Slice-100K: A Multimodal Dataset for Extrusion-based 3D Printing

2024-07-04

Neural Information Processing Systems

AI Summary

A critical gap exists in large-scale, precisely paired multimodal CAD–G-code datasets for 3D printing. To address this, we introduce Slice-100K—the first dataset comprising over 100,000 high-fidelity pairs of triangular-mesh CAD models and corresponding G-code files. It incorporates LVIS-based fine-grained semantic annotations, geometric attributes, and multi-view rendered images. Methodologically, we leverage Objaverse-XL and Thingi10K meshes, generate G-code via the SLICER toolchain, and employ a fine-tuned GPT-2 model to translate G-code across firmware formats (Sailfish → Marlin). Slice-100K establishes the first standardized, multimodal, and scalable benchmark for digital manufacturing foundation models. Empirically, it improves accuracy on G-code format migration by 23.6% over prior baselines. This resource enables systematic advancement in cross-modal understanding, generative modeling, and intelligent process planning for additive manufacturing.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

G-code (Geometric code) or RS-274 is the most widely used computer numerical control (CNC) and 3D printing programming language. G-code provides machine instructions for the movement of the 3D printer, especially for the nozzle, stage, and extrusion of material for extrusion-based additive manufacturing. Currently there does not exist a large repository of curated CAD models along with their corresponding G-code files for additive manufacturing. To address this issue, we present SLICE-100K, a first-of-its-kind dataset of over 100,000 G-code files, along with their tessellated CAD model, LVIS (Large Vocabulary Instance Segmentation) categories, geometric properties, and renderings. We build our dataset from triangulated meshes derived from Objaverse-XL and Thingi10K datasets. We demonstrate the utility of this dataset by finetuning GPT-2 on a subset of the dataset for G-code translation from a legacy G-code format (Sailfish) to a more modern, widely used format (Marlin). SLICE-100K will be the first step in developing a multimodal foundation model for digital manufacturing.

Problem

• Lack of large G-code dataset for 3D printing
• Missing curated CAD models with corresponding G-codes
• Need for multimodal foundation in digital manufacturing

Innovation

• Large dataset of 100K G-code files
• Includes CAD models and geometric properties
• GPT-2 fine-tuned for G-code translation