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..
Class-agnostic Reconstruction of Dynamic Objects from Videos
Authors: Zhongzheng Ren, Xiaoming Zhao, Alex Schwing
NeurIPS 2021 | Venue PDF | 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. |