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..
VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids
Authors: Albert Pumarola, Artsiom Sanakoyeu, Lior Yariv, Ali Thabet, Yaron Lipman
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we extensively evaluate Vis Co grids. First, we evaluate on two standard surface reconstruction benchmarks [46, 22] (Sec. 4.1) against a large variety of state-of-the-art methods: Poisson Surface Reconstruction [24], DGP [46], IGR [18], SIREN [43], FFN [45], NSP [47], PHASE [27], GD [14], BPA [8], SPSR [25], RIMLS [34], SALD [5], IGR [19], Occ Net [30], Deep SDF [36], LIG [23], Points2Surf [17], DSE [38], IMLSNet [28] and Parse Net [42]. We then perform an ablation study (Sec. 4.2), and conduct a detail examination of the main components of the model, namely the viscosity and coarea losses. Finally, we discuss the model s ability to reconstruct sparse point clouds (Sec. 4.3) using scans from Stanford 3D Scanning Repository. |
| Researcher Affiliation | Collaboration | 1Meta AI, 2Weizmann Institute of Science |
| Pseudocode | No | The paper describes methods and formulas but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | No explicit statement about open-sourcing code or providing a repository link for the methodology. |
| Open Datasets | Yes | We next evaluated our model on two benchmarks: Surface Reconstruction Benchmark [46] and Surface Reconstruction from Real-Scans [22]. |
| Dataset Splits | No | The paper mentions using 'standard' benchmarks and that 'we use same hyper-paramenters for all meshes of all benchmarks', but it does not specify exact train/validation/test splits (e.g., percentages or counts) or provide citations to predefined splits. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, or memory) used for experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper. |
| Experiment Setup | No | The paper mentions key hyperparameters of the loss function (λp, λn, λv, λc) and discusses varying ϵ and λc in ablation studies, but it does not provide specific values for all hyperparameters, training configurations, or system-level settings required for full experimental setup reproduction (e.g., learning rate, optimizer, batch size, epochs, etc.). |