We lifted 11 functions out of this paper's own repositories and ran 8 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 |
|---|---|---|
| SAmmarAbbas/birds-eye-view | canonical | 8 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| get_abcline_from_two_points | Ran | SAmmarAbbas/birds-eye-view/utils/geometry.py code served (permissive licence) · get_code("fe63b14b45f4dc1b") |
| get_point_on_2pointline_normal_to_3rdpoint | Ran | SAmmarAbbas/birds-eye-view/utils/projection.py code served (permissive licence) · get_code("cae34523b46e1666") |
| get_point_on_sphere_normal_to_plane | Ran | SAmmarAbbas/birds-eye-view/utils/projection.py code served (permissive licence) · get_code("1515b9ba2356962c") |
| get_projection_on_sphere | Ran | SAmmarAbbas/birds-eye-view/utils/projection.py code served (permissive licence) · get_code("731aee05bcf54013") |
| get_slope_intercept_from_abc_line | Ran | SAmmarAbbas/birds-eye-view/utils/geometry.py code served (permissive licence) · get_code("420ab89b99034648") |
| resize_image_with_vps | Ran | SAmmarAbbas/birds-eye-view/utils/images.py code served (permissive licence) · get_code("24f28b7c4fff4754") |
| scale_abcline | Ran | SAmmarAbbas/birds-eye-view/utils/geometry.py code served (permissive licence) · get_code("f6f639da7e199940") |
| wrapped_partial | Ran | SAmmarAbbas/birds-eye-view/nets/mobilenet_v1.py code served (permissive licence) · get_code("b6e06004f7efe660") |
| preprocess_for_eval | Not yet run | SAmmarAbbas/birds-eye-view/preprocessing/vgg_preprocessing.py code served (permissive licence) · get_code("9d85e040f6eb6b96") |
| preprocess_for_train | Not yet run | SAmmarAbbas/birds-eye-view/preprocessing/vgg_preprocessing.py code served (permissive licence) · get_code("e5e1627b9d206d2e") |
| preprocess_image | Not yet run | SAmmarAbbas/birds-eye-view/preprocessing/vgg_preprocessing.py code served (permissive licence) · get_code("bc061bfb32babd6d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The objective of this paper is to rectify any monocular image by computing a homography matrix that transforms it to a bird's eye (overhead) view. We make the following contributions: (i) we show that the homography matrix can be parameterised with only four parameters that specify the horizon line and the vertical vanishing point, or only two if the field of view or focal length is known; (ii) We introduce a novel representation for the geometry of a line or point (which can be at infinity) that is suitable for regression with a convolutional neural network (CNN); (iii) We introduce a large synthetic image dataset with ground truth for the orthogonal vanishing points, that can be used for training a CNN to predict these geometric entities; and finally (iv) We achieve state-of-the-art results on horizon detection, with 74.52% AUC on the Horizon Lines in the Wild dataset. Our method is fast and robust, and can be used to remove perspective distortion from videos in real time.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1905.02231")
get_code_for_paper("1905.02231")
have("1905.02231")
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