Min Sun, Ning-Hsu Wang, Chi-Wei Hsiao, Hwann-Tzong Chen, Cheng Sun
We lifted 7 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| sunset1995/panoplane360 | — | 5 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| Bin_Mean_Shift | Ran | sunset1995/panoplane360/models/pano_plane_360.py pointer only (licence: NONE) · get_code("7c25ac9747c0fb7c") |
| FPN_refine | Ran | sunset1995/panoplane360/models/pano_plane_360.py pointer only (licence: NONE) · get_code("ce5b6bddb078776e") |
| find_peaks | Ran | sunset1995/panoplane360/models/pano_plane_360.py pointer only (licence: NONE) · get_code("33c8f55017005e9d") |
| radius_loss | Ran | sunset1995/panoplane360/models/pano_plane_360.py pointer only (licence: NONE) · get_code("a4213b01aa248670") |
| segement_by_rad | Ran | sunset1995/panoplane360/models/pano_plane_360.py pointer only (licence: NONE) · get_code("d510afcba3002ede") |
| Net | Not yet run | sunset1995/panoplane360/models/pano_plane_360.py pointer only (licence: NONE) · get_code("77a4b881d28a3400") |
| hinge_embedding_loss | Not yet run | sunset1995/panoplane360/models/pano_plane_360.py pointer only (licence: NONE) · get_code("a21a95354281c4f1") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Indoor panorama typically consists of human-made structures parallel or perpendicular to gravity. We leverage this phenomenon to approximate the scene in a 360-degree image with (H)orizontal-planes and (V)ertical-planes. To this end, we propose an effective divide-and-conquer strategy that divides pixels based on their plane orientation estimation; then, the succeeding instance segmentation module conquers the task of planes clustering more easily in each plane orientation group. Besides, parameters of V-planes depend on camera yaw rotation, but translation-invariant CNNs are less aware of the yaw change. We thus propose a yaw-invariant V-planar reparameterization for CNNs to learn. We create a benchmark for indoor panorama planar reconstruction by extending existing 360 depth datasets with ground truth H&V-planes (referred to as "PanoH&V" dataset) and adopt state-of-the-art planar reconstruction methods to predict H&V-planes as our baselines. Our method outperforms the baselines by a large margin on the proposed dataset.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2106.14166")
get_code_for_paper("2106.14166")
have("2106.14166")
Connect an agent — have() is free.