cubes or spheres) into a tree [21
6 33
B-rep denotes CAD models as interconnected faces, edges, and vertices 3
7
49
37
Compared to CSG and B-rep, SEM-based methods [45] 50 48 17
55
23
61 57 S1 are
consistent with prevailing CAD tools, allowing users sequentially extrude sketches into 3D shapes,
with each sketch comprising one or multiple loops. Notably, within a sketch, any loop nested inside another loop serves hole. Recently, SEM-based controllable CAD generation has garnered lot
of attention due to its potential to revolutionize the design process [50 48 46 23 57 Specifically, these methods allow for some level of control over the parts or attributes of the original CAD models.
Among them. (50) 46 23 57 achieve sketch-level control, while [48] offers finer-grained control over local loops. Despite these capabilities, these methods struggle to follow textual instructions. limiting users from conveying their design intent in an intuitive and convenient manner.
On the other hand, current text-to- CDD methods that have demonstrated meaningful progress 2 18
43 2 typically generate a new CAD model from the ground up based textual instructions, which limits their ability to precisely target refine specific primarily gather textual descriptions from global 3D perspectives rather than localized 2D views. These 3D perspectives are typically captured in oblique orientations, which limits their ability to precisely quantify critical geometric attributes (e.g., length and angle) of local parts during the training process. (6T] can effectively concentrate on the generation of local parts but fails to follow geometric instructions. To sum up, current studies lack the ability to achieve local geometry generation.
47 55
43 22. Notably, [24 18 47
44
local parts as per user specifications. Moreover, 18 55 43)
Large Language Models (LLMs). Compared to traditional deep-learning based models 62 12 36
60
g
25). LLMs have recently demonstrated remarkable ability follow textual instructions
40
1.54,
51
28 10, (2). Leveraging this capability, L.MMs have shown notable versatility and efficacy 59 (38) Users can employ various textual instructions to
across diverse applications [58 22 52
[3]
direct LLMs in accomplishing diverse tasks like code generation [15] [1I and question answering [39
20 As branch, vision large language models (VLLMs) have also achieved significant success in
vision domains 27
56 26] More recently, both LLMS and VLLMS have shown promise in CAD
generation 47 2 55 44
43
61]. Specifically, 47 55
61 primarily rely on VLLMMs for CAD
generate CAD models from scratch. On the other hand, [2] employs training-fire free manner to generate CAD codes via informative prompts. As mentioned above, these methods either cannot effectively focus on local generation or struggle to follow geometric instructions accurately. Distinguished from them, our GeoCAD excels in local part generation while precisely adhering to geometric instructions.
caption synthesis or fine-tune LLMs or transformers 41
3 Methodology
In this section, we present GeoCAD, a user-friendly and local ceometry-controllable CAD generation method. As shown in Fig. | GeoCAD incorporates three inputs: (1) original CAD model,
3

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Figure 2: The complementary captioning strategy. (a) Vertex-based captioning for simple local parts. Vertex coordinates are initially extracted, followed by geometric analysis to enable precise captions. (b) VLLM-based captioning for complex local parts. We first convert complex parts into 2D images and subsequently employ powerful VLLMs to produce descriptive captions.
represented in hicrarchically textual format proposed by FlexCAD [I]. (2) the local part designated for modification, and (3) geometric instructions specified by the user. GeoCAD then generates new CAD models, altering only the designated local part while closely adhering to the provided instructions. To achieve this, we first propose complementary captioning strategy to generate ~221 geometric instructions for local parts (Sec. (.I). Building on these instructions, we then formulate a two-stage training pipeline to fine-tune LLMS for local CAD generation (Sec. 3.2).
3.1
Complementary Captioning for Local Parts
The main challenge in achieving local geometry-come is tackling the lack of training data, par- ticularly concerning geometric instructions for local parts within 3D CAD models. Since manual captioning is excessively expensive and labor-intensive, we propose a complementary captioning strategy. In the beginning, we collect local parts (i.e., local loops) from the CAD models within the DeepCAD dataset [5]. filtering out duplicates and discarding invalid ones (i.e., those that are not closed loops or involve intersecting line segments). Subsequently, introduced FlexCAD [61] to represent CAD models and their local parts, where each local part denoted as a contiguous string comprising the side type and vertex coordinates, illustrated in Fig. 2 These local parts are then categorized into simple and complex groups based on their internal side numbers and types. Specifically, shown in the lower part of Fig. a simple parts represent common geometric shapes (e.g., triangles with three lines, quadrilaterals with four lines, sectors with two lines and arc), making up roughly 50% of the entire of local parts, while complex parts typically exhibit more intricate visual patterns as shown in the lower part of Fig. bb)
adopt the textual format
As shown in the upper part of Fig. ((b). for complex parts, we transform them into 2D images and then leverage powerful VLLMS 1 4 to obtain their geometric instructions (see the detailed prompts to guide VLLMs in the appendix). However, VLLMs exhibit limitations in fine-grained geometric descriptions for simple parts. For instance, they struggle to reliably discern whether image alone. To address this problem,
quadrilateral is a rhombus according vertex-basse captioning method for simple parts. As shown in the upper part of Fig. ala), we first extract vertex coordinates from the original CAD text representation and then analyze geometric properties to precisely categorize these parts. For instance, given a quadrilateral, we can calculate its side lengths and inter-side angles based its vertex coordinates. If it has four lines of equal length, is a rhombus; if it includes right angles, it further categorized as a square. Moreover, for partial simple parts, we also incorporate key dimensional parameters into the captions (such the radius length of a circle and the side length of a square). In total, we annotate nearly 22 lk distinct local parts, consisting of 116k complex parts and 105k simple parts.
propose
3.2 Fine-tuning LLMs with Geometric Instruction
With the geometric instructions derived in Sec. 3.1 we fine-tune LLMSs to achieve local geometry- controllable CAD generation. The training procedure comprises two stages:

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The prompt template used in stage 1. Local parts are first augmented through translation, scaling, rotation, and reflection. Subsequently, we construct the corresponding prompt that incorpo- rates the geometric instruction, and ask LLMs to predict both the initial and augmented parts.
Figure 3:
Instruction: Below partial description of CAA sequence where command has been replaced with the string "floop mask]':
DeupxDesigg Design
26,31 <curve, end> 31,1 <cuive end> 16ne,36,3 <cuvve end> line.31,44 <curve end> <1oop_ _eene>dd <lace end> <sketch end> add,31,39,3 0,0011111001111110000000000 1,
Proppt
<extrusion end> [loop mask]
<face end> <sketch end>
eextrusion end>
Generate a string that could replace "[loop mask]" in the CA sequence. Notably, the string demotes a semicircle.
Answer: 35,33,,,1,,,7 <curve end> line,27,31 <cuive end> <oop end>
Figure 4: The prompt template used in stage 2. Given a local part (highlighted in blue) in CAD model, we formulate the prompt that integrates the geometric instruction (highlighted in green) and the remaining parts of the CAD model, and require LLMs to predict this local part.
Stage 1: Pre-training for CAD-text Alignment (Optional). As mentioned in Sec. 3.1 we
follow [6] to represent local parts using their internal side types and vertex coordinates. Since such CAD-specific geometric representation is typically absent from the pretraining corpus of LLMs, this stage focuses on aligning the representation of local parts with textual geometric instructions. thereby further enhancing the LLMs' understanding of the CAD-specific representation. Specifically, as illustrated Fig. 3 for each local part, we apply random data augmentation via translation, scaling, rotation, and reflection. Notably, the geometric instructions of augmented samples remain unchanged due to the geometric consistency (e.g., the geometric instructions of the augmented samples in Fig. are all right trapezoids). Subsequently, for the initial and augmented samples, their corresponding instructions and answers are all employed to fine-tune LLMs.
Stage 2: Instruction Fine-Tuning Tuning for Local Geometry- Contrrl. In practice, when modifying specific part of a CAD model, it is crucial to retain the other parts of the CAD model unchanged. Additionally, the newly generated part should integrate with them without any conflicts. To this end, inspired by FlexCAD [6]] each epoch, for given CAD model, we randomly mask local part and design geometric instructions. These instructions are employed to prompt LLMs to predict this masked part autoregressively. However, FlexCAD's training process has one critical limitation: its prompts lack geometric constraints during training. Consequently, once trained, FlexCAD struggles to follow geometric instructions. In light of this, as shown in Fig. 4 our prompts incorporate the geometric instructions constraints when fine-tuning LLMs to generate predictions. As shown in Fig. []] during stages and the cross-entropy (CE) loss between the predicted tokens and the answer
Ignored Tokios
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(a) The Training Process
(b) The Inference Process
The overall framework of GeoCAD. (a) Training process. Given CAD model, randomly mask a local loop within it. During stages and 2, we design the corresponding prompts (as introduced in Fig. 3 and Fig. 4 and fine-tune LLMs. (b) Inference process. Users can optionally mask any local part for modification, driven by various geometric instructions (GI). The mask part is then infilled with the predicted local parts to construct new CAD models.
Figure 5:
5

