We lifted 16 functions out of this paper's own repositories and ran 15 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 |
|---|---|---|
| zhixinwang/frustum-convnet | canonical | 15 of 16 |
| Function | Status | Where it lives |
|---|---|---|
| in_hull | Ran | zhixinwang/frustum-convnet/datasets/data_utils.py code served (permissive licence) · get_code("aab935d4b7b55c2c") |
| Conv1d | Ran | zhixinwang/frustum-convnet/models/common.py code served (permissive licence) · get_code("df9e498714c411f2") |
| Conv2d | Ran | zhixinwang/frustum-convnet/models/common.py code served (permissive licence) · get_code("ce58b42def83bfb4") |
| Conv3d | Ran | zhixinwang/frustum-convnet/models/common.py code served (permissive licence) · get_code("e0f1f2d3ccac5f2e") |
| adjust_coord_for_view | Ran | zhixinwang/frustum-convnet/datasets/check_utils.py code served (permissive licence) · get_code("3b86dc1730e8d1c1") |
| center_decode | Ran | zhixinwang/frustum-convnet/models/box_transform.py code served (permissive licence) · get_code("f47e771020b20fef") |
| collate_fn | Ran | zhixinwang/frustum-convnet/datasets/provider_sample.py code served (permissive licence) · get_code("0c5c08f1db6108da") |
| compute_alpha | Ran | zhixinwang/frustum-convnet/datasets/provider_sample.py code served (permissive licence) · get_code("8d0c2b6ba5dcbf2d") |
| extract_pc_in_box3d | Ran | zhixinwang/frustum-convnet/datasets/data_utils.py code served (permissive licence) · get_code("4b69faf22a81a1e4") |
| get_box3d_corners_helper | Ran | zhixinwang/frustum-convnet/models/model_util.py code served (permissive licence) · get_code("281f3748f309afc2") |
| huber_loss | Ran | zhixinwang/frustum-convnet/models/model_util.py code served (permissive licence) · get_code("f6eae958a22ae877") |
| rotate_pc_along_y | Ran | zhixinwang/frustum-convnet/datasets/data_utils.py code served (permissive licence) · get_code("fda77318c74af228") |
| size_decode | Ran | zhixinwang/frustum-convnet/models/box_transform.py code served (permissive licence) · get_code("9989d7e45fda73c4") |
| size_encode | Ran | zhixinwang/frustum-convnet/models/box_transform.py code served (permissive licence) · get_code("53a9950814898b9c") |
| smooth_l1_loss | Ran | zhixinwang/frustum-convnet/models/model_util.py code served (permissive licence) · get_code("ce156beb68c9daa3") |
| load_cfg | Not yet run | zhixinwang/frustum-convnet/configs/config.py code served (permissive licence) · get_code("a124059a5c42fe93") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this work, we propose a novel method termed \emph{Frustum ConvNet (F-ConvNet)} for amodal 3D object detection from point clouds. Given 2D region proposals in an RGB image, our method first generates a sequence of frustums for each region proposal, and uses the obtained frustums to group local points. F-ConvNet aggregates point-wise features as frustum-level feature vectors, and arrays these feature vectors as a feature map for use of its subsequent component of fully convolutional network (FCN), which spatially fuses frustum-level features and supports an end-to-end and continuous estimation of oriented boxes in the 3D space. We also propose component variants of F-ConvNet, including an FCN variant that extracts multi-resolution frustum features, and a refined use of F-ConvNet over a reduced 3D space. Careful ablation studies verify the efficacy of these component variants. F-ConvNet assumes no prior knowledge of the working 3D environment and is thus dataset-agnostic. We present experiments on both the indoor SUN-RGBD and outdoor KITTI datasets. F-ConvNet outperforms all existing methods on SUN-RGBD, and at the time of submission it outperforms all published works on the KITTI benchmark. Code has been made available at: {\url{https://github.com/zhixinwang/frustum-convnet}.}
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1903.01864")
get_code_for_paper("1903.01864")
have("1903.01864")
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