Neural Transmitted Radiance Fields
Authors: Chengxuan Zhu, Renjie Wan, Boxin Shi
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed method achieves superior performance from the experiments on a newly collected dataset compared with state-of-the-art methods. We report quantitative performance using PSNR, SSIM and LPIPS [36]. We conduct several experiments to evaluate the benefits of these two parts. |
| Researcher Affiliation | Academia | Chengxuan Zhu Nat l Eng. Research Center of Visual Technology School of Computer Science Peking University peterzhu@pku.edu.cn Renjie Wan Department of Computer Science Hong Kong Baptist University renjiewan@comp.hkbu.edu.hk Boxin Shi Nat l Eng. Research Center of Visual Technology School of Computer Science Peking University shiboxin@pku.edu.cn |
| Pseudocode | No | The paper describes its method through text and mathematical equations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and data is available at https://github.com/FreeButUselessSoul/TNeRF. |
| Open Datasets | Yes | Our experiments are based on a real-world dataset we collect. This dataset contains 8 different real-world scenes, each consisting of 20 to 30 mixture images with different poses. Specifically, 4 scenes are with the ground truth for quantitative evaluations in the experiments. [...] Our code and data is available at https://github.com/FreeButUselessSoul/TNeRF. We also test our network on the LLFF dataset [35] and the RFFR dataset [32]. |
| Dataset Splits | No | The paper states 'All the results in Section 5 are obtained using six views for training' which indicates training data, but it does not specify explicit train/validation/test dataset splits, percentages, or sample counts for validation. |
| Hardware Specification | Yes | We optimize a single model for about 100K iterations on two NVIDIA V100 GPUs. |
| Software Dependencies | No | The paper states 'We implement our framework using Py Torch', but it does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | In the training and testing phase, two eight-layer MLPs with 256 channels are used to predict colors c and densities σ corresponding to the transmitted and reflection scenes. We train a coarse network along with a fine network network for importance sampling. We sample 64 points along each ray in the coarse model and 64 points in the fine model. A batch contains an image patch of 32 32 pixels, equivalent to 1024 rays. Similar to the settings in Ne RF [1], positional encoding is applied to input location before they are passed into the MLPs. We use the Adam optimizer with defaults values β1 = 0.999, β2 = 0.9, ε = 10 8, and a learning rate 10 4 that decays following the cosine scheduler during the optimization. We optimize a single model for about 100K iterations on two NVIDIA V100 GPUs. |