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..
Boosting Point Clouds Rendering via Radiance Mapping
Authors: Xiaoyang Huang, Yi Zhang, Bingbing Ni, Teng Li, Kai Chen, Wenjun Zhang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We obtain a PSNR of 31.74 on Ne RF-Synthetic, 25.88 on Scan Net and 30.81 on DTU. Code and data are publicly available in https://github.com/seanywang0408/Radiance Mapping. ... Experiments ... Benchmark Evaluation ... Ablation Study |
| Researcher Affiliation | Collaboration | Xiaoyang Huang1*, Yi Zhang1*, Bingbing Ni1 , Teng Li2, Kai Chen3, Wenjun Zhang1 1Shanghai Jiao Tong University, Shanghai 200240, China 2Anhui University 3Shanghai AI Lab |
| Pseudocode | No | The paper describes the method and pipeline in text and uses figures, but it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Code and data are publicly available in https://github.com/seanywang0408/Radiance Mapping. |
| Open Datasets | Yes | We experiment on three datasets: Ne RF-Synthetic (Mildenhall et al.), Scan Net (Dai et al.) and DTU (Aanæs et al.). |
| Dataset Splits | No | The paper mentions 'training and testing views are fixed' and uses standard datasets like Ne RF-Synthetic, Scan Net, and DTU, but it does not explicitly provide specific percentages, counts, or a detailed methodology for creating train/validation/test splits within the paper's text. |
| Hardware Specification | Yes | All results are measured on a Ti TAN Xp with an image size of 800 800. |
| Software Dependencies | No | The paper mentions software components such as Open GL, Py Torch3D, and Adam optimizer, but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | We set the radius threshold Ο as 5e-3 for Ne RF-Synthetic, 1.5e-2 for Scan Net, and 3e-3 for DTU. ... We use the Adam optimizer for training, with a batch size of 1. The initial learning rates of MLP and U-Net are 5e-4 and 1.5e-4 respectively, which are multiplied by 0.9999 in each step. |