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..
Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting
Authors: Junha Hyung, Susung Hong, Sungwon Hwang, Jaeseong Lee, Jaegul Choo, Jin-Hwa Kim
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the effective rank regularization, comparing its performance as an add-on to baseline models. Additionally, we analyze the contributions of different components of the method. ... Table 1 presents the quantitative results of geometry reconstruction on the DTU dataset. We report the Chamfer distance for each scene, along with the mean Chamfer |
| Researcher Affiliation | Collaboration | Junha Hyung1 Susung Hong4 Sungwon Hwang1 Jaeseong Lee1 Jaegul Choo1 Jin-Hwa Kim2,3 1KAIST 2NAVER AI Lab 3SNU AIIS 4Korea University |
| Pseudocode | No | The paper does not contain a pseudocode block or a clearly labeled algorithm block. |
| Open Source Code | Yes | The project page is available at https://junhahyung.github.io/erankgs.github.io/. |
| Open Datasets | Yes | We evaluate our model on the DTU [14] and Mip-Ne RF360 [2] datasets. |
| Dataset Splits | No | For novel view synthesis, the images are split into training and test sets, while the entire set of images is used for geometry reconstruction. |
| Hardware Specification | Yes | All experiments are conducted on a Tesla V100 GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies used in the experiments. It only mentions the NSML platform, but no specific libraries or their versions. |
| Experiment Setup | Yes | The regularization hyperparameter ฮปerank = 0.01 is used for all training. |