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..
MeshSDF: Differentiable Iso-Surface Extraction
Authors: Edoardo Remelli, Artem Lukoianov, Stephan Richter, Benoit Guillard, Timur Bagautdinov, Pierre Baque, Pascal Fua
NeurIPS 2020 | Venue PDF | 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. |