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..
Crafting Monocular Cues and Velocity Guidance for Self-Supervised Multi-Frame Depth Learning
Authors: Xiaofeng Wang, Zheng Zhu, Guan Huang, Xu Chi, Yun Ye, Ziwei Chen, Xingang Wang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show MOVEDepth achieves state-of-the-art performance: Compared with Monodepth2 and Pack Net, our method relatively improves the depth accuracy by 20% and 19.8% on the KITTI benchmark. MOVEDepth also generalizes to the more challenging DDAD benchmark, relatively outperforming Many Depth by 7.2%. |
| Researcher Affiliation | Collaboration | Xiaofeng Wang1,2, Zheng Zhu3, Guan Huang3, Xu Chi3, Yun Ye3, Ziwei Chen4, Xingang Wang1* 1 Institute of Automation, Chinese Academy of Sciences 2 School of Artificial Intelligence, University of Chinese Academy of Science 3 Phi Gent Robotics 4 Southeast University |
| Pseudocode | No | The paper describes methods in text and with diagrams but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | The code is available at https://github.com/ Jeff Wang987/MOVEDepth. |
| Open Datasets | Yes | MOVEDepth is evaluated on KITTI (Geiger, Lenz, and Urtasun 2012) and DDAD (Guizilini et al. 2019) to verify the effectiveness. |
| Dataset Splits | Yes | Following from the Eigen split (Eigen and Fergus 2014), with data preprocessing from (Zhou et al. 2017), the data is divided into 39810/4424/697 training, validation and test images. |
| Hardware Specification | Yes | MOVEDepth is trained on 4 NVIDIA RTX 3090 GPUs with batch size 6 on each GPU. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers, such as specific deep learning frameworks (e.g., PyTorch, TensorFlow) or their versions, or Python version. |
| Experiment Setup | Yes | MOVEDepth is trained with an input resolution of 640 192 (KITTI) and 640 384 (DDAD). We only use two frames {It 1, It} for cost volume construction, and use {It 1, It, It+1} for reprojection loss. We train MOVEDepth for 20 epochs and optimize it with Adam (Kingma and Ba 2015). The learning rate is initially set as 0.0002, which decays by a factor of 10 for the final 5 epochs. Following (Godard et al. 2018; Watson et al. 2021), the loss weight γ is set as 0.001, and λi(i {1,2,3}) = 1. For MVS cost volume construction, the number of depth candidates is 16, group correlation G = 16, and β = 0.15. |