SparseGNV: Generating Novel Views of Indoor Scenes with Sparse RGB-D Images
Authors: Weihao Cheng, Yan-Pei Cao, Ying Shan
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Sparse GNV on real-world indoor scenes and demonstrate that it outperforms state-of-the-art methods based on either neural radiance fields or conditional image generation. As the results presented in Table 1, our method outperforms all the baselines on PSNR, SSIM, and LPIPS. |
| Researcher Affiliation | Industry | Weihao Cheng, Yan-Pei Cao, Ying Shan ARC Lab, Tencent PCG whcheng@tencent.com, caoyanpei@gmail.com, yingsshan@tencent.com |
| Pseudocode | No | The paper describes the system architecture and training/inference procedures in text and diagrams, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement of code release for the described methodology or a link to a code repository. The provided arXiv link is for the paper itself. |
| Open Datasets | Yes | We use the Scan Net dataset (Dai et al. 2017), following the original train/test split, to study the proposed and baseline methods. |
| Dataset Splits | No | The paper mentions following 'the original train/test split' of the ScanNet dataset and holding out specific scenes for testing, but it does not provide explicit details (percentages, counts, or a formal description) for a validation set split. |
| Hardware Specification | Yes | The experiments are conducted on NVIDIA V100 GPUs. |
| Software Dependencies | No | The paper mentions various models and optimizers used (e.g., Point-Ne RF, VQ-GAN, Adam optimizer) but does not provide specific version numbers for any software, programming languages, or libraries used in the implementation. |
| Experiment Setup | Yes | We train the model with a learning rate 1e-4 and batch size 16 using the Adam optimizer. |