ARXIV:2603.13098 · 3D DESIGN · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

SldprtNet: A Large-Scale Multimodal Dataset for CAD Generation in Language-Driven 3D Design

Q: What products could be built from this research?

Why now — the manufacturing industry is under pressure to accelerate product cycles and adopt Industry 4.0, while recent advances in multimodal AI (like Qwen2.5-VL) make language-to-CAD feasible; this dataset provides the training foundation needed to build commercial products.

Q: What are the practical use cases?

A cloud-based AI assistant that allows mechanical engineers to describe a part in natural language (e.g., 'a bracket with four mounting holes and a 90-degree bend') and automatically generates a parametric CAD model in SolidWorks or STEP format, ready for simulation or manufacturing.

arXiv

SldprtNet is a comprehensive multimodal dataset for CAD generation, enhancing 3D design through semantic-driven modeling.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain SldprtNet is a comprehensive multimodal dataset for CAD generation, enhancing 3D design through semantic-driven modeling.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

SldprtNet is a comprehensive multimodal dataset for CAD generation, enhancing 3D design through semantic-driven modeling. The dataset provides 3D models in both .step and .sldprt formats to support diverse training and testing.

METHOD

Full abstract

We introduce SldprtNet, a large-scale dataset comprising over 242,000 industrial parts, designed for semantic-driven CAD modeling, geometric deep learning, and the training and fine-tuning of multimodal models for 3D design. The dataset provides 3D models in both .step and .sldprt formats to support diverse training and testing. To enable parametric modeling and facilitate dataset scalability, we developed supporting tools, an encoder and a decoder, which support 13 types of CAD commands and enable lossless transformation between 3D models and a structured text representation. Additionally, each sample is paired with a composite image created by merging seven rendered views from different viewpoints of the 3D model, effectively reducing input token length and accelerating inference. By combining this image with the parameterized text output from the encoder, we employ the lightweight multimodal language model Qwen2.5-VL-7B to generate a natural language description of each part's appearance and functionality. To ensure accuracy, we manually verified and aligned the generated descriptions, rendered images, and 3D models. These descriptions, along with the parameterized modeling scripts, rendered images, and 3D model files, are fully aligned to construct SldprtNet. To assess its effectiveness, we fine-tuned baseline models on a dataset subset, comparing image-plus-text inputs with text-only inputs. Results confirm the necessity and value of multimodal datasets for CAD generation. It features carefully selected real-world industrial parts, supporting tools for scalable dataset expansion, diverse modalities, and ensured diversity in model complexity and geometric features, making it a comprehensive multimodal dataset built for semantic-driven CAD modeling and cross-modal learning.

RESULT

ScienceToStartup currently rates this 6.0/10 on the public viability pass. The dataset provides 3D models in both .step and .sldprt formats to support diverse training and testing.

WHY NOW

3D Design moved forward this cycle; last verified April 2026. Public score 6.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score6.0

PainSldprtNet is a comprehensive multimodal dataset for CAD generation, enhancing 3D design through semantic-driven modeling.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

SldprtNet is a comprehensive multimodal dataset for CAD generation, enhancing 3D design through semantic-driven modeling.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.13098 · 3D DESIGN · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

SldprtNet: A Large-Scale Multimodal Dataset for CAD Generation in Language-Driven 3D Design

arXiv

SldprtNet is a comprehensive multimodal dataset for CAD generation, enhancing 3D design through semantic-driven modeling.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain SldprtNet is a comprehensive multimodal dataset for CAD generation, enhancing 3D design through semantic-driven modeling.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

ScienceToStartup currently rates this 6.0/10 on the public viability pass. The dataset provides 3D models in both .step and .sldprt formats to support diverse training and testing.

WHY NOW

3D Design moved forward this cycle; last verified April 2026. Public score 6.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score6.0

PainSldprtNet is a comprehensive multimodal dataset for CAD generation, enhancing 3D design through semantic-driven modeling.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

SldprtNet is a comprehensive multimodal dataset for CAD generation, enhancing 3D design through semantic-driven modeling.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.13098

SldprtNet: A Large-Scale Multimodal Dataset for CAD Generation in Language-Driven 3D Design

SldprtNet is a comprehensive multimodal dataset for CAD generation, enhancing 3D design through semantic-driven modeling.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback45 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

6.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

ScienceToStartup currently rates this 6.0/10 on the public viability pass. The dataset provides 3D models in both .step and .sldprt formats to support diverse training and testing.

WHY NOW

3D Design moved forward this cycle; last verified April 2026. Public score 6.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
SldprtNet is a comprehensive multimodal dataset for CAD generation, enhancing 3D design through semantic-driven modeling. The dataset provides 3D models in both .step and .sldprt formats to support diverse training and testing.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
We introduce SldprtNet, a large-scale dataset comprising over 242,000 industrial parts, designed for semantic-driven CAD modeling, geometric deep learning, and the training and fine-tuning of multimodal models for 3D design. The dataset provides 3D models in both .step and .sldprt formats to support diverse training and testing.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. The dataset provides 3D models in both .step and .sldprt formats to support diverse training and testing.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
3D Design moved forward this cycle; last verified April 2026. Public score 6.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

3D Design

Competitors

not indexed

Competitive landscape

SldprtNet is a comprehensive multimodal dataset for CAD generation, enhancing 3D design through semantic-driven modeling.

