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..
CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion
Authors: Guangyao Zhai, Evin Pınar Örnek, Shun-Cheng Wu, Yan Di, Federico Tombari, Nassir Navab, Benjamin Busam
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on SG-FRONT, where Common Scenes shows clear advantages over other methods regarding generation consistency, quality, and diversity. |
| Researcher Affiliation | Collaboration | 1Technical University of Munich 2Munich Center for Machine Learning 3Google |
| Pseudocode | No | The paper does not contain a section or figure explicitly labeled "Pseudocode" or "Algorithm," nor does it present structured code-like blocks outlining a procedure. |
| Open Source Code | Yes | Codes and the dataset are available on the website. https://sites.google.com/view/commonscenes |
| Open Datasets | Yes | Due to the lack of scene graph datasets also providing high-quality object meshes, we construct SG-FRONT, a set of well-annotated scene graph labels, based on a 3D synthetic dataset 3D-FRONT [18] that offers professionally decorated household scenarios. ... Codes and the dataset are available on the website. |
| Dataset Splits | No | The paper mentions that "SG-FRONT comprises around 45K 3D samples" and that "Extensive experiments are conducted on SG-FRONT," but it does not explicitly specify the training, validation, and test splits (e.g., percentages, sample counts, or predefined split references) needed for reproduction. |
| Hardware Specification | Yes | We conduct the training, evaluation, and visualization of Common Scenes on a single NVIDIA A100 GPU with 40GB memory. |
| Software Dependencies | No | The paper mentions using "Adam W optimizer" but does not specify other key software dependencies such as libraries (e.g., PyTorch, TensorFlow) or programming languages with their version numbers. |
| Experiment Setup | Yes | We set {λ1, λ2, λ3} = {1.0, 1.0, 1.0} in all our experiments. Nc in distribution Z is set to 128 and TSDF size D is set as 64. |