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..
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 | Venue PDF | 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. |