We lifted 4 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 |
|---|---|---|
| chowkamlee81/CentrePointNet | pwc_unofficial | 2 of 3 |
| safetylab24/FusionCVCP | pwc_unofficial | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| children | Ran | safetylab24/FusionCVCP/det3d/builder.py code served (permissive licence) · get_code("68d8eb1ee0915c7d") |
| make_dot | Ran | chowkamlee81/CentrePointNet/det3d/visualization/netviz.py code served (permissive licence) · get_code("78f63746495b109f") |
| replace | Ran | chowkamlee81/CentrePointNet/det3d/visualization/netviz.py code served (permissive licence) · get_code("34a445a4ab172be6") |
| parse | Not yet run | chowkamlee81/CentrePointNet/det3d/visualization/netviz.py code served (permissive licence) · get_code("50ded6a49b80467e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
High-efficiency point cloud 3D object detection operated on embedded systems is important for many robotics applications including autonomous driving. Most previous works try to solve it using anchor-based detection methods which come with two drawbacks: post-processing is relatively complex and computationally expensive; tuning anchor parameters is tricky. We are the first to address these drawbacks with an anchor free and Non-Maximum Suppression free one stage detector called AFDet. The entire AFDet can be processed efficiently on a CNN accelerator or a GPU with the simplified post-processing. Without bells and whistles, our proposed AFDet performs competitively with other one stage anchor-based methods on KITTI validation set and Waymo Open Dataset validation set.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2006.12671")
get_code_for_paper("2006.12671")
have("2006.12671")
Connect an agent — have() is free.