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..
Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes
Authors: Sidhanth Holalkere, David Bindel, Silvia Sellán, Alexander Terenin
ICML 2025 | Venue PDF | 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. |