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tokens is back- prop-propeg to update the trainable parameters of LLMs. Furthermore, we follow FelxCAD [6]] to fine-tune the LLM using LoRA [T6]. which enables partial parameters training while freezing most parameter weights. This strategy allows us to retain the advantages of large-scale pre-trained models while accelerating convergence during optimization.
Inference. In practical applications, users can selectively mask any local part for modification, guided by various geometric instructions (e.g., a square, rounded rectangle, or an annular sector). The mask part is then replaced with the predicted local parts, which are seamlessly integrated with the remaining parts of the original CAD model to form new CAD models, as shown in Fig. ()).
Experiments
4.1 Experimental Setup
Datasets. To maintain consistency with prior research [6]] we evaluate our GeoCAD DeepCAD [5]. a large-scale 3D beetch--xtrude-modeling CAD dataset. This dataset contains 178,238 sketch- extrusion sequences, which are randomly partitioned into training, validation, and test subsets at a 90%-5%-5% ratio. Following established preprocessing protocols from SkexGen [50], we first eliminate duplicate and invalid sequences to ensure data quality. Subsequently, we follow FlexCAD [6]] to convert the remaining CAD sequences into concise textual representations, which can be easily fed into LLMs. Within this dataset, we systematically collect and caption approximately 221k lk distinct local parts, including 1 11kk complex parts and 105k simple parts.
Implementation Details. To ensure fair comparison with FlexCAD [6],, adopt Llama-3-8B 32] as the base L.M,, which achieves competitive performance among open-source L.MMs. We use the same LORA (16) setting as used in [61]. with a rank of 8 and an alpha of 32. In stage we implement translation, scaling, rotation, and reflection for simple parts, while applying only translation and scaling to complex parts avoid semantic inconsistencies in captions. The model is trained on 8 A100 GPUs using Adam W (30] with batch size of 32, a cosine annealing learning rate initialized at 5 10-4, 4 and trained for 10 and 30 epochs across stage 1 and stage 2. During inference, we set the temperature T and Top-p at 0.9 and 0.9 to balance quality and validity in local generation.
Metrics. As this work pioneers local geomeery-ccontrollalee CAD generation, propose a compre- hensive evaluation benchmark based on three key aspects:
1) Generation quality. We adopt metrics from prior work (50 48 61 Specifically, Coverage (COV)
measures the diversity of generated shapes and helps identify whether the model suffers from mode collapse. Minimum Matching Distance (MMD) reports the average minimum distance between real data and the generated set. Jensen-Shannon Divergence (JSD) quantifies the similarity between the distributions of real and generated samples. Together, these metrics measure generation quality on generated CAD models with respect to the test set.
2) Validity. Predicted local parts must form closed loops and must not contain intersecting line segments. In addition, these parts should seamlessly integrate with the existing parts to enable successful rendering into valid 3D shapes, rather than invalid or empty outputs. Following [61] we use Prediction Validity (PV) to quantify the overall validity rate of the generated predictions.
3) Mext-to-CAD consistency. The generated 2D local parts should be consistent with user-defined geometric instructions. To measure this, we propose a vertex-based score ( (Ver-score) for assessing simple parts, and a VLLM-based score (VLLM-score) to evaluate complex parts. Finally, Realism denotes the human evaluation score, manually assessing whether the generated 3D CAD models fully satisfy user requirements for local geometry-control. See details of these metrics in the appendix.
4.2 Performance Comparision with Existing Methods
Baselines. As discussed above, most controllable CAD generation methods are not applicable to the local coometry-contror task. Thus, we compare our GeoCAD with OpenAI-o3 [33]. one of the most powerful closed-source LLMs, and FlexCAD [61), a ofute-of-the-ar baseline for local CAD generation by fine-tuning L.Mss. Without fine-tuning, the output format of the vanilla Open model does not conform to the textual representation defined in 61). making it unable to directly generate local parts. To address this, we improve the performance of OpenAT-o3 with a few-shot learning strategy. Moreover, we manually enhance FlexCAD's performance when generating simple

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Table 1: Performance comparison on the DeepCAD test set. Five-shot denotes that each prompt includes five exemplars selected from the training set that are either identical semantically similar to the target instruction. Exemplars used in OpenAl consist of instructions and answers, following the format shown in Fig.
Best performances are in bold, and the second-bese are marked by *
Model
COVt
MMD1
JSDL
PVT
Ver-scoref
VLLM-scoref
Realism
OpenAI-o3 (five-shot)
53.6%
1.64
1.49
65.7%
33.6%
22.1% 1%
18.7%
FlexCAD
58.3%
1.40
1.58
86.7%
19.8%
6.93%
13.6%
FlexCAD (five-shot)
59.4%
1.37
1.34
88.1%
43.5%
26.8%
20.2%
GeoCAD
64.9%*
1.13
0.98*
90.5%*
76.4%
65.7%*
40.9%*
GeoCAD (five-shot)
66.0%
#1.16
0.80
92.3%
82.2%
68.2%
43.6%
quarter-ciclle
semicircle
shope resembling elangsted oval
trapezoid
right trapezoid
parallelogram
isosceles briangle
right triangle
right triangle
rectangle with ofte conter eut off
rectangle with smaller rectongle cut off
annular sector
rectangular shape
elengated rectangle with rounded
with polyson
'H'
OpenAT-o3 FlexCAD
Ours
OpenAI-o3
FlexCAD
Ours
OpenAT-o3
FlexCAD
Ours
Figure 6: Qualitative comparison results for three methods. On the left, we show the original CAD models (in textual format), with the local parts to be modified (highlighted in blue, the same below). On the right, the upper section presents the user-defined geometric instructions, and the lower section displays the corresponding newly generated CAD models. Both FlexCAD and OpenAI-o3 are enhanced using five-shot learning, Red boxes indicate frequently occurring shapes in the training set (e.g., circles or rectangles) that do not conform to the given geometric instructions. Green boxes highlight local parts that are poorly integrated with the remaining parts of the original CAD models.
parts by adjusting the internal curve types and numbers. For example, when aiming to generate an isosceles trapezoid, we try our best guide FlexCAD to produce a loop composed of four lines.
Quantitative Results. We randomly sampled 1k CAD models from the test set. For each CAD model, local part was randomly masked, and each method was prompted to generate 10 new parts using simple and complex geometric instructions. Here, simple and complex instructions correspond to the generation of simple and complex local parts, respectively. After infilling, this process yielded a total of 10k generated CAD models per method. compute the COV, MMD, and JSD metrics, which rely subset of ground-truth samples, we randomly selected 3k CAD models from the test set and calculated the average results over three separate runs. presented Table [ OpenAl-o3 delivers subpar performance without fine-tuning, even when supported by five-shot

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Figure 7: Additional qualitative results for GeoCAD. On the right, the upper section shows the user-defined instructions, while the lower section presents the newly generated CAD models.
circle with the radius length of (R) units
a square with the side length of [X] units
R 20
R=15
R-10 10
R-5
-
X -5
X 20
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a rectangle with the length of {X} units and the width of (Y) units
-5500,17
X 10, Y-30
20, Y-30
-10
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~100 Y-15
-10 Y-85
Figure 8: GeoCAD capable of precisely controlling the key dimensional parameters. The right side displays the newly generated CAD models and the corresponding geometric instructions.
learning. In comparison, proposed GeoCAD achieves superior results over the state-of-the-art baseline, FlexCAD, particularly in terms Ver-score, VI.LM-score and Realism, with significant improvements of up to 38.7%. 41.4%, and 23.4%, respectively. This is mainly because FlexCAD lacks the ability to align with geometric instructions during the generation of local parts. On the other hand, the few-shot learning ability of LLMs leads performance improvements for both FlexCAD and our GeoCAD. Overall, the results demonstrate the clear advantage of our GeoCAD in generation quality, validity, and text-to-CAD consistency.
Qualitative Results. To intuitively demonstrate performance, we randomly selected six CAD models from the test set. As shown in Fig. 6 the results clearly highlight that our GeoCAD significantly improves controllability and text-to-CAD consistency compared to existing baseline methods. In particular, GeoCAD is able to modify local parts in way that closely adheres to user-defined geometric instructions. In contrast, FlcxCAD struggles to comply with such instructions and frequently generates overly common shapes, such circles rectangles (see green boxes in Fig. 6). Moreover, as shown in the red boxes Fig. 6 both OpenAl-o3 and FlexCAD often produce local parts that fail align properly with the remaining parts of the original CAD models, resulting in outputs that are functionally or aesthetically implausible. These visualizations further validate the superior local controllability and effectiveness of our proposed GeoCAD.
Furthermore, we provide additional qualitative results generated by GeoCAD. As illustrated Fig. 7 given a CAD model, GeoCAD is capable of effectively modifying any target loop within it to form simple or complex patterns. Moreover, for certain simple parts, we incorporate specific dimensional constraints into the instructions, such the radius of a circle, the side length of square, and the

