Hierarchical Normalization for Robust Monocular Depth Estimation
Authors: Chi Zhang, Wei Yin, Billzb Wang, Gang Yu, BIN FU, Chunhua Shen
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments show that the proposed normalization strategy remarkably outperforms previous normalization methods, and we set new state-of-the-art on five zero-shot transfer benchmark datasets. |
| Researcher Affiliation | Collaboration | Chi Zhang1, Wei Yin2, Zhibin Wang1, Gang Yu1 , Bin Fu1, Chunhua Shen3 1Tecent PCG, China 2DJI Technology, China 3Zhejiang University, China |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the described methodology. |
| Open Datasets | Yes | We follow Le Re S [37] to construct a mix-data training set, which includes 114K images from Taskonomy dataset, 121K images from DIML dataset, 48K images from Holopix50K, and 20K images from HRWSI [33]. |
| Dataset Splits | Yes | We withhold 1K images from all datasets for validation during training. |
| Hardware Specification | Yes | The model is trained on 8 V100 GPU with the batch size of 32. |
| Software Dependencies | No | The paper mentions using "DPTHybrid [23]" but does not specify software versions for libraries, frameworks, or programming languages (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We use the Adam optimizer with a learning rate of 10−5. The model is trained on 8 V100 GPU with the batch size of 32. In each mini-batch, we sample the equal number of images from different training data sources. For HDN in the spatial domain, we select the grid size Sspatial from {20, 21, 22, 23} to construct the hierarchical contexts. For HDN in depth domain, the group number Sdepth is chosen from {20, 21, 22}. |