Michael Bronstein, Justin Solomon, Ziwei Liu, Yue Wang, Yongbin Sun, Sanjay Sarma
We lifted 44 functions out of this paper's own repositories and ran 16 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 |
|---|---|---|
| vinits5/learning3d | — | 3 of 3 |
| hansen7/NRS_3D | — | 3 of 3 |
| hqucms/ParticleNet | — | 3 of 3 |
| princeton-vl/SimpleView | — | 2 of 2 |
| AmitBracha/GIP_project | — | 1 of 17 |
| af13s/dgcnn-amino | — | 1 of 12 |
| WangYueFt/dgcnn | — | 1 of 1 |
| brent-murray/tr3d_pointaugdgcnn | — | 1 of 1 |
| AnTao97/dgcnn.pytorch | — | 1 of 1 |
| nnn911/MLSI | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| DGCNN | Ran | princeton-vl/SimpleView/dgcnn/pytorch/model.py code served (permissive licence) · get_code("ad3b789a1f825e88") |
| DGCNN | Ran | WangYueFt/dgcnn/pytorch/model.py code served (permissive licence) · get_code("bd4d12ec0ab7479e") |
| DGCNN | Ran | brent-murray/tr3d_pointaugdgcnn/models/dgcnn.py pointer only (licence: NONE) · get_code("105ce77f02aa8fc3") |
| DGCNN | Ran | vinits5/learning3d/models/dgcnn.py code served (permissive licence) · get_code("f746806bd48762f6") |
| DGCNN | Ran | hansen7/NRS_3D/models/dgcnn_cls.py code served (permissive licence) · get_code("5225ebf366dff39c") |
| DGCNN_cls | Ran | AnTao97/dgcnn.pytorch/model.py code served (permissive licence) · get_code("deae149da586b0cb") |
| batch_distance_matrix_general | Ran | hqucms/ParticleNet/tf-keras/tf_keras_model.py code served (permissive licence) · get_code("4db0912e4435ab42") |
| edge_conv | Ran | hqucms/ParticleNet/tf-keras/tf_keras_model.py code served (permissive licence) · get_code("32c56f6033c6d2fb") |
| get_graph_feature | Ran | princeton-vl/SimpleView/dgcnn/pytorch/model.py code served (permissive licence) · get_code("9a8756778dd8234a") |
| get_graph_feature | Ran | vinits5/learning3d/models/dgcnn.py code served (permissive licence) · get_code("26b869462d7d37e6") |
| get_graph_feature | Ran | hansen7/NRS_3D/models/dgcnn_cls.py code served (permissive licence) · get_code("ffacb4286b9c5b79") |
| knn | Ran | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("afa67b67c8fbe518") |
| knn | Ran | af13s/dgcnn-amino/models/dgcnn.py pointer only (licence: NONE) · get_code("9000f8e3010b8eb7") |
| knn | Ran | hqucms/ParticleNet/tf-keras/tf_keras_model.py code served (permissive licence) · get_code("86fd210ea5960f23") |
| knn | Ran | vinits5/learning3d/models/dgcnn.py code served (permissive licence) · get_code("ee7b4d081c1fd7b3") |
| knn | Ran | hansen7/NRS_3D/models/dgcnn_cls.py code served (permissive licence) · get_code("999b1b3194e7ace2") |
| DGCNN_cls | Not yet run | nnn911/MLSI/src/MLSI/DG_CNN_tools.py pointer only (licence: NOASSERTION) · get_code("340df50c5fa6ae0a") |
| _variable_on_cpu | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("adf5846b3d969f1f") |
| _variable_on_cpu | Not yet run | af13s/dgcnn-amino/models/dgcnn.py pointer only (licence: NONE) · get_code("2918d47029a9f7b8") |
| _variable_with_weight_decay | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("469a43579e0ab430") |
| _variable_with_weight_decay | Not yet run | af13s/dgcnn-amino/models/dgcnn.py pointer only (licence: NONE) · get_code("095d661fc452344e") |
| batch_norm_dist_template | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("e836d5d02fbe1569") |
| batch_norm_dist_template | Not yet run | af13s/dgcnn-amino/models/dgcnn.py pointer only (licence: NONE) · get_code("bd5dd2c726337fbb") |
| batch_norm_for_conv2d | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("aa9412f2d47b5c71") |
| batch_norm_for_conv2d | Not yet run | af13s/dgcnn-amino/models/dgcnn.py pointer only (licence: NONE) · get_code("278e89613b2fcc9d") |
| batch_norm_for_fc | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("8563d3c5e43f3c8d") |
| batch_norm_for_fc | Not yet run | af13s/dgcnn-amino/models/dgcnn.py pointer only (licence: NONE) · get_code("3e5a9afaceaecb8e") |
| batch_norm_template | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("df7b6d675ad1faf4") |
| conv2d | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("5caf714eb8eb5aa5") |
| conv2d | Not yet run | af13s/dgcnn-amino/models/dgcnn.py pointer only (licence: NONE) · get_code("2f2a5221e029473a") |
| dropout | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("a7e0217e7e46649e") |
| fully_connected | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("65878b612b525cd9") |
| fully_connected | Not yet run | af13s/dgcnn-amino/models/dgcnn.py pointer only (licence: NONE) · get_code("40bf1749410aa96f") |
| get_edge_feature | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("f7b114580fc34243") |
| get_edge_feature | Not yet run | af13s/dgcnn-amino/models/dgcnn.py pointer only (licence: NONE) · get_code("afb21d6165856bb0") |
| get_edge_feature_fc_momentum | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("a3b270bbc3562938") |
| get_model | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("aa032686f869c6ca") |
| get_model | Not yet run | af13s/dgcnn-amino/models/dgcnn.py pointer only (licence: NONE) · get_code("b15247159c9e74bf") |
| get_tensor_second_momentum | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("3554079d199ef44d") |
| input_transform_net | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("d07227089f4eb7c0") |
| input_transform_net | Not yet run | af13s/dgcnn-amino/models/dgcnn.py pointer only (licence: NONE) · get_code("c5549fd76a62a77a") |
| max_pool2d | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("5bd4731c81b94fda") |
| pairwise_distance | Not yet run | AmitBracha/GIP_project/models/dgcnn.py pointer only (licence: NONE) · get_code("c849266d759a5b88") |
| pairwise_distance | Not yet run | af13s/dgcnn-amino/models/dgcnn.py pointer only (licence: NONE) · get_code("3d90b05b13b0c0b9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed Edge-Conv suitable for CNN-based high-level tasks on point clouds including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks including ModelNet40, ShapeNetPart, and S3DIS. CCS Concepts: • Computing methodologies → Neural networks; Pointbased models; Shape analysis;
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1801.07829")
get_code_for_paper("1801.07829")
have("1801.07829")
Connect an agent — have() is free.