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rounded-
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rectangular shape corner rethoved
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Figure 9: Generalization ability of GeoCAD. On the right, each row contains two groups, with each group comprising three examples generated based semantically similar instructions.
Table 2:
Effectiveness analysis of the complementary captioning strategy and pre-training. Only Vertex-based Captioning and Only VLLM-based Captioning indicate that local parts are described using only vertex-based or VLLM-based captioning, respectively. w w/o stage 1 means that stage skipped, i.e., no pre-training is conducted for aligning CAD data with textual descriptions. w/o data augmentation represents that only the original samples used during pre-training, with- out any augmented data. Best performances are in bold.
Model
COVt
MMD
JSD
PVT
Ver-scoret
VLLM-score
Only Vertex-based Captioning
63.6%
1.18
1.02
89.5%
78.3%
Only VLLM-based Captioning
61.8%
1.26
1.05
89.1%
64.2%
w/o stage I
61.3%
1.21
1.16
89.6%
71.5%
60.4%
w/o data augmentation
62.9%
1.18
1.09
88.5%
73.2%
61.8%
Ours
64.9%
1.13
0.98
90.5%
76.4%
65.7% %
length and width of a rectangle. As shown in Fig. GeoCAD not only accurately generates the desired shapes but also adheres closely the specified dimensional parameters. On the other hand, as shown in Fig. | GeoCAD demonstrates robust generalization capabilities in accurately understanding and executing semantically similar instructions, even when some of these instructions (e.g., narrow, rounded-end rectangle and right-angled triangle) never appeared in the training data.
4.3 Ablation Studies
conduct a series of ablation studies under the same experimental settings described in Table
Effectiveness the complementary captioning strategy. As shown Table 2 using only vertex- based V-...- captioning fails generate complex parts (e.g., letter V) simple parts (e.g., a trapezoid), thereby failing to obtain the corresponding Ver-score and VLLM-score. In contrast, the complementary captioning integrating both of them leads to improved performance.
Effectiveness of Pre-training. As depicted in Table 2 omitting stage results in the poorest perfor- mance, demonstrating that pre-training is essential for achieving preliminary text-CAD alignment. Additionally, excluding data augmentation during pre- pre-training leads to a performance decline, indi- cating that diverse augmented samples enhance GeoCAD 's alignment capability. Together, these findings confirm the effectiveness of the pre-training process.
Conclusion
In this paper, we introduce a local geotetty-conttollaalee CAD generation method, GeoCAD, enabling users specify design intent for specific parts through geometric instructions. To the best of our

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knowledge, GeoCAD is the first to achieve local geometry- in the CAD generation field. To accomplish this, GeoCAD introduces both vertex-based and VLLM-based captioning pipelines and employs a two-stage training strategy for L.LM fine-tuning. Extensive qualitative and quantitative evaluations demonstrate that GeoCAD substantially improves generation quality, validity, and text-to- CAD consistency in local geometry-contoolable CAD generation.
Acknowledgement
This work supported in part by the Key R&D Program of Zhejiang Province (2025C01212). in part by Yongjiang Talent Introduction Programme (2022A-240-G), in part by Ningbo Key R&D Program (2023Z229), in part by The National Nature Science Foundation of China (Grant NOs: 62273303, 62303406, 62273302, 62036009), in part by Ningbo Key R&D Program (NO.: 20252055).
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Appendix
Due to space limitations in the main paper, we provide additional results and discussions in this appendix, organized follows:
Sec.
More Details about VLLM-based Captioning.
Sec.
Detailed Comparison with Existing Work.
Sec.
Detailed Categories of Simple Parts and Complex Parts.
Sec.
Details about Metrics for Evaluating Text-to-CAD consistency.
Sec.
LLMs of Different Scales.
Sec.
Sensitivity Analysis of Key
in Sampling.
Sec.
Criteria for Dataset Selection.
Sec.
Sketch-level Editing.
Sec. 1
Failure Cases, Limitations and Future Work.
Sec. J
Five-shot Prompt Example.
Sec.
Additional Qualitative Results.
A
More Details about VLLM-based Captioning
The prompt used for VLLM-based captioning is follows:
"Given a loop in a CAD sketch, provide a brief description of its geometric shape starting with 'a' 'an' if identifiable; otherwise, state 'None'..
Using this prompt. we randomly caption 1k complex local parts with GPT-4o [ and Qwen2.5-VL. 12B-Instruct [4) respectively. Regardless of whether these models output specific shape or 'None', we manually evaluate each result by judging its correctness either "Yes" or "No". The overall captioning accuracy across these 1,000 parts is 91.3% for Owen2.5-VLL-22B-Im VL--2LB-------- and 86.5% for GPT-40.. These results indicate that Qwen2.5- VLL--V -L_-ru--nnttruc Instruct outperforms GPT-4o in this captioning task, which is consistent with with the latest multimodal model leaderboard rankings Furthermore, given the lower cost of Qwen2.5-VL-72B- Instruct, we use caption the remaining complex parts.
B Detailed Comparison with Existing Work
As mentioned in lines 36-48 in our main paper, existing work struggles achieve local geometry- controllable CAD generation. Here, we further highlight the differences between CAD-Editor [55), FlexCAD [61] and our GeoCAD. CAD-Editor has difficulty focusing local generation for two main reasons: 1) It may unintentionally modify the remaining parts, resulting in outputs that do not align with user requirements (as illustrated in the last example of Fig. 1 in the original paper). 2) CAD-Editor fails to accurately obtain angle and length information, making it incapable of generating even simple parts, such right triangle, let alone isosceles right triangle, as mentioned in line 45 of our main paper. FlexCAD, on the other hand, can focus on local parts but incorporates minimal geometric constraints, thereby struggling follow geometric instructions. In particular, FlexCAD is unable to understand, let alone follow, simple or complex geometric instructions. This limitation is clearly demonstrated in Fig. of our main paper.
C Detailed Categories of Simple Parts and Complex Parts
The categories of simple parts include acute triangle, right triangle, obtuse triangle, isosceles triangle, isosceles right triangle (Notably, equilateral triangles do not occur in the DeepCAD (45) dataset), quadrilateral, trapezoid, isosceles trapezoid, kite (Two pairs of adjacent sides equal), parallelogram, rectangle, rhombus, square, circle, semicirele, quarter-circle, three-quarter circle, major-are loop (defined as containing an are longer than a semicircle), minor-arc loop (defined as containing an are shorter than a semicircle), and so on. The remaining local parts are classified as complex, exhibiting more intricate and diverse visual patterns.
21

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Table 3: Ablation studies on fine-tuning LLMs with different scales. Llama-3-8B is the model used in our main paper enable fair comparison with FlexCAD [6]]. Transformer-4M small Transformer-basee
42 language model, with a total number of trainable parameters comparable to that of our model in the main paper when using LoRA. L-ama-3-8B--Fll denotes full-parameter fine- tuning. Llama-3-8B, Swen2.5-3B-Instruee and Qwen2.5-77
are all fine-tuned using LoRA. The best results are shown in bold, and second-best results are marked with *,
Model
COvt
MMD1
JSDL
PV1
Ver-scoref
VLLM-score
Transformer-4M
59.1%
1.32
1.26
85.5%
69.3%
51.2%
Llama-3-8B-Full
67.5%*
1.02*
1.06
89.7%
78.9%*
64.2%
Llama-3-8B
64.9%
1.13
0.98*
90.5% %
76.4%
65.7%*
Qwen2.5-3B-Instruue
65.8%
1.01
1.10
87.4%
74.2%
64.9%
Qwen2.5-7B-Instee
68.7%
1.05
0.86
90.1%%*
79.8%
70.2%
D Details about Metrics for Evaluating Text-to-CAD consistency
As mentioned in lines 213-217 of our main paper, we employ Ver-score, VL..M-score, and Realism comprehensively evaluate model performance in terms of text-to-CAD consistency. Specifically, to compute Ver-score, we extract vertex coordinates from the generated local parts and analyze their geometric attributes to determine whether they align with the given geometric instructions. To obtain VLLM-score, we first render the local parts into images and then prompt two of the most powerful VLLMs, GPT-4o I and Qwen2.5 images match the corresponding instructions, assigning a binary label: "Yes" or "No." We report the average of their scores in Table of our main paper, where both models significantly outperform the baselines. To evaluate Realism, we randomly render 500 newly generated CAD models into images, with the modified local parts clearly marked. Five crowd workers are then asked to assess whether the generated local parts align with the geometric instructions and do not conflict with the remaining parts. If both criteria are satisfied, they assign binary label: "Yes"; otherwise, "No." The average score from these workers is reported in Table I of our main paper.
[4. to judge whether the rendered
E LLMs of Different Scales
As shown in Table 3] Transformer-4N, achieves the lowest performance, confirming that LLMs play key role in enhancing local CAD generation. Llama-3-SB-Ful performs comparably to Llama-3-8B, demonstrating the effectiveness the LoRA strategy [16]. As two of the most popular open-source LLMs, Oween2.5-7B-Inntrrun slightly outperforms Llama-3-8B.
F Sensitivity Analysis of Key Hyper-parameters in Sampling
Table 4: Effectiveness analysis of key hyper- parameters, including the sampling temperature r and Top-p. Best performances are in bold and the second-best, are marked by *
Model
COVT
MMDJ
JSD,
PVt
Ver-score"
VLLM-score""
7= 0.7
63.4%
1.18
1.03
91.2%
75.9%
63.2%
= 0.9
64.9%*
1.13
0.98*
90.5%
76.4%*
65.7% %
1.1
65.6%
1.16*
0.95
89.1%
77.5%
65.1%*
Top-p 0.8
64.1%
1.21
1.09
91.0%
75.3%
64.4%
Top-p 0.9
64.9% *
1.13
0.98*
90.5%
7
65.7%
Top-p 1.0
65.2%
1.18*
0.92
88.3%
76.9%
66.8 %
As shown Table we conduct sensitivity analysis on key hyperparameters, including the sampling temperature and Top-p. All other experimental settings follow those described in Section 4.2 of our main paper. In general, increasing 7 or Top-p results more diverse and stochastic predictions. However, this comes at the cost of reduced PV, while other metrics tend to improve, consistent with
22

