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..
Always Clear Depth: Robust Monocular Depth Estimation Under Adverse Weather
Authors: Kui Jiang, Jing Cao, Zhaocheng Yu, Junjun Jiang, Jingchun Zhou
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our ACDepth surpasses md4all-DD by 2.50% for night scene and 2.61% for rainy scene on the nu Scenes dataset in terms of the abs Rel metric. |
| Researcher Affiliation | Academia | 1Harbin Institute of Technology 2Dalian Maritime University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods using mathematical equations and textual descriptions, but does not include any clearly labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include a link to a code repository or mention code in supplementary materials. |
| Open Datasets | Yes | In this study, the commonly used nu Scenes [Caesar et al., 2020] and Robot Car [Maddern et al., 2017] datasets are used for training and comparison. |
| Dataset Splits | Yes | Following [Gasperini et al., 2023], we adopt 15,129 generated samples (day-clear, day-rain, night) for training and 6,019 samples (including 4449 day-clear, 1088 rain, and 602 night) for testing. Robot Car is a large outdoor dataset collected in Oxford, UK. Following [Gasperini et al., 2023], we adopt 16,563 generated samples (day, night) for training and 1,411 samples (including 702 day 709 night) for testing. |
| Hardware Specification | Yes | All experiments are conducted on the same Res Net18 architecture [He et al., 2016]. We train the student model and teacher model on a single NVIDIA 3090 GPU with a batch size of 16, using the Adam optimizer. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and a ResNet18 architecture, but it does not specify any software names with version numbers (e.g., specific deep learning frameworks like PyTorch or TensorFlow, or their versions). |
| Experiment Setup | Yes | We train the student model and teacher model on a single NVIDIA 3090 GPU with a batch size of 16, using the Adam optimizer. We set initial learning rate to 5e-4, reducing it by a factor of 0.1 every 15 epoch. The student model are trained for 25 epochs. Following the experimental protocol of [Gasperini et al., 2023], we maintain identical hyperparameter settings for self-supervised learning. Through experimental validation of different parameter combinations, the weights for the loss functions are set to λ1 = 0.01, λ2 = 0.02. |