HORIZON: High-Resolution Semantically Controlled Panorama Synthesis
Authors: Kun Yan, Lei Ji, Chenfei Wu, Jian Liang, Ming Zhou, Nan Duan, Shuai Ma
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We rigorously evaluate our methodology on a diverse array of indoor and outdoor datasets, establishing its superiority over recent related work, in terms of both quantitative and qualitative performance metrics. |
| Researcher Affiliation | Collaboration | Kun Yan1, Lei Ji2, Chenfei Wu2, Jian Liang3, Ming Zhou4, Nan Duan2, Shuai Ma1 1SKLSDE Lab, Beihang University, 2Microsoft Reseach Asia, 3Peking University, 4Langboat Technology |
| Pseudocode | No | The paper describes its methods in text and uses diagrams (e.g., Figure 2) but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing the source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We evaluate our model on the high-resolution Street Learn dataset (Mirowski et al. 2019), which consists of Google Street View panoramas. |
| Dataset Splits | No | The Pittsburgh dataset containing 58k images, split into 52.2k for training and 5.8k for testing. The paper mentions training and testing splits, but does not explicitly state a validation split. |
| Hardware Specification | Yes | The experiments are conducted on 64 V100 GPUs, each with 32Gi B memory. |
| Software Dependencies | No | The paper mentions using components like a 'pretrained CLIP visual module' but does not provide specific version numbers for any software dependencies or libraries required for reproduction. |
| Experiment Setup | Yes | Every equirectangular projected panoramic image with a resolution of 768x1,536 is first divided into 3x6=18 RGB view patches, each with a resolution of 256x256. Then we train a VQGAN(Esser, Rombach, and Ommer 2021) on every view patch separately. |