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..
Event-based HDR Structured Light
Authors: Jiacheng Fu, Yue Li, Xin Dong, Wenming Weng, Yueyi Zhang, Zhiwei Xiong
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the effectiveness of our method with the proposed confidence-driven strategy on both synthetic and real-world datasets. Experimental results demonstrate that our proposed HDR framework enables accurate 3D measurement even under extreme conditions. |
| Researcher Affiliation | Academia | Jiacheng Fu Yue Li Xin Dong Wenming Weng Yueyi Zhang Zhiwei Xiong University of Science and Technology of China EMAIL EMAIL |
| Pseudocode | No | The paper describes the methodology using text and diagrams (e.g., Figure 3: 'Pipeline of the Confidence-Driven Stereo Matching strategy', Figure 4: 'The overview of the proposed ECE (a) and CPV (b)'), but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and data are available at https://github.com/Quma233/Event-based-HDR-SL. |
| Open Datasets | Yes | Third, we contribute an event-based SL simulator and propose the first event-based HDR SL dataset. We also collect a real-world benchmarking dataset with ground truth. The code and data are available at https://github.com/Quma233/Event-based-HDR-SL. |
| Dataset Splits | Yes | We render a total of 8,550 image pairs at a resolution of 1280 704. The dataset is split into 7,500 training samples and 1,050 test samples. |
| Hardware Specification | Yes | All experiments are conducted on the NVIDIA 3090 GPUs. |
| Software Dependencies | No | Our HDR 3D reconstruction method is implemented in Py Torch, trained on a synthetic dataset, and evaluated on both synthetic and real-world data. The paper mentions 'Py Torch' but does not specify a version number or other software dependencies with specific version numbers. |
| Experiment Setup | Yes | For multi-contrast HDR coding, the projection intensities of the three pairs are set to [32,55], [32,200], and [0,255], respectively. The depth sensing rate of our system is 200 Hz. [...] The disparity search range is set to 256. |