We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| georg-bn/zz-net | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| create_loaders | Ran | georg-bn/zz-net/rotation_estimation_experiments/ZZNet.py code served (permissive licence) · get_code("f99e5f3875af4ff0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we are concerned with rotation equivariance on 2D point cloud data. We describe a particular set of functions able to approximate any continuous rotation equivariant and permutation invariant function. Based on this result, we propose a novel neural network architecture for processing 2D point clouds and we prove its universality for approximating functions exhibiting these symmetries. We also show how to extend the architecture to accept a set of 2D-2D correspondences as indata, while maintaining similar equivariance properties. Experiments are presented on the estimation of essential matrices in stereo vision.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2111.15341")
get_code_for_paper("2111.15341")
have("2111.15341")
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