VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids
Authors: Albert Pumarola, Artsiom Sanakoyeu, Lior Yariv, Ali Thabet, Yaron Lipman
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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.). |