DeBaRA: Denoising-Based 3D Room Arrangement Generation
Authors: Léopold Maillard, Nicolas Sereyjol-Garros, Tom Durand, Maks Ovsjanikov
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach through extensive experiments and demonstrate significant improvement upon state-of-the-art approaches in a range of scenarios. |
| Researcher Affiliation | Collaboration | Léopold Maillard1,2 Nicolas Sereyjol-Garros Tom Durand2 Maks Ovsjanikov1 1LIX, École Polytechnique, IP Paris 2Dassault Systèmes |
| Pseudocode | Yes | Algorithm 1 Self Score Evaluation |
| Open Source Code | No | Unfortunately, the code for De Ba RA cannot be disclosed due to author affiliation. |
| Open Datasets | Yes | Our experiments are conducted on the 3D-FRONT [10] synthetic indoor layouts, furnished with assets from 3D-FUTURE [11] that we use as the object retrieval database. |
| Dataset Splits | No | The paper states 'leading respectively to 2338/587 and 2071/516 train/test splits' but does not explicitly provide numerical details for a validation split for reproducibility. |
| Hardware Specification | Yes | All the training and evaluation experiments as well as the computation of generation times reported in Table 5 have been performed on a single NVIDIA RTX A6000 GPU. |
| Software Dependencies | No | While PyTorch and Meta Llama-3-8B are mentioned, specific version numbers for PyTorch and other key software components are not provided, preventing full reproducibility of software dependencies. |
| Experiment Setup | Yes | We trained our models separately on the 3D-FRONT [10] living room and dining room subsets for 3000 epochs, with a batch size of 32 and monitor the validation loss to avoid overfitting of the training set in the late iterations. We use the Adam W [29] optimizer with its Py Torch default parameters and learning rate η = 10 4, scheduled with a linear warmup phase for the first 50 epochs, starting at η 0.01. Following this, a cosine annealing schedule [28] reduces η to a minimum of 10 8 over 2200 epochs. |