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 [1].
EMLight: Lighting Estimation via Spherical Distribution Approximation
Authors: Fangneng Zhan, Changgong Zhang, Yingchen Yu, Yuan Chang, Shijian Lu, Feiying Ma, Xuansong Xie3287-3295
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that EMLight achieves accurate illumination estimation and the generated relighting in 3D object embedding exhibits superior plausibility and ๏ฌdelity as compared with state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Fangneng Zhan 1, Changgong Zhang 2, Yingchen Yu 1, Yuan Chang 3, Shijian Lu 1, Feiying Ma 2, Xuansong Xie 2 1 Nanyang Technological University 2 DAMO Academy, Alibaba Group 3 Beijing University of Posts and Telecommunications EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks clearly labeled as "Pseudocode" or "Algorithm". |
| Open Source Code | No | The paper does not contain an unambiguous statement that the authors are releasing the code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | We evaluate EMLight with the Laval Indoor HDR Dataset (Gardner et al. 2017) that consists of 2,100 HDR panoramas taken in a variety of indoor environments. |
| Dataset Splits | No | The paper explicitly mentions a test set and training, but does not provide specific details for a separate validation dataset split needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions "Blender (Hess 2010)" and "Dense Net121" but does not provide specific version numbers for software dependencies like programming languages, frameworks, or libraries used for the implementation or experiments. |
| Experiment Setup | Yes | We then employ Vogel s method (Vogel 1979) to generate N (N=128 by default in this work) uniformly distributed anchor points on a unit sphere. |