BIM-Edit benchmark finds best LLM scores only 49.5% average across geometric, semantic, and topological metrics on 324 IFC editing tasks, with no model fully solving more than 3.4%.
Blenderllm: Training large language models for computer-aided design with self- improvement
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3D-CoS represents 3D objects as Blender code generated by VLMs, with workflows for planning, RAG, and agents, showing better edit fidelity than point-cloud baselines.
SceneCode compiles natural language prompts into executable code programs that generate editable, articulated indoor scenes for physics simulation.
CADBench is a new multimodal benchmark for CAD program generation that combines 18k samples from DeepCAD, Fusion 360, ABC, MCB, and Objaverse across clean/noisy meshes and various renders, used to test 11 models and reveal failure modes.
BlenderRAG improves LLM-generated Blender code for 3D objects by retrieving semantically similar examples from a curated multimodal dataset of 500 expert-validated cases.
citing papers explorer
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BIM-Edit: Benchmarking Large Language Models for IFC-Based Building Information Modeling
BIM-Edit benchmark finds best LLM scores only 49.5% average across geometric, semantic, and topological metrics on 324 IFC editing tasks, with no model fully solving more than 3.4%.
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3D-CoS: A New 3D Reconstruction Paradigm Based on VLM Code Synthesis
3D-CoS represents 3D objects as Blender code generated by VLMs, with workflows for planning, RAG, and agents, showing better edit fidelity than point-cloud baselines.
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SceneCode: Executable World Programs for Editable Indoor Scenes with Articulated Objects
SceneCode compiles natural language prompts into executable code programs that generate editable, articulated indoor scenes for physics simulation.
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CADBench: A Multimodal Benchmark for AI-Assisted CAD Program Generation
CADBench is a new multimodal benchmark for CAD program generation that combines 18k samples from DeepCAD, Fusion 360, ABC, MCB, and Objaverse across clean/noisy meshes and various renders, used to test 11 models and reveal failure modes.
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BlenderRAG: High-Fidelity 3D Object Generation via Retrieval-Augmented Code Synthesis
BlenderRAG improves LLM-generated Blender code for 3D objects by retrieving semantically similar examples from a curated multimodal dataset of 500 expert-validated cases.