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..
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning
Authors: Bolivar Solarte, Chin-Hsuan Wu, Yueh-Cheng Liu, Yi-Hsuan Tsai, Min Sun
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our solution achieves favorable performance against state-of-the-art methods when self-training from three publicly available source datasets to a unique, newly labeled dataset consisting of multi-view images of the same scenes. In experiments, we leverage the MP3D-FPE [29] multi-view dataset as our unlabeled new domain. |
| Researcher Affiliation | Collaboration | Bolivar Solarte 1, Chin-Hsuan Wu 1, Yueh-Cheng Liu1, Yi-Hsuan Tsai 2, Min Sun1 1National Tsing Hua University, 2Phiar Technologies |
| Pseudocode | No | The paper does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | We will make our models, codes, and dataset available to the public. (This statement indicates future availability, not current concrete access.) |
| Open Datasets | Yes | We conduct extensive experiments using publicly available 360-image layout datasets: Matterport3D Floor Plan Estimation (MP3D-FPE) [29] as the target dataset, and three real-world datasets as the pre-training datasets, including Matterport Layout [36, 48], Zillow Indoor Dataset (ZIn D) [9], and the dataset used in Layout Net [47]. |
| Dataset Splits | No | The paper specifies training and testing sets, but does not explicitly define a 'validation set' or 'validation split' with specific percentages or sample counts for hyperparameter tuning or model selection. |
| Hardware Specification | Yes | All models are trained on a single NVIDIA TITAN X GPU with 12 GB of memory. |
| Software Dependencies | No | The paper mentions 'Horizon Net [30] as our layout estimation backbone' but does not specify version numbers for any software dependencies like programming languages, libraries, or frameworks (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We use the Adam optimizer to train the model for 300 epochs by setting the learning rate as 0.0001 and the batch size as 4. |