MeshSDF: Differentiable Iso-Surface Extraction
Authors: Edoardo Remelli, Artem Lukoianov, Stephan Richter, Benoit Guillard, Timur Bagautdinov, Pierre Baque, Pascal Fua
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use two different applications to validate our theoretical insight: Single-View Reconstruction via Differentiable Rendering and Physically-Driven Shape Optimization. In both cases our differentiable parameterization gives us an edge over state-of-the-art algorithms. |
| Researcher Affiliation | Collaboration | Edoardo Remelli 1 Artem Lukoianov 1,2 Stephan R. Richter 3 Benoît Guillard 1 Timur Bagautdinov 2 Pierre Baque 2 Pascal Fua 1 1CVLab, EPFL, {name.surname}@epfl.ch 2Neural Concept SA, {name.surname}@neuralconcept.com 3Intel Labs, {name.surname}@intel.com |
| Pseudocode | Yes | Algorithm 1: Mesh SDF Forward |
| Open Source Code | No | The paper cites PyTorch3D [38] with a link, but this is a third-party library they used. There is no explicit statement or link from the authors stating they are releasing their Mesh SDF code. |
| Open Datasets | Yes | We demonstrate that our method is straightforward to apply to this task and validate our approach on two standard datasets, namely Shape Net [6] and Pix3D [45]. |
| Dataset Splits | Yes | We used standard train/test splits along with the renderings provided in [56] for all the methods we tested. |
| Hardware Specification | No | No specific GPU, CPU, or other hardware details are mentioned for running the experiments. |
| Software Dependencies | No | The paper mentions 'Open Foam [18]' and 'PyTorch3D [38]' and 'Adam [23]', but does not provide specific version numbers for any of these software components as required. |
| Experiment Setup | Yes | In practice, we run 400 gradient descent iterations using Adam [23] and keep the z with the smallest Ltask as our final code vector. |