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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Novel View Synthesis Under Large-Deviation Viewpoint for Autonomous Driving
Authors: Xin Ma, Jiguang Zhang, Peng Lu, Shibiao Xu, Chengwei Pan
AAAI 2025 | Venue PDF | LLM Run Details | Input Tokens: 13,163 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,589 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate substantial improvements in synthesis quality for large-deviation viewpoints, validating the effectiveness of our approach. ... Experiments |
| Researcher Affiliation | Academia | 1Beijing University of Posts and Telecommunications, Beijing, China 2Institute of Automation, Chinese Academy of Sciences, Beijing, China 3Beihang University, Beijing, China |
| Pseudocode | No | The paper describes methods in prose and mathematical formulations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to a code repository. |
| Open Datasets | Yes | Datasets. We conduct experiments on two large-scale urban datasets: KITTI (Geiger, Lenz, and Urtasun 2012) and Waymo Open Datasets (Sun et al. 2020). |
| Dataset Splits | Yes | As common practice for evaluation novel view synthesis performance, we select every 8th image in the sequences as the test set and the remaining images as the training set. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU types) used for running the experiments. It mentions a rendering speed of 83 FPS but without corresponding hardware details. |
| Software Dependencies | No | The paper mentions using ResNet-50 and COLMAP but does not provide specific version numbers for these or any other software libraries or dependencies. |
| Experiment Setup | No | The 'Experimental Setup' section describes the datasets and metrics used but does not provide specific hyperparameter values (e.g., learning rate, batch size, optimizer settings) or other detailed training configurations. |