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..
OAMaskFlow: Occlusion-Aware Motion Mask for Scene Flow
Authors: Xiongfeng Peng, Zhihua Liu, Weiming Li, Yamin Mao, Qiang Wang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our proposed OAMask Flow has reduced the EPE3D metric by 21.0% on the Flying Things3D dataset and decreased SF-all metric by 24.3% on the KITTI scene flow benchmark than the baseline method RAFT-3D. |
| Researcher Affiliation | Industry | Samsung R&D Institute China-Beijing, China EMAIL |
| Pseudocode | No | The paper describes the method using equations and textual explanations, but does not include any explicit pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | To validate our OAMask Flow method, we perform experiments on the synthetic Flying Things3D (Mayer et al. 2016) and the real-world KITTI (Menze and Geiger 2015) datasets. Furthermore, we extensively apply OAM mask into DROID-SLAM framework (Teed and Deng 2021a) and make experiments on the synthetic Tartan Air SLAM challenge (Wang et al. 2020a) dataset to validate our proposed OAM mask effectiveness. |
| Dataset Splits | Yes | Following Flow Net3D (Liu, Qi, and Guibas 2019), approximately 2000 test examples are sampled from the Flying Things3D test set for evaluation. |
| Hardware Specification | Yes | We train OAM-DROID-SLAM network on four Nvidia 3090 GPUs with two stages on Tartan Air (Wang et al. 2020a) dataset. |
| Software Dependencies | No | The paper uses existing frameworks and methods like RAFT-3D and DROID-SLAM but does not specify software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions) for its own implementation. |
| Experiment Setup | Yes | We train our network for 200k iterations with a batch size of 4 and a crop size of [320, 720]. The initial learning rate is 2 10 4 and decays linearly during training. |