USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields
Authors: Moyang Li, Peng Wang, Lingzhe Zhao, Bangyan Liao, Peidong Liu
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental evaluations are conducted with both synthetic and real datasets, to evaluate the performance of our method. The experimental results demonstrate that USB-Ne RF achieves superior performance compared to prior state-of-the-art methods (e.g. as shown in Figure 1) in terms of rolling shutter effect removal, novel view image synthesis as well as camera motion estimation. |
| Researcher Affiliation | Academia | Moyang Li1,2 Peng Wang1,3 Lingzhe Zhao1 Bangyan Liao1,3 Peidong Liu1 1Westlake University 2ETH Z urich 3Zhejiang University moyali@ethz.ch, {wangpeng, zhaolingzhe, liaobangyan, liupeidong}@westlake.edu.cn |
| Pseudocode | No | The paper describes the method details in prose (Section 3) but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data are available at https://github.com/WU-CVGL/USB-Ne RF. |
| Open Datasets | Yes | We also evaluate our method on a public synthetic dataset (i.e. Carla-RS (Liu et al., 2020)) for fair comparisons against other methods. |
| Dataset Splits | No | The paper describes the total number of images generated for synthetic datasets and mentions using '6 additional views' for novel view synthesis evaluation, but does not specify explicit train/validation/test dataset splits (e.g., percentages or counts) or reference predefined splits with citations for reproduction. |
| Hardware Specification | Yes | we run a total of 200K steps on an NVIDIA RTX 3090 GPU. |
| Software Dependencies | No | The paper states 'We implement our method in Py Torch' and 'We use Adam (Kingma & Ba, 2014) optimizer', but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We set the learning rates to be 5 10 4 and 1 10 3 for the Ne RF and pose optimizers respectively. Both learning rates gradually decay to 5 10 5 and 1 10 5 respectively. During each training step, we randomly select 7200 pixels from all training images to minimize the loss function presented in Eq. 10 and we run a total of 200K steps on an NVIDIA RTX 3090 GPU. We adopt the linear adjustment of the positional encoding starting from steps 20K to 100K to achieve the coarse-to-fine training strategy as in BARF (Lin et al., 2021). |