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findings in [61. In our experiments, we balance this trade-off by selecting r and Top-p values that ensure the PV remains above 90%.
G Criteria for Dataset Selection
DeepCAD [45 is a suitable dataset for evaluation, and the reasons are detailed below: 1) Scale: Deep- CAD is large-scale 3D CAD dataset, comprising over 178k samples. Relevance Controllability: Compared to 2D sketch datasets, DeepCAD better reflects the requirements of controllable generation, as aligning local parts within 3D CAD models is both more challenging and more practical. 3) Design Process Alignment: In contrast to other 3D CAD datasets, such as the ABC dataset [19], DeepCAD in- cludes
sequences that closely mirror the design workflows of commercial CAD tools like SolidWorks and AutoCAD. 4) Community Adoption: Due to its characteristics, DeepCAD also the only choice for prior studies, including SkexGen [50]. HNC-CAD 488
CAD-Editor [55
CADFusion [44], Tex12CAD [18), and FlexCAD [6]].
symmebyy wis
symmety wiss
ractangle
reflection
Figure A1: An example of sketch-level editing.
H
Sketch-level Editing
For sketch-level editing, if a sketch contains multiple loops, ideally, we would like to learn the inter-loop constraints (c.g., symmetry, patterns, etc.) that define the overall structure. However, as mentioned above, DeepCAD is currently the only dataset suitable for controllable 3D CAD generation, and unfortunately, such inter-loop constraint annotations are not provided in the dataset. Fortunately, without supervision from these constraints, sketch-level editing is still achievable based loop-level editing capability. This is because the loop serves as the fundamental element of sketch. For example, shown in Fig. Al if user selects sketch consisting of two symmetric loops and wishes to replace them with another pair of symmetric loops, the following automatic process can be performed: 1) Estimate the center point of each original loop by averaging its coordinate points, which are extracted using string matching. 2) Determine the symmetry axis based on the two center points. 3) Generate
local loop through GeoCAD replacing one of the orignal loops. 4) Reflect the newly generated loop across the symmetry axis to produce the second symmetric loop, thereby replacing both original loops.
I Failure Cases, Limitations and Future Work
pooiiiouuee wit tie. nemiivele connected to cter wite alcended arme by berrow vitamtu
qurteridiiis
semoick connected to
Figure A2: Failure cases. The generated local parts align well with the user's geometric instructions but do not integrate smoothly with the remaining parts of the original CAD model.
Failure cases. Despite notable advancements, our GeoCAD sometimes results in failure cases. As shown in Fig. A2 given CAD model, when only the special part is modified (i.e., the part upon which the remaining parts are constructed and strictly aligned in size), the unchanged remaining parts may lead to structural conflicts with it. To mitigate this issue, when modifying the special parts, users should provide geometric instructions that account for the constraints imposed by the remaining parts, since the DeepCAD dataset does annotate the relationships between different parts.
Limitations and future work. this paper, we fine-tune L.LMs to enable local geometry-controllan CAD generation, primarily guided by textual instructions. However, in practice, certain complex local
23

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parts may be difficult or even impossible to describe using text alone. Thus, in the future, if users can complement textual inputs with hand-drawn images for local geometry-controollbbe CAD generation. they may be able to convey their design intent more effectively. Given the strong capabilities of VLLMM in both CAD generation and understanding, our future work aims to develop a more advanced multimodal LLM tailored for controllable CAD generation from both text and image inputs.
J Five-shot Prompt Example
To better illustrate the implementation details the baselines and our GeoCAD in Table of our main paper, present five-shot prompt example, as shown in Fig. A3
K
Additional Qualitative Results
We provide additional qualitative results in Fig. A4
24

