SLIBO-Net: Floorplan Reconstruction via Slicing Box Representation with Local Geometry Regularization
Authors: Jheng-Wei Su, Kuei-Yu Tung, Chi-Han Peng, Peter Wonka, Hung-Kuo (James) Chu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted experiments on a large-scale indoor synthetic dataset called Structured3D [25]. Following the approach in [9, 24], we divided the complete dataset into 2991 training samples, 250 validation samples, and 241 test samples |
| Researcher Affiliation | Academia | 1National Tsing Hua University 2National Yang Ming Chiao Tung University 3King Abdullah University of Science and Technology |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly provided in the paper. |
| Open Source Code | Yes | Our code and dataset are available online1. 1https://ericsujw.github.io/SLIBO-Net/ |
| Open Datasets | Yes | We conducted experiments on a large-scale indoor synthetic dataset called Structured3D [25]. |
| Dataset Splits | Yes | We divided the complete dataset into 2991 training samples, 250 validation samples, and 241 test samples |
| Hardware Specification | Yes | We implemented our model in Py Torch and trained our center transformer Tcenter on 2 NVIDIA V100s for 2 days. The box transformer Tbox was trained on 8 NVIDIA V100s for 1.9 days. |
| Software Dependencies | No | The paper mentions implementing the model in |
| Experiment Setup | Yes | We use the Adam optimizer with b1=0.9 and b2=0.999. The learning rates of two transformers and the Res Net-50 are 2.5e-4, and 1e-5, and we train the center transformer for 5000 epochs, box transformer for 16000 epochs with batch size 128 and 123 for each GPU. We empirically set 1 = 1, 2 = 1 in Equation 5, 3 = 1, 4 = 1, 5 = 5., 6 = 1 in Equation 10, = 2 in Equation 4, and M = 25, N = 50 in Section 3.3. |