We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| chetanmreddy/voxelnet_chetan | reimplementation | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| backproject | Ran | chetanmreddy/voxelnet_chetan/mmdet3d/models/detectors/imvoxelnet.py code served (permissive licence) · get_code("14993714c473bfec") |
| get_extrinsics | Ran | chetanmreddy/voxelnet_chetan/mmdet3d/models/detectors/imvoxelnet.py code served (permissive licence) · get_code("c0d7a59ff5b66060") |
| get_points | Ran | chetanmreddy/voxelnet_chetan/mmdet3d/models/detectors/imvoxelnet.py code served (permissive licence) · get_code("b4ba32634504fe10") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we introduce the task of multi-view RGB-based 3D object detection as an end-to-end optimization problem. To address this problem, we propose ImVoxelNet, a novel fully convolutional method of 3D object detection based on monocular or multi-view RGB images. The number of monocular images in each multi-view input can variate during training and inference; actually, this number might be unique for each multi-view input. ImVoxelNet successfully handles both indoor and outdoor scenes, which makes it general-purpose. Specifically, it achieves state-of-the-art results in car detection on KITTI (monocular) and nuScenes (multi-view) benchmarks among all methods that accept RGB images. Moreover, it surpasses existing RGB-based 3D object detection methods on the SUN RGB-D dataset. On ScanNet, ImVoxelNet sets a new benchmark for multi-view 3D object detection. The source code and the trained models are available at https://github.com/saic-vul/imvoxelnet.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2106.01178")
get_code_for_paper("2106.01178")
have("2106.01178")
Connect an agent — have() is free.