Segment

3D Design

Adoption evidence

No public code link in the paper record yet

Commercial read

6.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.13098 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(45)

CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generation

2025Anna C. Doris, Md Ferdous Alam et al.

CAD-GPT: Synthesising CAD Construction Sequence with Spatial Reasoning-Enhanced Multimodal LLMs

2024Siyu Wang, Cailian Chen et al.

CAD-MLLM: Unifying Multimodality-Conditioned CAD Generation With MLLM

2024Jingwei Xu, Chenyu Wang et al.

Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Prompts

2024Mohammad Sadil Khan, Sankalp Sinha et al.

Mistral 7B

2023Albert Qiaochu Jiang, Alexandre Sablayrolles et al.

T3Bench: Benchmarking Current Progress in Text-to-3D Generation

2023Yuze He, Yushi Bai et al.

SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines

2023Dimitrios Mallis, Sk Aziz Ali et al.

Hierarchical Neural Coding for Controllable CAD Model Generation

2023Xiang Xu, P. Jayaraman et al.

Neurosymbolic Models for Computer Graphics

2023Daniel Ritchie, Paul Guerrero et al.

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

2023Junnan Li, Dongxu Li et al.

Magic3D: High-Resolution Text-to-3D Content Creation

2022Chen-Hsuan Lin, Jun Gao et al.

CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language

2022Aditya Sanghi, Rao Fu et al.

DreamFusion: Text-to-3D using 2D Diffusion

2022Ben Poole, Ajay Jain et al.

CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations

2022Elona Dupont, K. Cherenkova et al.

Future prospects of computer-aided design (CAD) – A review from the perspective of artificial intelligence (AI), extended reality, and 3D printing

2022Bonsa Regassa Hunde, Abraham Debebe Woldeyohannes

Flamingo: a Visual Language Model for Few-Shot Learning

2022Jean-Baptiste Alayrac, Jeff Donahue et al.

Chain of Thought Prompting Elicits Reasoning in Large Language Models

2022Jason Wei, Xuezhi Wang et al.

Zero-Shot Text-Guided Object Generation with Dream Fields

2021Ajay Jain, B. Mildenhall et al.

Vitruvion: A Generative Model of Parametric CAD Sketches

2021Ari Seff, Wenda Zhou et al.

'CADSketchNet' - An Annotated Sketch dataset for 3D CAD Model Retrieval with Deep Neural Networks

2021Bharadwaj Manda, Shubham Dhayarkar et al.

Showing 20 of 45 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkBenDFM: A taxonomy and synthetic CAD dataset for manufacturability assessment in sheet metal bending

6.0

Extension

none indexed

Commercially relevant

Higher ViabilityDreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces

8.0

Higher ViabilityLearning From Design Procedure To Generate CAD Programs for Data Augmentation

7.0

Higher ViabilityA2Z-10M+: Geometric Deep Learning with A-to-Z BRep Annotations for AI-Assisted CAD Modeling and Reverse Engineering

7.0

Higher ViabilityCADBench: A Multimodal Benchmark for AI-Assisted CAD Program Generation

8.0

Higher ViabilityBenchCAD: A Comprehensive, Industry-Standard Benchmark for Programmatic CAD

7.0

Higher ViabilityMUSE: Benchmarking Manufacturable, Functional, and Assemblable Text-to-CAD Generation

7.0

Higher ViabilityTOOLCAD: Exploring Tool-Using Large Language Models in Text-to-CAD Generation with Reinforcement Learning

7.0

Higher ViabilityTowards High-Fidelity CAD Generation via LLM-Driven Program Generation and Text-Based B-Rep Primitive Grounding

7.0

Higher ViabilityIndustrial3D: A Terrestrial LiDAR Point Cloud Dataset and CrossParadigm Benchmark for Industrial Infrastructure

7.0

Conflicting

none indexed

Owned Distribution

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Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.13098
Route: /paper/sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design
Active tab: read
Artifact: sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design/paper-pack
REST build passport API/api/v1/paper/sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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  "arxiv_id": "2603.13098",
  "canonical_route": "/paper/sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design",
  "endpoints": {
    "paper_pack": "/api/v1/paper/sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design/paper-pack",
    "build_passport": "/api/v1/paper/sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
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}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design

stale

Proof freshness: stale
Proof status: unverified
Display score: 6/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

SldprtNet: A Large-Scale Multimodal Dataset for CAD Generation in Language-Driven 3D Design

Canonical ID sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design | Route /paper/sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.13098"
  }
}

source_context

{
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  "mode": "paper",
  "query": "SldprtNet: A Large-Scale Multimodal Dataset for CAD Generation in Language-Driven 3D Design",
  "normalized_query": "2603.13098",
  "route": "/paper/sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design",
  "paper_ref": "sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: SldprtNet: A Large-Scale Multimodal Dataset for CAD Generation in Language-Driven 3D Design

/buildability/sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design

Watchwatch

Subject: SldprtNet: A Large-Scale Multimodal Dataset for CAD Generation in Language-Driven 3D Design

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design

Paper ref

sldprtnet-a-large-scale-multimodal-dataset-for-cad-generation-in-language-driven-3d-design

arXiv id

2603.13098

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

ef2fe162764b69235d26185e7af22a1ead8430c9506f89f34006116131be0453

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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SldprtNet: A Large-Scale Multimodal Dataset for CAD Generation in Language-Driven 3D Design

SldprtNet: A Large-Scale Multimodal Dataset for CAD Generation in Language-Driven 3D Design

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