MetaSDF: Meta-Learning Signed Distance Functions
Authors: Vincent Sitzmann, Eric Chan, Richard Tucker, Noah Snavely, Gordon Wetzstein
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 sitzmann@cs.stanford.edu Eric R. Chan Stanford University erchan@cs.stanford.edu Richard Tucker Google Research richardt@google.com Noah Snavely Google Research snavely@google.com Gordon Wetzstein Stanford University gordon.wetzstein@stanford.edu |
| 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. |