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..
MetaSDF: Meta-Learning Signed Distance Functions
Authors: Vincent Sitzmann, Eric Chan, Richard Tucker, Noah Snavely, Gordon Wetzstein
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study properties of different generalization methods on 2D signed distance functions (SDFs) extracted from the MNIST dataset. From every MNIST digit, we extract a 2D SDF via a distance transform, such that the contour of the digit is the zero-level set of the corresponding SDF, see Fig. 2. Following [1], we directly fit the SDF of the MNIST digit via a fully connected neural network. We benchmark three alternative generalization approaches. |
| Researcher Affiliation | Collaboration | Vincent Sitzmann Stanford University EMAIL Eric R. Chan Stanford University EMAIL Richard Tucker Google Research EMAIL Noah Snavely Google Research EMAIL Gordon Wetzstein Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1 Meta SDF: Gradient-based meta-learning of shape spaces |
| Open Source Code | Yes | All code and datasets will be made publicly available. |
| Open Datasets | Yes | We study properties of different generalization methods on 2D signed distance functions (SDFs) extracted from the MNIST dataset. From every MNIST digit, we extract a 2D SDF via a distance transform, such that the contour of the digit is the zero-level set of the corresponding SDF, see Fig. 2. Following [1], we directly fit the SDF of the MNIST digit via a fully connected neural network. We benchmark three alternative generalization approaches. |
| Dataset Splits | Yes | We train all models on SDFs of the full MNIST training set, providing supervision via a regular grid of 64 64 ground-truth SDF samples. For CNPs and the proposed approach, we train two models each, conditioned on either (1) the same 64 64 ground-truth SDF samples or (2) a set of 512 points sampled from the zero-level set. We then test all models to reconstruct SDFs from the unseen MNIST test set from these two different kinds of context. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions 'ADAM optimizer [40]' but does not provide specific version numbers for software dependencies like Python, PyTorch/TensorFlow, or CUDA. |
| Experiment Setup | Yes | All models are implemented as fully connected Re LU-MLPs with 256 hidden units and no normalization layers. Φ is implemented with four layers. The set encoder of CNPs similarly uses four layers. Hypernetworks are implemented with three layers as in [8]. The proposed approach performs 5 inner-loop update steps, where we initialize α as 1 10 1. All models are optimized using the ADAM optimizer [40] with a learning rate of 1 10 4. |