We lifted 21 functions out of this paper's own repositories and ran 17 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 |
|---|---|---|
| sony/polar-densification | canonical | 17 of 21 |
| Function | Status | Where it lives |
|---|---|---|
| add_paths_from_globs | Ran | sony/polar-densification/dataloaders/polar_loader.py code served (permissive licence) · get_code("9e6404a0976e7a62") |
| adjust_learning_rate | Ran | sony/polar-densification/util/helper.py code served (permissive licence) · get_code("4abf45ccf439f11e") |
| calc_fourPolar_from_stokes | Ran | sony/polar-densification/util/polarutils_torch.py code served (permissive licence) · get_code("02cd5046b0f39835") |
| convbnrelu | Ran | sony/polar-densification/model/basic.py code served (permissive licence) · get_code("1ef4059f083c613e") |
| convrelu | Ran | sony/polar-densification/model/basic.py code served (permissive licence) · get_code("9b9b4801eb831ee9") |
| deconvbnrelu | Ran | sony/polar-densification/model/basic.py code served (permissive licence) · get_code("97961b6e9fbfdb11") |
| get_globs | Ran | sony/polar-densification/dataloaders/polar_loader.py code served (permissive licence) · get_code("83929b3a35f16cc7") |
| get_paths_from_globs | Ran | sony/polar-densification/dataloaders/polar_loader.py code served (permissive licence) · get_code("6c3db4142c7a43f9") |
| getmask | Ran | sony/polar-densification/util/polarutils.py code served (permissive licence) · get_code("192446ad94834c15") |
| getmask_conv | Ran | sony/polar-densification/util/polarutils.py code served (permissive licence) · get_code("ef89618b040ad0df") |
| getmask_quad | Ran | sony/polar-densification/util/polarutils.py code served (permissive licence) · get_code("a93b5b49471202de") |
| log10 | Ran | sony/polar-densification/metrics.py code served (permissive licence) · get_code("8ac65c61454e6081") |
| rad2deg | Ran | sony/polar-densification/util/polarutils_torch.py code served (permissive licence) · get_code("95d79315f9d21270") |
| search_checkpoint_best | Ran | sony/polar-densification/util/helper.py code served (permissive licence) · get_code("2f6b58f1024ca8a2") |
| search_checkpoint_latest | Ran | sony/polar-densification/util/helper.py code served (permissive licence) · get_code("f66932de834085d0") |
| select_refine_input | Ran | sony/polar-densification/model/model_sna.py code served (permissive licence) · get_code("11a92db2bda32ad9") |
| select_refine_input_ch | Ran | sony/polar-densification/model/model_sna.py code served (permissive licence) · get_code("27c611f3e591529c") |
| adjust_brightness | Not yet run | sony/polar-densification/dataloaders/transforms.py code served (permissive licence) · get_code("2d6e3abc415f30a8") |
| adjust_contrast | Not yet run | sony/polar-densification/dataloaders/transforms.py code served (permissive licence) · get_code("3d7396405403ab61") |
| adjust_saturation | Not yet run | sony/polar-densification/dataloaders/transforms.py code served (permissive licence) · get_code("59616bf6be4cce9b") |
| calc_dop_from_stokes | Not yet run | sony/polar-densification/util/polarutils_torch.py code served (permissive licence) · get_code("787ad37e60a7613e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper proposes a novel polarization sensor structure and network architecture to obtain a high-quality RGB image and polarization information. Conventional polarization sensors can simultaneously acquire RGB images and polarization information, but the polarizers on the sensor degrade the quality of the RGB images. There is a trade-off between the quality of the RGB image and polarization information as fewer polarization pixels reduce the degradation of the RGB image but decrease the resolution of polarization information. Therefore, we propose an approach that resolves the trade-off by sparsely arranging polarization pixels on the sensor and compensating for low-resolution polarization information with higher resolution using the RGB image as a guide. Our proposed network architecture consists of an RGB image refinement network and a polarization information compensation network. We confirmed the superiority of our proposed network in compensating the differential component of polarization intensity by comparing its performance with state-of-the-art methods for similar tasks: depth completion. Furthermore, we confirmed that our approach could simultaneously acquire higher quality RGB images and polarization information than conventional polarization sensors, resolving the trade-off between the quality of RGB images and polarization information. The baseline code and newly generated real and synthetic large-scale polarization image datasets are available for further research and development.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2209.13106")
get_code_for_paper("2209.13106")
have("2209.13106")
Connect an agent — have() is free.