IL3D: A Large-Scale Indoor Layout Dataset for LLM-Driven 3D Scene Generation

Wenxu Zhou, NIE KAIXUAN, Hang Du, Dong Yin, Wei Huang, Siqiang Guo, Xiaobo Zhang, Pengbo Hu

Keywords: Indoor scene understanding, Synthetic dataset, 3D Computer Vision

TL;DR: IL3D, a large-scale dataset with 27,816 indoor layouts and 29,215 3D assets, supports LLM-driven 3D scene generation. It includes natural language annotations and rigorous benchmarks, enhancing the 3D scene generation task.

Abstract: In this study, we present IL3D, a large-scale dataset meticulously designed for large language model (LLM)-driven 3D scene generation, addressing the pressing demand for diverse, high-quality training data in indoor layout design. Comprising 27,816 indoor layouts across 18 prevalent room types and a library of 29,215 high-fidelity 3D object assets, IL3D is enriched with instance-level natural language annotations to support robust multimodal learning for vision-language tasks. We establish rigorous benchmarks to evaluate LLM-driven scene generation. Experimental results show that supervised fine-tuning (SFT) of LLMs on IL3D significantly improves generalization and surpasses the performance of SFT on other datasets. IL3D offers flexible multimodal data export capabilities, including point clouds, 3D bounding boxes, multiview images, depth maps, normal maps, and semantic masks, enabling seamless adaptation to various visual tasks. As a versatile and robust resource, IL3D significantly advances research in 3D scene generation and embodied intelligence, by providing high-fidelity scene data to support environment perception tasks of embodied agents. The dataset and accompanying code will be publicly accessible on GitHub.

Primary Area: datasets and benchmarks

Submission Number: 2377