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Y You answer questions about controllable CAD generation. When answering user questions, please follow these examples:
Example
Instruction:
Below a pertial description of a CAD sequence where one command has been replaced with the string "(loop mask]";
line, 0,26 <curve end> line, 1,26 sourve, end> line, 1.28 <rurve end> line,0,28 <curve end> <oop end> <face, und> floop mask] <fare, end> <sketthh end> add,31,45, 45,31,31,33 ,1, 1,000,,,,,001,,,,,,,,,,, ,36 sextrusion end>
Generate an string that could replace "floop mask]" in the CAD sequence. Notably, the string denotes an isosceles night trangge..
A nswee::
line, line,0,28 0,28 <curve end> line, 1,29 ecurve end> 1,28, <ruvve end> <loop, end>
Example
Instruction:
Below partial description of CAD sequence where one command bas been replaced with the string 'looop mask]":
line, 42,47 epurve und> 43,46 <turve und> 43,47 souvve end> cloop und> <face end> linw, 43,15 scuvee end> line <curve end> 11,1,44,16 scurve, end> <loop end> <face, end> (loop mask] <lace end> <sketch end> add,28,34,31 38,33,,,,,,,,,,,,,,,,3 31,00,3,, 1,0,30,46,31 <uxtrusion und>
Generate an string that could replace *Dooop mask]' in the CAD sequence. Notably, the string denotes an lsosceles right triangle Answer:
line, 43,47 <ourve end> 44,66 <rurve end> line,44,1) ecurve, end> <loop, end>
Example
Instruction:
Below is partial description of a CAD sequence where one command has been replaced with the string "looo mask]":
line, 14,57 <curve end> line, 16,55 cruvve, end> line,16,10 16,10 <cuvve end> 2i,4 <curve ends line 47,4 <curve end> line 48,8 <cuvee end> 4i1,, <curve end 17,e,47,5 scurve end> 21ne <rurve, end> line, 16, <qurve, end> line, 16,55 <rurve end> line, 14,58 scurve end> <loop. endo dlace ends <sketch end> add,31,63, 6,,,3,,3,33,,,,,,,,,,,,,,33,,,,3 3,,,,,,,,,,,,,,,,,,,, 3,,,,,0,,,00011000 1,0,,,,,,,,,,,3,, 45 <extrusion end> l6ne,26,0 <curve end 29,0 <ouve, end> line, 36,7 ecurve eod> line, 36,62 couvve eod> lin8,36,62 ecurve end> line, 36,7 <ouve, end> line 29,0 <curve end> line 26,0 <ruvee end> <loop. ends <face end> line, 26,0 <cuvee end> 29,0, <curve end> line,36, <ouve end> line,36,62 62,62 <ouve end> line, 26,00 62 <cuvve and> <loop end> <aace eod> [loup mask] <lace end> <sketch end> cut,27,31, 21,31, 31,31,,,,,,,,,, 31,,1,,5,- -5,55,,100000 1,000,00000011000000 0,000,1.1,,0000 1,111,,,16,,,111 <axtrusion, end> G Generate an string that could replace "Doop mask]' in the CAD sequence. Notably, the string decotes isosceles night manngl..
A aswer:
2i,0,29,0 <curve end> 16,0,36,0 ecurve ends end>line,,36, 36, <rurve end> <loop end>
Example
Instruction:
Below is partial description of CAD sequence where one command has been replaced with the string "(loop mask]':
[loop mask] <face eod> <sketob end> add, 31, 62,66,.33 31,,1,,1,,1,,,,0,,00. 1,000.1000-100.,00 1,0,6,,,,,6,,1,,44 <extusion end> line, <curve end> l,31, <curve end> 22,48 <curve end> line 40,48 <curve end> iine,5,,31 <curve end> line 57,14 scurve end> line, 31,40 <curve end> <xxtrusion, end> Generate an string that could replace "loopp mask]' in the CAD sequenes. Notably, the string denoles an isusvales right triangle. A nnswer:
<lop end> <face end> <aketch end> add, 31,3,,,,,1,,1,1111 1,110000000010110110,00,,,00
31,1,31,17 <ourve und> 19ne,59,17 <curve und> 31,4,31,45 sourve und> cloop end>
Example 5
Instruction:
Balow partial description of CAD sequeace where one command bas been replaced with the string 'looo mask]':
6,ne,612 scurve and> 6,50 scave end>ling 43,50 scuvve eod> 56,3, scuve and> 56,33 scuive und> 32,e,32,3 scurve end> line, 32,29 sourve and> line,56,20 ccuvee end> line,66,26 26 scuve end> line 43,12 cuuve end> <loop and> <face end> 4ine443.12
<curve end> line,56,1 <curve end> 56,266,26 <cuive end> <loop end> <face end> 43,50 quuvee and> 56n3,56,3 ecuiee end> line, 56,50 spurve end> doop ends dace end> <sketch end>
3,,3,,,,,31,,,,,,,,, 1,000,0,,,,,,,11,,,,,0 1,4,0,,,44,44,33 <extrusion end> [loop mask) <ace end> <sketch end> 1,27, 45.34 cextrusion end> line.22,1 <curve end> 40, eruve end> line 40,61 <nuve end> line 22,61
<lace end> 20,6) <curve end> line, 42,39 <purve end> 42,61 <curve end> <loop 31,,31,57,31.3 3,,,0,1,0,0,,0,,000 <curve end> <loop end> <lace end> esketth end> add,31 11,30,33,,,,,1,,00 1,0,0,,1, 1,0,0,0 1,233,311 cextrssion end> (ine,0,55 <curve end> 6,37 <zuive end> lime.56,37 eruive ends line 62,25 <ruive end> <lopp end> <face end> <aketch end> 5, 1,3,1,1,11,33,4 11,33,4,,, 1,1.0,1,,,000,111111111, 19,,0,19,31,34 eextrusion end>
Generate an string that could replace "lloop mask]' in the CAD sequence. Notably, the string decotes an isosceles right maangle. Aswer:
20),, <gurve end> line, 1 <nurve end>>ine, 42,2, cruive end> doop end>
Instruction:
Below is partial description of a CAD sequence where one command has been replaced with the string 'lloop mask]':
4,20,4,20 <cuive end> line, 22,14 <cuive end> line, 47,14 <ruive end> line 58,25 <cuive end> line, 47,25 <cuive end> line 22,25 <cuive end> line, 4,25 <cuvve end> <loop. end> <face end> line, <nuve end- line 22,37 <ruve end> line 22.48 <curve end> line 4,42 <cuve end> <loop and> <face end> line 37 scurve end> line,47,37 <curve end> line <curve end> 22,48 <curve end> <toup ends <face end> arc 47,25,11,1
eourve ead> 53,3, <cuvve end> 58,,,8,33. 31 ecuvve end> line, 58,25 <curve end> doop end> <aace und> 47,,77,,,5,,55 scurve, ond> 53,31 scurve end> 1ine,56311 56,31 scurve ond> line 58,37 scurve end> <log and> <foce und> (loop mask] <face, end> <sketch end> 29,,,29,3333,313,3 1,,0,,, 1,0,0.0,1.1... 1,32,18,31 4extrusion end> Generate an string that could replace "loop musk]' in the CAD sequence. Nolably, the string demoles an isosceles right triangle.
Answer:
Figure A3: A five-shot prompt example used in Table of our main paper.
25

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ellipse with axtersion both the and bottom
ractangle missing corner
broken ring
polyson tket looks shopp
shope that like water drapled
majorrrr connected rectangle
skope thet semicirsular ring
rectorple wreh and four notches
recbonale widh at the top
receansle wibi at the top
heart-shoped
shape formed by connected circles
satticircle rectongle below ié
rounded reckanale top and bottom
quarder circle rectangle
en itregalor polegor, the top
crass-shaped
recbargle witk et the boskom
rectanple witk at the botsom
irregular polygon
recbangle with and Four notches
racbample sutnicircle
crase- shaped
letter
letter "P
letter
letter 4
letter V
letter
letier
'
ledber
letber
x
letber
letter 'V
lebber V
ledber
letter x
letber '
letter A
witk sides
with rides
elongoted rectangle
rhombus
isosceles trapezoid
right trapezald
quadrilbberl
rhombus
cirsle
Figure A4: Additional Qualitative Results.
26


