Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes
Authors: Sidhanth Holalkere, David Bindel, Silvia Sellán, Alexander Terenin
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results show that our approach provides a cleaner, more-principled, and more-flexible stochastic surface reconstruction pipeline. 4. Experiments and Applications We now demonstrate the proposed approach empirically on a suite of example problems. |
| Researcher Affiliation | Academia | 1Cornell University 2Columbia University 3MIT. Correspondence to: Sidhanth Holalkere <EMAIL>. |
| Pseudocode | No | The paper describes methods in prose and mathematical formulations, but does not contain a dedicated 'Pseudocode' or 'Algorithm' section or block. |
| Open Source Code | Yes | Code: HTTPS://GITHUB.COM/SHOLALKERE/GEOSPSR. |
| Open Datasets | Yes | Meshes. The Armadillo, Bunny, Falcon, Scorpion, Springer, Tree, and Well meshes are from Oded Stein’s repository, at: ODEDSTEIN.COM/MESHES. The Dragon mesh is originally from the Stanford 3D Scanning Repository we use the version from Alec Jacobson’s repository, at: GITHUB.COM/ALECJACOBSON/COMMON-3D-TEST-MODELS/. |
| Dataset Splits | No | The paper discusses input data and test points but does not specify explicit training/test/validation dataset splits, percentages, or methodologies for data partitioning. |
| Hardware Specification | Yes | All reported timings are calculated on a machine running Ubuntu 20.04 with an Intel Xeon Silver 4316 CPU, 256GB RAM, and an Nvidia RTX A6000 GPU. |
| Software Dependencies | No | We implement our algorithm in Python using GPYTOOLBOX [29] for common geometry processing subroutines, JAX [7] for numerical computations, and render our results in Blender using BLENDERTOOLBOX [21]. |
| Experiment Setup | Yes | All of our results use the Matérn kernel with ν = 3/2 and length scales between 4 × 10^−2 and 1 × 10^−2, depending on the specific mesh. Unless specified otherwise, we use L = 100^3 functions for the cross-covariance and L˜ = 40^3 functions for the prior samples. The cross-covariance is amortized using a total of 50^3 points. |