We lifted 9 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| public-bots/gaussianpretrain | canonical | 2 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| average_precision | Ran | public-bots/gaussianpretrain/mmdet3d/core/evaluation/indoor_eval.py code served (permissive licence) · get_code("8d35a41368d8dd47") |
| eval_det_cls | Ran | public-bots/gaussianpretrain/mmdet3d/core/evaluation/indoor_eval.py code served (permissive licence) · get_code("2283234eb1863906") |
| bbox3d2result | Not yet run | public-bots/gaussianpretrain/mmdet3d/core/bbox/transforms.py code served (permissive licence) · get_code("049f10575d6acf59") |
| bbox3d2roi | Not yet run | public-bots/gaussianpretrain/mmdet3d/core/bbox/transforms.py code served (permissive licence) · get_code("071c297bbb04127e") |
| bbox3d_mapping_back | Not yet run | public-bots/gaussianpretrain/mmdet3d/core/bbox/transforms.py code served (permissive licence) · get_code("cd7847cc58eb29bd") |
| box_camera_to_lidar | Not yet run | public-bots/gaussianpretrain/mmdet3d/core/bbox/box_np_ops.py code served (permissive licence) · get_code("96c8084488bd023b") |
| camera_to_lidar | Not yet run | public-bots/gaussianpretrain/mmdet3d/core/bbox/box_np_ops.py code served (permissive licence) · get_code("9e22984820c39868") |
| corners_nd | Not yet run | public-bots/gaussianpretrain/mmdet3d/core/bbox/box_np_ops.py code served (permissive licence) · get_code("5b1dacd1f125c08f") |
| eval_map_recall | Not yet run | public-bots/gaussianpretrain/mmdet3d/core/evaluation/indoor_eval.py code served (permissive licence) · get_code("3e68c67f05177852") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Self-supervised learning has made substantial strides in image processing, while visual pre-training for autonomous driving is still in its infancy. Existing methods often focus on learning geometric scene information while neglecting texture or treating both aspects separately, hindering comprehensive scene understanding. In this context, we are excited to introduce GaussianPretrain, a novel pre-training paradigm that achieves a holistic understanding of the scene by uniformly integrating geometric and texture representations. Conceptualizing 3D Gaussian anchors as volumetric LiDAR points, our method learns a deepened understanding of scenes to enhance pre-training performance with detailed spatial structure and texture, achieving that 40.6% faster than NeRF-based method UniPAD with 70% GPU memory only. We demonstrate the effectiveness of GaussianPretrain across multiple 3D perception tasks, showing significant performance improvements, such as a 7.05% increase in NDS for 3D object detection, boosts mAP by 1.9% in HD map construction and 0.8% improvement on Occupancy prediction. These significant gains highlight GaussianPretrain's theoretical innovation and strong practical potential, promoting visual pre-training development for autonomous driving. Source code will be available at https://github.com/Public-BOTs/GaussianPretrain
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2411.12452")
get_code_for_paper("2411.12452")
have("2411.12452")
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