Reviews:
[{"review_id": "DAnSJeUc9v", "reviewer": "Reviewer_td3y", "summary": "This paper proposes a method for performing local edits in parametric CAD models represented as a sequence of sketch-extrude commands. The method works by converting the CAD model commands into a sequence of text-based tokens resembling the xml format. This format is adopted from FlexCAD and importantly captures hierarchical geometric information in the sketch such as curves, loops and faces. Based on this format, a masking strategy is introduced on the loop-level and an LLM is fine tuned to predict the masked commands representing the loop, while also being conditioned by an input text prompt. The text prompt is a description of the loop to be infilled by the LLM. For training, these prompts are obtained by a combination of vLLM labeling + heuristics. This strategy allows local edits to be performed on the sketches while ensuring that the rest of the CAD model is updated in a consistent manner. Results demonstrated on the DeepCAD dataset show that the proposed method outperforms FlexCAD and OpenAI’s o3 model in instruction following and validity of the synthesized edits.", "questions": "- Can the authors demonstrate examples where multiple loops or faces are masked and edits that are more complex & higher level are performed? This is more in line with what designers expect from a text based editing system. Working with individual loops feels too low-level (it’s fine to train the model this way though) and demonstrating this capability can significantly improve the practical implications of this work.\n- Can the authors demonstrate if design intent is preserved when editing the shape? A simple example would be (but please don’t be limited to this) to gradually reduce the dimensions of the outer loop and evaluate if the inner loops are updated sensibly without self intersections or symmetries breaking. A crucial requirement for editing workflows is to preserve design intent, and if the method can be demonstrated to do this, then the potential of this approach to work with higher level semantic edits in future will be very clear.", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n- the paper is well written and the critical design choices of masking loops, finetuning the LLM with LoRA, generating captions using vision language models, are all very sensible. Ablation studies evaluate the critical design choices and demonstrate their significance.\n- the demonstrated results are strong and show excellent instruction following by the proposed model, while preserving the design intent in the rest of the CAD model.\n\nWeaknesses:\n- Novelty: the proposed approach closely follows FlexCAD with some small but notable improvement in the training strategy. Consequently, technical novelty is limited, but results are better particularly in terms of the instruction following. \n- Practicality: the captions and loop-level edits used in the paper are quite simple. Loop level edits are quite low-level for CAD designers to work with, and can be quickly done manually. Sketch level editing would have been a good use case to demonstrate, but it appears that this wasn’t explored. It is also important to capture and measure if design intent is preserved, for example if there are two symmetric circles in a face, and one circle edited, does the other one get updated automatically? Having to manually constrain the sketch and/or apply dimensions to each loop is quite tedious. Similarly, more complex shapes loop level edits aren’t shown in the paper. The captions are fairly low level geometric descriptions. To give an example, changing the number of teeth in a gear, or reducing the dimension of an outer loop which automatically updates the inner loop geometries while preserving design intent (symmetries, patterns etc.) would be more practical for designers. It feels like these are within grasp with this method, but weren’t explored in the paper.", "was_revised": true, "final_justification": "The authors have addressed some of concerns in the rebuttal and accepted to add visualizations of additional results. Loop level editing using very specific geometric text prompts is still not something that a CAD designer would want and many of the edits demonstrated in the paper can be performed easily in CAD software. But I understand there is a dataset problem here, and the additional results will improve the practical usability of this work to some extent. Consequently, I am increasing my score to Borderline Accept.", "rebuttal": "**Q1: Novelty: Small but notable improvement in the training strategy. Technical novelty is limited.**  \nA1: We appreciate the reviewer’s recognition of our method’s notable improvements and better results.\nActually, compared to FlexCAD, our paper presents novelty in both the task and technical aspects.\n1) Task Novelty.\nWe propose and solve a new task (i.e., local geometry-controllable CAD generation, see lines 27–35 in our main paper).\n**All reviewers recognized the value of our proposed task (Reviewer td3y: very sensible, Reviewer 6Hfr: more impactful than generating from scratch, Reviewer ZjoJ: novel and interesting, Reviewer HFw7: reasonable).**\nTo support this task, we also collected a dedicated dataset of local parts and their corresponding captions, which Reviewer 6Hfr described as a sizeable and solid contribution.\n2) Technical Novelty.\nIn contrast to the one-stage training strategy used in FlexCAD, we propose a two-stage training pipeline.\nIn Stage 1, we introduce a data augmentation strategy tailored for local CAD generation, which was recognized by Reviewer HFw7 as both novel and effective.  \nIn Stage 2, we intentionally retain the standard training strategy with minimal modifications from FlexCAD.\nThe reasons are as follows:   \ni) To demonstrate that our method is simple yet effective. Without relying on elaborate training strategies, GeoCAD significantly outperforms FlexCAD and substantially enhances geometric instruction-following capabilities.  \nii) To facilitate the development of an omni CAD LLM. A standard and unified training strategy allows for better integration between different models, such as FlexCAD and our GeoCAD, bringing us closer to a general-purpose, omni-capable CAD foundation model.\n\n\n\n\n**Q2: Practicality: Loop-level edits are simple for CAD designers, and can be quickly done manually. More complex loop-level edits aren’t shown in the paper.**   \nA2: In fact, as shown in **Fig. 7** in our main paper and **Fig. A3** in the supplementary material, our GeoCAD is capable of generating not only simple CAD shapes (e.g., an isosceles trapezoid) bot also complex shapes (e.g., different letters or irregular geometric shapes).\nCompared to manual editing, our GeoCAD have two major advantages:\n1) User-friendliness for non-experts. Users do not require any specialized CAD drawing knowledge. They only need to select the part they wish to modify and input geometric instructions. \nIn contrast, manual editing in traditional CAD systems typically require significant domain expertise.\nFor example, to manually create an isosceles trapezoid, a user would need to draw a quadrilateral, ensure that two sides are parallel but of unequal length, the other two are of equal length, and align the shape with the remaining parts of the CAD model.\nSimilarly, manually designing different letter shapes requires meticulous crafting of loops to match specific shape requirements.\n2) Efficiency in generation time. When tested on an A100 GPU, GeoCAD generates a valid isosceles trapezoid and a letter shape in an average of 0.43s and 0.96s, respectively, significantly faster than manual editing.  \n\nOn another note, as stated in FlexCAD, DeepCAD is currently the only dataset suitable for controllable 3D CAD generation.\nHowever, this dataset contains few gears, which limits GeoCAD’s ability to modify gear features such as the number of teeth.\nIn the future, this limitation can be solved by collecting more gear-related data.\nDespite this limitation, our model can still easily handle other common complex shapes such as “an ellipse with rectangular extensions at both the top and bottom” and “a heart-shaped loop”, as shown in Fig. A3 in the supplementary material.\n\n**Q3: Sketch level editing should be demonstrated while preserving the design intent.**   \nA3: We are glad the reviewer also notice this point. \nFor sketch-level editing, if a sketch contains multiple loops, ideally, we would like to learn the inter-loop constraints (e.g., symmetry, patterns, etc.) that define the overall structure.\nHowever, as mentioned above, DeepCAD is currently the only dataset suitable for controllable 3D CAD generation, and unfortunately, such inter-loop constraint annotations are not provided in the dataset.\nFortunately, even without supervision from these constraints, **sketch-level editing is still achievable based on our loop-level editing capability.**\nThis is because the loop serves as the fundamental element of a sketch. For example, if a user selects a sketch consisting of two symmetric trapezoids and \nwishes to replace them with two symmetric letter \"E\" shapes, \nthe following automatic process can be performed:\n1) Estimate the center point of each trapezoid by averaging its coordinate points, which are extracted using string matching.\n2) Determine the symmetry axis based on the two center points.\n3) Generate a local letter \"E\" through GeoCAD replacing one of two trapezoids.\n4) Reflect the generated \"E\" across the symmetry axis to produce the second symmetric letter \"E\", thereby replacing both original trapezoids.  \n\nThus, during the sketch-level editing process, it is unnecessary for the model to generate the entire sketch; generating the fundamental loop alone suffices.\nSubsequently, the remaining loops within the same sketch can be derived from the fundamental loop or existing loops through scaling, rotation, translation, reflection or pattern operations.\nMore specifically, i) for pattern-based sketch, it can be achieved by providing the pattern centers and the fundamental loops generated by GeoCAD.\nii) in the case of sketch consisting of an outer loop and an inner loop, the inner loop does not need to be regenerated by the model when the outer loop is modified by GeoCAD. \nInstead, the inner loop can be easily updated through translation, scaling, or rotation operations to preserve the original positional relationships between the two loops.\n\n\n\n**Q4: Demonstrate examples where multiple loops or faces are masked.**  \nA4: Our GeoCAD exhibits the generalization ability to generate multiple loops or faces when they are masked simultaneously. \nSpecifically, we consider two cases:\n1) Independent Loops. If the target loops are not constrained relative to each other, we generate them iteratively during inference. \nIn each step, a single loop is generated based on a corresponding geometric instruction, \nand the resulting loop text is used to infill the masked region. \nThis strategy ensures that subsequent loops are generated in a way that is consistent with previously generated ones. \nThis process is also user-friendly, as it only requires the user to specify the masked region and desired geometric instructions.\n2) Constrained Loops. If the loops are interrelated by design constraints (e.g., symmetry, patterns), \nGeoCAD supports complex structure generation by leveraging its loop-level editing capability, as discussed in A3.\n\nSimilarly, as faces are composed of one or more loops, the same strategy can be applied during the masking and generation of multiple faces.  \nAccording to the official NeurIPS policy, we cannot provide visualization results during rebuttal. \nWe will add them in the revised paper. Thanks!\n\n\n**Q5:  Demonstrate if design intent is preserved when editing the shape**   \nA5: As mentioned in A3, our GeoCAD has the ability to preserve the design intent (e.g., symmetry, patterns) solely based on loop-level editing.\nWe argue that the loop serves as the fundamental element of a sketch. \nWhen generating a sketch consisting of constrained Loops, it is unnecessary for the model to generate the entire sketch; generating the fundamental loop alone suffices.\nThus, in a sketch consisting of an outer loop and an inner loop, the inner loop does not need to be regenerated by the model when the dimension of the outer loop is reduced by GeoCAD. \nTo preserve the existing positional relationships between the two loops, the inner loop can be easily updated through translation, scaling, or rotation operations.  \nAccording to the official NeurIPS policy, we cannot provide visualization results during rebuttal. \nWe will add them in the revised paper. Thanks!"}, {"review_id": "vWW1zzkTQb", "reviewer": "Reviewer_6Hfr", "summary": "They tackle the problem of \"geometry-controllable CAD\" generation, swhich aims to modify specific parts of CAD models, instead of generating everything from scratch.\n\n  They introduce a vertex-basesd and VLLM-based captioning system for annotating simple and complex parts.\n  They use this appraoch to create a dataset of annotations of local parts, used ot train an LLM.\n\n  The method takes in 3 inputs: FlexCAD-style CAD model to be edited, the Local part designated for modification, and geometric instructions by the user. It ouptuts a new CAD model.", "questions": "Figure 8 shows the ability for the model to understand measurements. Is this from the LLM base understanding from Llama pretraining, or were there captions in your dataset that contained dimensional geometric instructions?\n\n  On average, how many CAD instructions compose the shapes? What is the maximum number of CAD instructions that the base LLM (llama-3-8b) able to handle (both theoretically and in practice)?\n\n  Is the model capable of doing mutliple rounds of consecutive edits, iteratively? Especially when the second instruction depends on the successful execution of the first instruction?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n\n  + The problem of being able to *edit* CAD in a locally controllable way is perhaps more impactful than the ability to *generate* from scratch, given the amount of time engineers spend editing their designs.\n\n  + Substantial work has gone into generating the captioning of local parts, and a sizeable dataset is produced. Both sound like solid contributions to the field.\n\n  + Evaluation metrics seems to be comprehensive. \n\nWeaknesses:\n\n  + The captioning method and geometric processing seem to rely on the need for the operations to compose mostly of curves + extrusions, which limits the space of shapes that this can be applied onto. Have you thought about how such methods can be used for more general, complex shapes?\n\n  + The language captions broadly describe the category of geometric shape, but it's unclear how this method would fare with more specific language instructions, like \"a rectangle cutout that's 45 degrees to the nearest outer wall of the shape\"", "was_revised": true, "final_justification": "I maintain my positive rating of the paper -- the  rebuttals have addressed my primary concerns.", "rebuttal": "**Q1:  mostly of curves + extrusions limit the space of shapes. How such methods can be used for more general, complex shapes?**   \nA1: We appreciate the reviewer’s concern regarding the current scope of our method. We would like to clarify as follows:\n1) Our work focuses on common and practical CAD primitives, specifically curves and extrusions. \nWhile these primitives are conceptually simple, they are sufficiently expressive to generate a wide range of shapes.\nMeanwhile, they also serve as the foundation for a significant portion of real-world parametric CAD models, as mentioned in [1].\n2) Our GeoCAD has extensibility to more complex shapes.  Beyond curve and extrusion operations, our GeoCAD is designed with extensibility in mind. \nIt can be easily adapted to incorporate more advanced operations, such as chamfer and revolve, once appropriate data becomes available for training. \nWe believe GeoCAD provides a scalable foundation for advancing toward general-purpose CAD modeling.  \n\n[1] R. Wu, C. Xiao, and C. Zheng. Deepcad: A deep generative network for computer-aided design models. ICCV 2021.\n\n**Q2: How this method would fare with more specific language instructions, like \"a rectangle cutout that's 45 degrees to the nearest outer wall of the shape\"**  \nA2: Thank you for raising this insightful question.   \nAs shown in Fig. 8 in our main paper, GeoCAD is capable of precisely controlling the key dimensional parameters when handling specific language instructions.