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..
Deep Non-line-of-sight Imaging from Under-scanning Measurements
Authors: Yue Li, Yueyi Zhang, Juntian Ye, Feihu Xu, Zhiwei Xiong
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed method achieves superior performance on both synthetic data and public real-world data, as demonstrated by extensive experimental results with different under-scanning grids. |
| Researcher Affiliation | Academia | Yue Li Yueyi Zhang Juntian Ye Feihu Xu Zhiwei Xiong University of Science and Technology of China EMAIL EMAIL |
| Pseudocode | No | The paper describes the architecture and components of the network in text and diagrams (Figure 2), but it does not provide any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/Depth2World/Under-scanning_NLOS. |
| Open Datasets | Yes | For the synthetic dataset, we simulate the data using a transient renderer provided by [5], rendering 277 motorbikes from the Shape Net dataset [33]. ... For the real-world scenes, we adopt the real-world dataset provided by [14] which contains diverse objects. |
| Dataset Splits | No | The synthetic dataset is composed of 2712 training samples and 291 testing samples. The paper specifies training and testing samples but does not mention a distinct validation set or split. |
| Hardware Specification | Yes | All the experiments are conducted on a workstation equipped with 4 NVIDIA Ge Force A100 GPUs. |
| Software Dependencies | No | The proposed network is implemented in Py Torch. No specific version number for PyTorch or other software dependencies are provided. |
| Experiment Setup | Yes | The optimizer is Adam W [34] with a learning rate of 1 10 4 and a weight decay of 1 10 4. The models are trained on the synthetic dataset for 50 epochs with a batch size of 4, and directly tested on the real-world dataset, setting the high-spatial resolution to 128 128. For the loss function, the hyperparameters λ1, λ2, and λ3 is 1, 1 10 5, and 1 10 6. |