We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| clinplayer/3DModelingRL | pwc_unofficial | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| read_as_3d_array | Ran | clinplayer/3DModelingRL/Mesh-Agent/utils/binvox_rw.py code served (permissive licence) · get_code("7dc0ccf56d156abb") |
| read_as_coord_array | Ran | clinplayer/3DModelingRL/Mesh-Agent/utils/binvox_rw.py code served (permissive licence) · get_code("d3b3bb299e8acb92") |
| read_header | Ran | clinplayer/3DModelingRL/Mesh-Agent/utils/binvox_rw.py code served (permissive licence) · get_code("ba966c4838f2c939") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We explore how to enable machines to model 3D shapes like human modelers using deep reinforcement learning (RL). In 3D modeling software like Maya, a modeler usually creates a mesh model in two steps: (1) approximating the shape using a set of primitives; (2) editing the meshes of the primitives to create detailed geometry. Inspired by such artist-based modeling, we propose a two-step neural framework based on RL to learn 3D modeling policies. By taking actions and collecting rewards in an interactive environment, the agents first learn to parse a target shape into primitives and then to edit the geometry. To effectively train the modeling agents, we introduce a novel training algorithm that combines heuristic policy, imitation learning and reinforcement learning. Our experiments show that the agents can learn good policies to produce regular and structure-aware mesh models, which demonstrates the feasibility and effectiveness of the proposed RL framework.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2003.12397")
get_code_for_paper("2003.12397")
have("2003.12397")
Connect an agent — have() is free.