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..
SparseGNV: Generating Novel Views of Indoor Scenes with Sparse RGB-D Images
Authors: Weihao Cheng, Yan-Pei Cao, Ying Shan
AAAI 2024 | Venue PDF | 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 EMAIL, EMAIL, EMAIL |
| 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. |