Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SceneCraft: Layout-Guided 3D Scene Generation
Authors: Xiuyu Yang, Yunze Man, Junkun Chen, Yu-Xiong Wang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experimental analysis, we demonstrate that our method significantly outperforms existing approaches in complex indoor scene generation with diverse textures, consistent geometry, and realistic visual quality.Trained with multi-view indoor scene datasets [49, 72], our work achieves state-of-the-art 3D indoor scene generation performance, both quantitatively and qualitatively. |
| Researcher Affiliation | Academia | 1 Shanghai Jiao Tong University 2 University of Illinois Urbana-Champaign |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | We promise that we will open-source the data and code after paper acceptance. |
| Open Datasets | Yes | We use multi-view images from Scan Net++ [72] and Hyper Sim [49] to construct BBI data.Our processed data are publicly available 1 2. 1Layout Scannet++: https://huggingface.co/datasets/gzzyyxy/layout_diffusion_scannetpp_voxel0.2 2Layout Hypersim: https://huggingface.co/datasets/gzzyyxy/layout_diffusion_hypersim |
| Dataset Splits | No | The paper mentions splitting generation tasks but does not provide specific percentages or counts for train/validation/test splits of the datasets (ScanNet++ and Hypersim). |
| Hardware Specification | Yes | For finetuning the diffusion model, we use a total batch size of 16 on 2 NVIDIA A6000 GPUs with a constant learning rate of 5e-5, training for around 10k iterations. For the scene generation task, we use 2 A6000 GPUs to perform all our experiments. |
| Software Dependencies | No | The paper mentions software like Stable Diffusion, NeRFStudio, and Control Nets but does not provide specific version numbers for these or other dependencies. |
| Experiment Setup | Yes | For finetuning the diffusion model, we use a total batch size of 16 on 2 NVIDIA A6000 GPUs with a constant learning rate of 5e-5, training for around 10k iterations. For Ne RF training, we use a constant learning rate of 1e-2 for proposal networks and 1e-3 for fields. |