Boosting Point Clouds Rendering via Radiance Mapping
Authors: Xiaoyang Huang, Yi Zhang, Bingbing Ni, Teng Li, Kai Chen, Wenjun Zhang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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 τ 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. |