Class-agnostic Reconstruction of Dynamic Objects from Videos
Authors: Zhongzheng Ren, Xiaoming Zhao, Alex Schwing
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study the efficacy of REDO in extensive experiments on synthetic RGBD video datasets SAIL-VOS 3D and Deforming Things4D++, and on real-world video data 3DPW. We find REDO outperforms state-of-the-art dynamic reconstruction methods by a margin. In ablation studies we validate each developed component. |
| Researcher Affiliation | Academia | Zhongzheng Ren , Xiaoming Zhao , Alexander G. Schwing University of Illinois at Urbana-Champaign |
| Pseudocode | No | The paper describes the steps of its method in paragraph form and through diagrams, but it does not include any clearly labeled pseudocode blocks or algorithms. |
| Open Source Code | Yes | https://jason718.github.io/redo |
| Open Datasets | Yes | We study the efficacy of REDO in extensive experiments on synthetic RGBD video datasets SAIL-VOS 3D and Deforming Things4D++, and on real-world video data 3DPW. |
| Dataset Splits | Yes | For evaluation, we sample 291 clips from 78 validation videos. We further hold out 2 classes (dog and gorilla) as an unseen test set. ... For evaluation, we create a validation set of 152 clips and a test set of 347 clips. ... The dataset contains 60 videos (24 training, 12 validation, and 24 testing). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'neural-ODE solver,' but it does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In each training iteration we sample 2048 query points for shape reconstruction and 512 vertices for learning of temporal coherence. We train REDO end-to-end using the Adam optimizer [39] for 60 epochs with a batch size of 8. The learning rate is initialized to 0.0001 and decayed by 10 at the 40th and 55th epochs. |