Bo Yang, Ziyang Song
We lifted 20 functions out of this paper's own repositories and ran 13 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 |
|---|---|---|
| vLAR-group/OGC | canonical | 0 of 2 |
| vlar-group/ogc | — | 13 of 18 |
| Function | Status | Where it lives |
|---|---|---|
| BatchNorm1d | Ran | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("7d9e410588d38e38") |
| BatchNorm2d | Ran | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("ea90589983c7d4fb") |
| Conv2d | Ran | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("66590da846c5ae6d") |
| FC | Ran | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("d14c0948cfdb965a") |
| GroupNorm | Ran | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("a005fafa886ca6c7") |
| MaskFormerHead | Ran | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("20e3a97f6963192c") |
| Seq | Ran | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("3d9e9e52517e5797") |
| SharedMLP | Ran | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("f38f770e896d67f3") |
| TransformerDecoderLayer | Ran | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("19e06e5da5917030") |
| _BNBase | Ran | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("359fcb0c4f682234") |
| _ConvBase | Ran | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("6cec49255b1f095e") |
| _PointnetSAModuleBase | Ran | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("6597c3e0acab9f29") |
| get_norm_layer | Ran | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("c1467b825cc365c5") |
| Conv1d | Not yet run | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("815a433370418f73") |
| MaskFormer3D | Not yet run | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("5d260efae3a9b405") |
| PointnetFPModule | Not yet run | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("5e62857933c0d4e6") |
| PointnetSAModule | Not yet run | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("2336b94be0832500") |
| PointnetSAModuleMSG | Not yet run | vlar-group/ogc/models/segnet_ogcdr.py pointer only (licence: NOASSERTION) · get_code("af2fc6415208e147") |
| bn_curve | Not yet run | vLAR-group/OGC/train_seg.py pointer only (licence: NOASSERTION) · get_code("accfe94474ce33c2") |
| lr_curve | Not yet run | vLAR-group/OGC/train_seg.py pointer only (licence: NOASSERTION) · get_code("fd2cd55cd5c23e2d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we study the problem of 3D object segmentation from raw point clouds. Unlike all existing methods which usually require a large amount of human annotations for full supervision, we propose the first unsupervised method, called OGC, to simultaneously identify multiple 3D objects in a single forward pass, without needing any type of human annotations. The key to our approach is to fully leverage the dynamic motion patterns over sequential point clouds as supervision signals to automatically discover rigid objects. Our method consists of three major components, 1) the object segmentation network to directly estimate multi-object masks from a single point cloud frame, 2) the auxiliary self-supervised scene flow estimator, and 3) our core object geometry consistency component. By carefully designing a series of loss functions, we effectively take into account the multi-object rigid consistency and the object shape invariance in both temporal and spatial scales. This allows our method to truly discover the object geometry even in the absence of annotations. We extensively evaluate our method on five datasets, demonstrating the superior performance for object part instance segmentation and general object segmentation in both indoor and the challenging outdoor scenarios. Our code and data are available at https://github.com/vLAR-group/OGC
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2210.04458")
get_code_for_paper("2210.04458")
have("2210.04458")
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