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 [1].
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
| 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. |