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 [1].
Neural SDF Flow for 3D Reconstruction of Dynamic Scenes
Authors: Wei Mao, Richard Hartley, Mathieu Salzmann, miaomiao Liu
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on real-world multi-view video datasets show that our reconstructions are better than those of the state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Australian National University EMAIL Richard Hartley Australian National University & Google EMAIL Mathieu Salzmann CVLab, EPFL & SDSC, Switzerland EMAIL Miaomiao Liu Australian National University EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/ wei-mao-2019/SDFFlow.git. |
| Open Datasets | Yes | We evaluate our method quantitativly on the CMU Panoptic dataset (Joo et al., 2017) and qualitatively on the Tensor4D dataset (Shao et al., 2023). |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits with percentages or sample counts for reproducibility of model training, but states 'we use all 10 camera views for training and only evaluate the meshes'. |
| Hardware Specification | Yes | We train our model for 2000 epochs, which takes around 7 days on 2 NVIDIA 4090 GPUs for ten 1920 1080 videos of 24 frames. |
| Software Dependencies | No | We implement our method using Pytorch (Paszke et al., 2017) and use the Adam optimizer (Kingma & Ba, 2014) to train our model. |
| Experiment Setup | Yes | The batch size is set to 1024. We use the second-order Runge-Kutta method to solve the integration in Equation 5. We train our model for 2000 epochs... The balancing weight λ is set to 0.1. |