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..
Micro-macro Wavelet-based Gaussian Splatting for 3D Reconstruction from Unconstrained Images
Authors: Yihui Li, Chengxin Lv, Hongyu Yang, Di Huang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that MW-GS delivers state-of-the-art rendering performance, surpassing existing methods. |
| Researcher Affiliation | Academia | 1 State Key Laboratory of Complex and Critical Software Environment, Beijing, China 2 School of Computer Science and Engineering, Beihang University, China 3 School of Artificial Intelligence, Beihang University, China 4 Shanghai Artificial Intelligence Laboratory, Shanghai, China EMAIL |
| Pseudocode | No | The paper describes the method using prose and mathematical formulas, without explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Following previous works (Chen et al. 2022b; Zhang et al. 2024), we evaluate different methods on three datasets: Brandenburg Gate, Sacre Coeur, and Trevi Fountain |
| Dataset Splits | No | The paper states 'with all images downsampled by a factor of 2 during both training and evaluation' but does not specify the training/validation/test splits, percentages, or sample counts for the datasets used. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. |