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 | Conference PDF | Archive PDF | Plain Text | 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. |