We lifted 3 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| ylwhxht/v2x-r | canonical | 0 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| one_hot_f | Not yet run | ylwhxht/v2x-r/opencood/loss/point_pillar_loss_coalign.py pointer only (licence: NONE) · get_code("3508d3e1819272d8") |
| softmax_cross_entropy_with_logits | Not yet run | ylwhxht/v2x-r/opencood/loss/point_pillar_loss_coalign.py pointer only (licence: NONE) · get_code("44590278c8d435af") |
| weighted_smooth_l1_loss | Not yet run | ylwhxht/v2x-r/opencood/loss/point_pillar_loss_coalign.py pointer only (licence: NONE) · get_code("3318bc2d0e244104") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Current Vehicle-to-Everything (V2X) systems have significantly enhanced 3D object detection using LiDAR and camera data. However, these methods suffer from performance degradation in adverse weather conditions. The weather-robust 4D radar provides Doppler and additional geometric information, raising the possibility of addressing this challenge. To this end, we present V2X-R, the first simulated V2X dataset incorporating LiDAR, camera, and 4D radar. V2X-R contains 12,079 scenarios with 37,727 frames of LiDAR and 4D radar point clouds, 150,908 images, and 170,859 annotated 3D vehicle bounding boxes. Subsequently, we propose a novel cooperative LiDAR-4D radar fusion pipeline for 3D object detection and implement it with various fusion strategies. To achieve weather-robust detection, we additionally propose a Multi-modal Denoising Diffusion (MDD) module in our fusion pipeline. MDD utilizes weather-robust 4D radar feature as a condition to prompt the diffusion model to denoise noisy LiDAR features. Experiments show that our LiDAR-4D radar fusion pipeline demonstrates superior performance in the V2X-R dataset. Over and above this, our MDD module further improved the performance of basic fusion model by up to 5.73%/6.70% in foggy/snowy conditions with barely disrupting normal performance. The dataset and code will be publicly available at: https://github.com/ylwhxht/V2X-R.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2411.08402")
get_code_for_paper("2411.08402")
have("2411.08402")
Connect an agent — have() is free.