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..
S-NeRF: Neural Radiance Fields for Street Views
Authors: Ziyang Xie, Junge Zhang, Wenye Li, Feihu Zhang, Li Zhang
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thorough experiments on the large-scale driving datasets (e.g., nu Scenes and Waymo) demonstrate that our method beats the state-of-the-art rivals by reducing 7 40% of the mean-squared error in the street-view synthesis and a 45% PSNR gain for the moving vehicles rendering. |
| Researcher Affiliation | Academia | Ziyang Xie1 , Junge Zhang1 , Wenye Li1, Feihu Zhang2, Li Zhang1 1Fudan University 2University of Oxford |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | https://ziyang-xie.github.io/s-nerf |
| Open Datasets | Yes | We perform our experiments on two open source self-driving datasets: nu Scenes (Caesar et al., 2019) and Waymo (Sun et al., 2020). |
| Dataset Splits | Yes | For the foreground vehicles, we extract car crops from nu Scenes and Waymo video sequences. For each vehicle, there are around 2 8 views used for training and 1 3 views for testing. |
| Hardware Specification | Yes | The training takes about 2 hours for each vehicle on a single RTX3090 gpu. For each vehicle, there are around 2 8 views used for training and 1 3 views for testing. Our S-Ne RF is trained on two RTX3090 GPUs which takes about 17 hours for a scene with about 250 images (with a resolution of 1280 1920). |
| Software Dependencies | No | We use NLSPN (Park et al., 2020) network for depth completion, which propagates the depth information from Li DAR points to surrounding pixels. |
| Experiment Setup | Yes | In all the experiments, the depth and smooth loss weight λ1 and λ2 are set to 1 and 0.15 respectively for foreground vehicles. And for background street scenes, we set τ = 20% for confidence measurement and the radius r = 3 in all scenes. λ1 = 0.2 and λ2 = 0.01 are used as the loss balance weights. We train our S-Ne RF for 30k iterations using Adam optimizer with 5 4 as the learning rate and 1024 as the batch size. The learning rate is reduced log-linearly from 5 10 4 to 5 10 6 with a warm-up phase of 2500 iterations. |