Jia Wu, Ziyang Chen, Wei Long, Yongjun Zhang, Bingshu Wang, Yongbin Qin, He Yao
We lifted 17 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 |
|---|---|---|
| zyangchen/mocha-stereo | canonical | 16 of 17 |
| Function | Status | Where it lives |
|---|---|---|
| build_gwc_volume | Ran | zyangchen/mocha-stereo/MoCha-Stereo/core/submodule.py code served (permissive licence) · get_code("b2535abc79c55bba") |
| conv1x1 | Ran | zyangchen/mocha-stereo/MoCha-Stereo/nets/feature.py code served (permissive licence) · get_code("8182b8e2a441abbd") |
| conv2d | Ran | zyangchen/mocha-stereo/MoCha-Stereo/nets/refinement.py code served (permissive licence) · get_code("2c4f90d71c073dfe") |
| conv3x3 | Ran | zyangchen/mocha-stereo/MoCha-Stereo/nets/feature.py code served (permissive licence) · get_code("9fba66e062846970") |
| conv5x5 | Ran | zyangchen/mocha-stereo/MoCha-Stereo/nets/feature.py code served (permissive licence) · get_code("af5d1e93b7cc0884") |
| convbn | Ran | zyangchen/mocha-stereo/MoCha-Stereo/core/extractor.py code served (permissive licence) · get_code("15ab3c76823aef7e") |
| count_parameters | Ran | zyangchen/mocha-stereo/MoCha-Stereo/evaluate_stereo.py code served (permissive licence) · get_code("f6b944f50d3f15ae") |
| default_conv | Ran | zyangchen/mocha-stereo/MoCha-Stereo/nets/refinement.py code served (permissive licence) · get_code("8b0e794d4d8f9b13") |
| disp_warp | Ran | zyangchen/mocha-stereo/MoCha-Stereo/nets/warp.py code served (permissive licence) · get_code("9797b88373fddbcd") |
| groupwise_correlation | Ran | zyangchen/mocha-stereo/MoCha-Stereo/core/submodule.py code served (permissive licence) · get_code("2e5c0e4dfa009847") |
| interp | Ran | zyangchen/mocha-stereo/MoCha-Stereo/core/update.py code served (permissive licence) · get_code("08bedbc0c0447f5d") |
| meshgrid | Ran | zyangchen/mocha-stereo/MoCha-Stereo/nets/warp.py code served (permissive licence) · get_code("d264efc1788f1659") |
| norm_correlation | Ran | zyangchen/mocha-stereo/MoCha-Stereo/core/submodule.py code served (permissive licence) · get_code("7d2e52593158ea33") |
| normalize_coords | Ran | zyangchen/mocha-stereo/MoCha-Stereo/nets/warp.py code served (permissive licence) · get_code("62da485d08a1a205") |
| pool2x | Ran | zyangchen/mocha-stereo/MoCha-Stereo/core/update.py code served (permissive licence) · get_code("25894ad2a73a8142") |
| weight | Ran | zyangchen/mocha-stereo/MoCha-Stereo/nets/mogrifier.py code served (permissive licence) · get_code("3ea274451c800fdf") |
| pool4x | Not yet run | zyangchen/mocha-stereo/MoCha-Stereo/core/update.py code served (permissive licence) · get_code("771e8ac695af28a9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Figure 1. Motivation. Addressing the issue of geometric structure loss in feature channels arising from deep learning. (From left to right: Input image, visualization of a Normal Channel, visualization of a Motif [39] Channel.) Due to the fuzziness of geometric edges in certain channels, achieving accurate matching of stereo image edges is a challenging problem. MoCha-Stereo guides ordinary channels to focus on edge features through motif channels, achieving more accurate detail matching. Motif Channel refers to channel that composed of repeatedly occurring geometric contours. The regions delineated by the yellow border represent the magnified details.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2404.06842")
get_code_for_paper("2404.06842")
have("2404.06842")
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