DME: Unveiling the Bias for Better Generalized Monocular Depth Estimation
Authors: Songsong Yu, Yifan Wang, Yunzhi Zhuge, Lijun Wang, Huchuan Lu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that DME achieves state-of-the-art performance on both NYU-Depth v2 and KITTI, and also delivers favorable zero-shot generalization capability on unseen datasets. |
| Researcher Affiliation | Academia | Songsong Yu, Yifan Wang, Yunzhi Zhuge, Lijun Wang*, Huchuan Lu Dalian University of Technology 22209083@mail.dlut.edu.cn, {wyfan, ljwang, lhchuan}@dlut.edu.cn, zgyzzgyz@gmail.com |
| Pseudocode | No | The paper describes its methods in prose and through diagrams (Figure 3), but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code can be obtained at https://github.com/YUsong360/DME-Unveilingthe-bias. |
| Open Datasets | Yes | Our training data consists of NYUD v2 (Silberman et al. 2012) and KITTI (Geiger et al. 2013) training datasets. |
| Dataset Splits | No | The paper mentions using NYUD v2 and KITTI datasets for training and testing, but does not explicitly state the training/validation/test splits (e.g., percentages, sample counts, or specific predefined split files) for reproducibility. |
| Hardware Specification | Yes | The training process is performed on one NVIDIA Ge Force RTX 3090Ti GPU, taking about 20 hours in total. |
| Software Dependencies | No | The paper mentions the use of the Adam optimizer but does not specify version numbers for any software, libraries, or frameworks used (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | During training, the Adam optimizer (Kingma and Ba 2014) is employed with a batch size of 2 and a weight decay of 1e-2. The initial learning rate is set to 3e-4. |