\nTo further evaluate its capability, we tested the instruction: \"a rectangle cutout that's 45 degrees to the nearest outer wall of the shape.\"\nWe generated 50 new parts based on this instruction. \nAfter human evaluation, 36 of the generated parts included a rectangle cutout.\n23 of them had the rectangle cutout positioned near the nearest outer wall, while 6 of them had the rectangle cutout oriented at approximately 45 degrees to the nearest outer wall.\nIn addition, we designed 50 complex instructions involving angle or length specifications. \nFor each instruction, we generated 10 samples.\nAfter human evaluation, the instruction-following success rate (i.e., at least one of the 10 samples met the instruction) reached 86%.\nThese results demonstrate that our model can follow fine-grained geometric instructions.\n\n**Q3: Figure 8 shows the ability for the model to understand measurements. Is this from the LLM base understanding from Llama pretraining, or were there captions in your dataset that contained dimensional geometric instructions?**  \nA3: It is from captions in our dataset. As shown in lines 148-150 in our main paper, we incorporate key dimensional parameters into the captions. Thanks!\n\n\n**Q4: On average, how many CAD instructions compose the shapes? What is the maximum number of CAD instructions that the base LLM (llama-3-8b) able to handle (both theoretically and in practice)?**  \nA4: On average, each shape is composed of 6 CAD instructions. \nEach CAD instruction contains approximately 120 tokens on average.\nGiven that the base LLM (LLaMA-3-8B) has a context window of 8,192 tokens, it can theoretically handle up to around 68 CAD instructions.\nIn practice, the maximum number of CAD instructions contained within a shape observed in the dataset is 19.\nThanks!\n\n**Q5: Is the model capable of doing multiple rounds of consecutive edits, iteratively? Especially when the second instruction depends on the successful execution of the first instruction?**  \nA5: Yes, our model is capable of doing multiple rounds of consecutive edits, iteratively.\nSpecifically, at the beginning, the first loop is generated based on its corresponding geometric instruction.\nThe generated text for this first loop is then used to infill the masked region in the CAD model.\nIn this way, it ensures that subsequent loops are generated to align with the previously generated ones.\nFurthermore, this iterative process is user-friendly, as it only requires the user to specify the masked parts along with the related geometric instructions."}, {"review_id": "1axMqahUMz", "reviewer": "Reviewer_ZjoJ", "summary": "This paper proposes a method to support user-friendly CAD model editing through prompts. Using prompts to edit CAD models is an interesting problem; this method additionally requires the user to select a local profile to be edited. The technical contributions focus on: 1. The captioning strategy - it creates a prompt-to-local-part dataset suitable for both simple and complex parts generation. 2. The pre-training strategy on the prompt-to-local-part dataset, which enables the VLLM to learn local parts generation better. The experiments show that the method can produce better results than several baselines and can follow prompts to generate desired local profiles.", "questions": "Is it possible to also use prompt language to change the extrusion length and the extrusion combination operations (union and differ)? It would be better to discuss this point.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 1, "strengths_and_weaknesses": "Strengths:\n1.The proposed problem is novel; using natural language to efficiently edit CAD models is an interesting problem.\n\n2.The performance is impressive compared to several baselines.\n\n3.The paper is well-structured and easy to understand. The experimental results are also promising.\n\nWeaknesses:\n\n1.The input requires a clean CAD shape program, which limits the application scope. Many CAD shapes in the ABC dataset cannot be edited by this method.\n\n2.The editing operations have many limitations. Many local patterns are difficult to describe through text. Additionally, users can only edit the 2D sketch, not the extrusion length. The text prompt will change the entire 2D sketch selected by the user, not just part of the sketch. I don't see more efficiency or flexibility compared to manual editing of 2D sketches. \n\n3.The authors proposed two main technical contributions - the separated captioning strategy and the pre-training - which appear very intuitive and specifically designed only for this task.\n\nOverall, I feel the technical contributions are not significant, and the use case is not quite general. There are many limitations that need to be addressed for more practical use cases.", "was_revised": true, "final_justification": "The rebuttal addressed some of my concerns.\n\nI still think the authors should add more examples to demonstrate the method can hanlde more complicated cases.\nFor example only editing part of the sketch and editing the symmetry sketches.\n\nOverall, I remain borderline acceptance.", "rebuttal": "**Q1:The input requires a clean CAD shape program, which limits the application scope. Many CAD shapes in the ABC dataset cannot be edited by this method.**  \nA1: We appreciate the reviewer’s concern regarding the current scope of our method. We would like to clarify the following point:\n1) Our work focuses on common and practical CAD primitives, specifically sketches and extrusions. \nThey are sufficiently expressive to cover a wide variety of shapes, and form the basis of many real-world CAD models [1].\n2) As mentioned in [1] and [2], the ABC dataset is provided in the B-rep format, with no sufficient information to recover the user's design process [1], making it unsuitable for controllable CAD generation [2].\nIn contrast, we focus on the sketch-and-extrusion format, which align with the CAD design process in commercial CAD tools like SolidWorks and AutoCAD.  \n3) GeoCAD is designed to be extensible. Our GeoCAD can be easily adapted to support more complex operations such as chamfer and revolve once relevant data becomes available for training.\n\n[1] R. Wu, C. Xiao, and C. Zheng. Deepcad: A deep generative network for computer-aided design models. ICCV 2021.  \n[2] Z. Zhang, S. Sun, W. Wang, et al. Flexcad: Unified and versatile controllable cad generation with fine-tuned large language models. ICLR, 2025.\n\n**Q2: Many local patterns are difficult to describe through text.**  \nA2: We appreciate that the reviewer also recognized this point.\nIn fact, we have discussed this in the limitation section. \nAs shown in lines 88-92 in the supplementary material, \nin practice, certain complex local parts may be difficult or even impossible to describe precisely using text alone. \nAs a potential direction for future work, we believe that combining textual input with hand-drawn images for \nlocal geometry could enable users to convey their design intent more effectively and accurately.\n\n**Q3: the entire sketch, the extrusion length and the extrusion combination operations (union and differ)**  \nA3: This a good question. Actually, sketch-level editing is achievable based on our loop-level editing capability.\nThis is because the loop serves as the fundamental element of a sketch. For example, if a user selects a sketch consisting of two symmetric trapezoids and \nwishes to replace them with two symmetric letter \"E\" shapes, \nthe following automatic process can be performed:\n1) Estimate the center point of each trapezoid by averaging its coordinate points, which are extracted using string matching.\n2) Determine the symmetry axis based on the two center points.\n3) Generate a local letter \"E\" through GeoCAD replacing one of two trapezoids.\n4) Reflect the generated \"E\" across the symmetry axis to produce the second symmetric letter \"E\", thereby replacing both original trapezoids.  \n\nThus, for sketch-level editing, it is unnecessary for the model to generate the entire sketch; generating only the fundamental loop using GeoCAD is sufficient.\nSubsequently, the remaining loops within the same sketch can be derived from the fundamental loop through scaling, rotation, translation, reflection or pattern operations.\n\nOn the other hand, there is no need to control parameters such as extrusion length based on designed prompts. Specifically:\n1) Modifying the extrusion length and performing extrusion combination operations both require only a single parameter change in the CAD text. Instead of designing detailed prompts, directly modifying the parameters in the original CAD text is more efficient.\n2) For more complex extrusion modifications, users must specify detailed parameters such as 3D translations and rotations. \nThis process can be quite unintuitive for non-experts and unnecessarily tedious for experts.\nIn contrast, GeoCAD is designed to be user-friendly for both non-experts and experts, where users only need to select the part they wish to modify and provide geometric instructions for local parts.\n\n**Q4:Two main technical contributions are very intuitive and specifically designed only for this task.**  \nA4: \nActually, the two-stage training process are common in LLM training. We are the first to propose a two-stage LLM training pipeline in controllable CAD generation.\nSpecifically, in Stage 1, we introduce a data augmentation strategy tailored for local CAD generation, which was recognized by Reviewer HFw7 as both novel and effective.  \nIn Stage 2, we intentionally retain the standard training strategy with minimal modifications from FlexCAD.\nThe reasons are as follows:   \ni) To demonstrate that our method is simple yet effective. Without relying on elaborate training strategies, GeoCAD significantly outperforms FlexCAD and substantially enhances geometric instruction-following capabilities.  \nii) To facilitate the development of an omni CAD LLM. A standard and unified training strategy allows for better integration between different models, such as FlexCAD and our GeoCAD, bringing us closer to a general-purpose, omni-capable CAD foundation model.\n\nOn the other hand, our paper presents novelty not only in the technical aspects but also in the task formulation.\nSpecifically, we propose and solve a new task (i.e., local geometry-controllable CAD generation, see lines 27–35 in our main paper).\n**All reviewers recognized the value of our proposed task (Reviewer td3y: very sensible, Reviewer 6Hfr: more impactful than generating from scratch, Reviewer ZjoJ: novel and interesting, Reviewer HFw7: reasonable).**\nTo support this task, we also collected a dedicated dataset of local parts and their corresponding captions, which Reviewer 6Hfr described as a sizeable and solid contribution."}, {"review_id": "8yFrhcKa1L", "reviewer": "Reviewer_HFw7", "summary": "This paper proposes GeoCAD, a local geometry controllable generation method. It can take the original CAD model and user-specific geometric instructions  as inputs. It is one of the first model to achieve geometry control in the generation of CAD.  Extensive results shows that the proposed GeoCAD approach significantly enhances generation quality, validity, and text-to-CAD consistency in local geometry-controllable CAD generation.", "questions": "I wonder if the scaling law also happens here when evaluating the LLMs’ ability in predicting the masked local part. Will larger LLMs perform better on this CAD task after the proposed finetuning strategy?", "limitations": "Yes.", "rating": 5, "confidence": 1, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n- The motivation of this paper sounds reasonable. GeoCAD can achieve local geometry-control in the CAD generation field. \n- The two stage training pipeline is novel and effective. Different data augmentation strategies are used in stage 1.\n- The paper is well written and easy to follow. Main figures in this paper are well drawn and make the method clear to understand.\n- The experiments are done comprehensively and there are a lot of qualitative results given, demonstrating the superiority of the proposed GeoCAD method compared with other baselines such as OpenAI-o3 and FlexCAD.\n\nWeaknesses:\n- The authors claim that VLLMs exhibit limitations in fine-grained geometric descriptions for simple parts. For instance, they struggle to reliably discern whether a quadrilateral is a rhombus according to an image alone. But this is well supported. Actually state-of-the-art VLLMs can already perform well on existing benchmarks asking for geometric understanding.\n\n- The quality of the annotated 221k local parts are not clear. Human evaluation for at least part of them is needed.\n\n- The LLM is fine-tuned using LoRA, while it lacks comparisons on using other parameter efficient finetuning strategies as well as full-model finetuning. It is also not sure if the rank of 8 and an alpha of 3 is the optimum setting for the finetuning.", "was_revised": true, "final_justification": "The rebuttal addressed my concerns. It's good to see the human evaluation results indicating the quality of the local parts and large model also works. I will raise my score.", "rebuttal": "**Q1: State-of-the-art VLLMs can already perform well on existing benchmarks asking for geometric understanding.**  \nA1: Thanks for your valuable feedback!\nWe agree that SOTA VLLMs (such as OpenAI-o3) can already perform well on geometric understanding.\nHowever, when annotating simple local parts, Vertex-based captioning offers two key advantages over VLLM-based captioning:\n1) Cost-free: It does not require deploying any local models or invoking paid API services. \n2) Fully accurate: Compared to VLLM-based captioning, rule-based captioning ensures 100% correctness in annotation by explicitly extracting angle and length information of loops.\n\n**Q2: The quality of the annotated 221k local parts are not clear. Human evaluation for at least part of them is needed.**  \nA2: We are pleased to see that the reviewer also recognized this point.\nIn fact, we have conducted a human evaluation of the annotated local parts in the supplementary material.\nSpecifically, as described in lines 20–22 of the supplementary material, we manually assessed the correctness of each caption by labeling it as either \"Yes\" (correct) or \"No\" (incorrect).\nBased on this evaluation of 1,000 randomly sampled parts, the overall captioning accuracy is 91.3% for Qwen2.5-VL-72B-Instruct and 86.5% for GPT-4o.\n\n**Q3: Use other parameter efficient finetuning strategies as well as full-model finetuning. It is also not sure if the rank of 8 and an alpha of 3 is the optimum setting for the finetuning.**  \nA3: In fact, we have compared LoRA with full-model finetuning in the Table 1 in the supplementary material.\nThe results show that full-model finetuning performs comparably to LoRA-based tuning, demonstrating the effectiveness of the LoRA strategy.\nHere, we compare another parameter efficient finetuning strategy (i.e., QLoRA [1]) with LoRA.\nThe results are as follows:  \n|Method|COV$\\uparrow$|MMD$\\downarrow$|JSD$\\downarrow$|PV$\\uparrow$|Ver-score$\\uparrow$|VLLM-score$\\uparrow$|\n|-|-|-|-|-|-|-|\n|QLoRA|62.2%|1.17|1.01|85.5%|74.5%|63.8%|\n|LoRA (rank=8, alpha=64)|__65.7%__|1.10|0.92|89.7%|__77.8%__|66.0%|\n|LoRA (rank=16, alpha=32)|65.3%|__1.08__|__0.86__|__90.9%__|77.2%|__66.3%__|\n|LoRA (rank=8, alpha=32,ours)|64.9%|1.13|0.98|90.5%|76.4%|65.7%|  \n  \nThe results show that QLoRA performs the worst among the evaluated methods.\nOn the other hand, increasing the LoRA rank and alpha leads to slight performance improvements.\nHowever, higher rank and alpha values result in slower convergence.\nTo balance performance and training efficiency and ensure a fair comparison with FlexCAD, we choose rank = 8 and alpha = 32 in our main experiments. Thanks!\n\n[1] Dettmers T, Pagnoni A, Holtzman A, et al. Qlora: Efficient finetuning of quantized llms. NeurIPS 2023.\n\n**Q4: Will larger LLMs perform better on this CAD task after the proposed finetuning strategy?**  \nA4: Here, we compare the performance of Llama-3-70B equipped with LoRA.\nThe results are as follows: \n|Method|COV$\\uparrow$|MMD$\\downarrow$|JSD$\\downarrow$|PV$\\uparrow$|Ver-score$\\uparrow$|VLLM-score$\\uparrow$|\n|-|-|-|-|-|-|-|\n|Llama-3-70B|__69.8%__|__0.95__|__0.83__|__92.2%__|__81.0%__|__71.6%__|\n|Llama-3-8B (ours)|64.9%|1.13|0.98|90.5%|76.4%|65.7%|\n\nThe results show that Llama-3-70B achieves superior performance, but it is more time-consuming and costly, as also explored in FlexCAD. In other words, as the LLM scale increases, both generation quality and instruction-following ability improve accordingly. Thanks!"}]

