VectorAdam for Rotation Equivariant Geometry Optimization
Authors: Selena Zihan Ling, Nicholas Sharp, Alec Jacobson
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now compare the performance of Adam and Vector Adam in a range of optimization problems in machine learning and traditional geometric optimization context. These experiments demonstrate the undesirable coordinate-system bias and artifacts caused by Adam s per-coordinate update rules, and show how Vector Adam solves this problem while achieving equivalent or even improved quality and convergence rate. All experiments are performed in PyTorch on an NVIDIA RTX 2080 GPU. |
| Researcher Affiliation | Collaboration | Selena Ling University of Toronto selena.ling@mail.utoronto.ca Nicholas Sharp University of Toronto nsharp@cs.toronto.edu Alec Jacobson University of Toronto, Adobe Research jacobson@cs.toronto.edu |
| Pseudocode | Yes | Algorithm 1 Vector Adam: minimizing f over r parameters, each an n-dimensional vector. |
| Open Source Code | No | The paper does not provide any concrete access to source code, repository links, or explicit statements about code release for the methodology described. |
| Open Datasets | Yes | Here, we trained a point cloud classifier from the rotation-equivariant Vector Neuron architecture [4] on the Model Net40 dataset [2]... using the widely-adopted Point Net architecture [24], in which we train the network using both Vector Adam and conventional Adam on a subset of Shape Net[33] dataset. |
| Dataset Splits | No | The paper mentions training and testing on datasets like ModelNet40 and ShapeNet, but does not specify the exact training, validation, and test splits (e.g., percentages, sample counts, or explicit splitting methodology). |
| Hardware Specification | Yes | All experiments are performed in PyTorch on an NVIDIA RTX 2080 GPU. |
| Software Dependencies | No | The paper mentions 'PyTorch' as the software used ('All experiments are performed in PyTorch'), but does not specify a version number for it or any other key software dependencies. |
| Experiment Setup | Yes | For geometry reconstruction, we run the same experiments as in [19], which optimize a sphere to match against rendering of a Nerfertiti bust. More specifically, we run the optimization 8 times with different learning rates in the range of 9e-6 and 1e-5... On the left, we optimize the ARAP energy for a triangle mesh with 200 vertices for 100 iterations with 1000 randomly rotated initial configurations. |