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..
Neural Non-Rigid Tracking
Authors: Aljaz Bozic, Pablo Palafox, Michael Zollhöfer, Angela Dai, Justus Thies, Matthias Niessner
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments In the following, we evaluate our method quantitatively and qualitatively on both non-rigid tracking and non-rigid reconstruction. To this end, we use the Deep Deform dataset [4] for training, with the given 340-30-30 train-val-test split of RGB-D sequences. Both non-rigid tracking and reconstruction are evaluated on the hidden test set of the Deep Deform benchmark. |
| Researcher Affiliation | Academia | 1Technical University of Munich 2Stanford University |
| Pseudocode | Yes | Algorithm 1 Gauss-Newton Optimization |
| Open Source Code | Yes | We make our code available at https://github.com/Deformable Friends/Neural Tracking. |
| Open Datasets | Yes | To this end, we use the Deep Deform dataset [4] for training, with the given 340-30-30 train-val-test split of RGB-D sequences. |
| Dataset Splits | Yes | To this end, we use the Deep Deform dataset [4] for training, with the given 340-30-30 train-val-test split of RGB-D sequences. |
| Hardware Specification | Yes | We use an Intel Xeon 6240 Processor and an Nvidia RTX 2080Ti GPU. |
| Software Dependencies | No | The paper states, 'The non-rigid tracking module has been implemented using the Py Torch library [24],' and mentions 'PWC-Net model [30],' but it does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | The non-rigid tracking module has been implemented using the Py Torch library [24] and trained using stochastic gradient descent with momentum 0.9 and learning rate 10 5. We use a 10-factor learning rate decay every 10k iterations, requiring about 30k iterations in total for convergence, with a batch size of 4. For optimal performance, we first optimize the correspondence predictor Φφ with (λcorr, λgraph, λwarp) = (5, 5, 5), without the weighting function Ψψ. Afterwards, we optimize the weighting function parameters ψ with (λcorr, λgraph, λwarp) = (0, 1000, 1000), while keeping φ fixed. Finally, we fine-tune both φ and ψ together, with (λcorr, λgraph, λwarp) = (5, 5, 5). In our experiments we use (λ2D, λdepth, λreg) = (0.001, 1, 1). |