Author rebuttal:

Meta-review: This paper addressed the problem of computer-aided design (CAD) and how Vision Language Models (VLMs) [called VLLMs in the paper] can help a user modify an existing CAD design at a component-level with a text-based description of the propopsed change to that component/part. VLMs and CAD are a growing area of resaerch and the paper is timely. The authors claim that theirs is the first to "achieve local geometry-control in the CAD generation field" with a novel captioning pipeline and empirical experiments against ad hoc base line (as opposed to existing baselines form prior work, as this approach seems to tackle a unique subproblem in CAD-VLM literature. The reviewers generally agreed that the paper was meritorious, Reviewers praised the paper for being well-written and showing strong empirical performance. Reviewers also had concerns that remained after the discussion, such as "I stand by the original assessment on the limited practicality of the proposed method...I also think that sketch level editing...is within the grasp of this method, and exploring that would have made a much bigger impact." Further, one reviewer noted that "I still think the authors should add more examples to demonstrate the method can handle more complicated cases...Also strengthen the limitation discussion section for local patterns, chamfer and revolve operations, and generalization for other representation, not only limited to the clean program." Given that most of the remaining concerns seem to be appropriate for consideration as "future work" and the reviewers were happy with the strength of the performance of the approach, its novelty, and the dataset and metrics, this paper is a strong one.
Decision: Accept (poster)