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..
InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior
Authors: Chenguo Lin, Yadong MU
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results reveal that the proposed method surpasses existing state-of-the-art approaches by a large margin. Thorough ablation studies confirm the efficacy of crucial design components. |
| Researcher Affiliation | Academia | Chenguo Lin, Yadong Mu Peking University EMAIL, EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm blocks found. |
| Open Source Code | Yes | Project page: https://chenguolin.github.io/projects/Instruct Scene. Our instruction-scene pair dataset and code for both training and evaluation can be found in https://chenguolin.github.io/projects/Instruct Scene. |
| Open Datasets | Yes | To fit practical scenarios and promote the benchmarking of instruction-drive scene synthesis, we curate a high-quality dataset containing paired scenes and instructions with the help of large language and multimodal models (Li et al., 2022; Ouyang et al., 2022; Open AI, 2023). Our instruction-scene pair dataset and code for both training and evaluation can be found in https://chenguolin.github.io/projects/Instruct Scene. |
| Dataset Splits | Yes | We use the same data split for training and evaluation as ATISS (Paschalidou et al., 2021). |
| Hardware Specification | Yes | our method takes about 12 seconds to generate a batch of 128 living rooms by our method on a single A40 GPU. |
| Software Dependencies | No | The paper mentions software like Open Shape, CLIP, Blender, and clean-fid library, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We use 5-layer and 8-head Transformers with 512 attention dimensions and a dropout rate of 0.1 for all generative models in this work. They are trained by the Adam W optimizer (Loshchilov & Hutter, 2018) for 500,000 iterations with a batch size of 128, a learning rate of 1e-4, and a weight decay of 0.02. Exponentially moving average (EMA) technique (Polyak & Juditsky, 1992; Ho et al., 2020) with a decay factor of 0.9999 is utilized in the model parameters. |