We lifted 14 functions out of this paper's own repositories and ran 10 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 |
|---|---|---|
| mileyan/Pseudo_Lidar_V2 | canonical | 10 of 14 |
| Function | Status | Where it lives |
|---|---|---|
| dataloader | Ran | mileyan/Pseudo_Lidar_V2/src/dataloader/KITTILoader3D.py code served (permissive licence) · get_code("a3afe1d264a83296") |
| default_loader | Ran | mileyan/Pseudo_Lidar_V2/src/dataloader/KITTILoader_dataset3d.py code served (permissive licence) · get_code("ac269a0e4b8d946e") |
| depth2ptc | Ran | mileyan/Pseudo_Lidar_V2/gdc/depthmap2ptc.py code served (permissive licence) · get_code("5f6f750ca5ca52c6") |
| disparity_loader | Ran | mileyan/Pseudo_Lidar_V2/src/dataloader/KITTILoader_dataset3d.py code served (permissive licence) · get_code("8d9f5f6ce951aa15") |
| filter_height | Ran | mileyan/Pseudo_Lidar_V2/gdc/depthmap2ptc.py code served (permissive licence) · get_code("99d5136a35c630cc") |
| get_depth_map | Ran | mileyan/Pseudo_Lidar_V2/gdc/ptc2depthmap.py code served (permissive licence) · get_code("bdbf99917155442d") |
| is_image_file | Ran | mileyan/Pseudo_Lidar_V2/src/dataloader/KITTILoader3D.py code served (permissive licence) · get_code("ab4109634b75ef8b") |
| project_disp_to_depth | Ran | mileyan/Pseudo_Lidar_V2/src/preprocess/generate_lidar_from_depth.py code served (permissive licence) · get_code("33b00ba26319a75f") |
| pto_ang_map | Ran | mileyan/Pseudo_Lidar_V2/gdc/sparsify.py code served (permissive licence) · get_code("e4da94c7d28b4708") |
| setup_logger | Ran | mileyan/Pseudo_Lidar_V2/src/logger.py code served (permissive licence) · get_code("aeb4136902fecf2a") |
| filter_mask | Not yet run | mileyan/Pseudo_Lidar_V2/gdc/gdc.py code served (permissive licence) · get_code("e1d7378c4e2e8fce") |
| filter_theta_mask | Not yet run | mileyan/Pseudo_Lidar_V2/gdc/gdc.py code served (permissive licence) · get_code("c2ce62aec738dd30") |
| read_calib_file | Not yet run | mileyan/Pseudo_Lidar_V2/src/dataloader/KITTILoader_dataset3d_knn.py code served (permissive licence) · get_code("bf109c04c389ad3f") |
| subsample_mask_by_grid | Not yet run | mileyan/Pseudo_Lidar_V2/gdc/gdc.py code served (permissive licence) · get_code("0bb6694313ca5db7") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Detecting objects such as cars and pedestrians in 3D plays an indispensable role in autonomous driving. Existing approaches largely rely on expensive LiDAR sensors for accurate depth information. While recently pseudo-LiDAR has been introduced as a promising alternative, at a much lower cost based solely on stereo images, there is still a notable performance gap. In this paper we provide substantial advances to the pseudo-LiDAR framework through improvements in stereo depth estimation. Concretely, we adapt the stereo network architecture and loss function to be more aligned with accurate depth estimation of faraway objects --- currently the primary weakness of pseudo-LiDAR. Further, we explore the idea to leverage cheaper but extremely sparse LiDAR sensors, which alone provide insufficient information for 3D detection, to de-bias our depth estimation. We propose a depth-propagation algorithm, guided by the initial depth estimates, to diffuse these few exact measurements across the entire depth map. We show on the KITTI object detection benchmark that our combined approach yields substantial improvements in depth estimation and stereo-based 3D object detection --- outperforming the previous state-of-the-art detection accuracy for faraway objects by 40%. Our code is available at https://github.com/mileyan/Pseudo_Lidar_V2.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1906.06310")
get_code_for_paper("1906.06310")
have("1906.06310")
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