We lifted 1 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| woodwindhu/rangeldm | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| compute_snr | Ran | woodwindhu/rangeldm/ldm/train_unconditional.py code served (permissive licence) · get_code("697095b278e89253") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Autonomous driving demands high-quality LiDAR data, yet the cost of physical LiDAR sensors presents a significant scaling-up challenge. While recent efforts have explored deep generative models to address this issue, they often consume substantial computational resources with slow generation speeds while suffering from a lack of realism. To address these limitations, we introduce RangeLDM, a novel approach for rapidly generating high-quality range-view LiDAR point clouds via latent diffusion models. We achieve this by correcting range-view data distribution for accurate projection from point clouds to range images via Hough voting, which has a critical impact on generative learning. We then compress the range images into a latent space with a variational autoencoder, and leverage a diffusion model to enhance expressivity. Additionally, we instruct the model to preserve 3D structural fidelity by devising a range-guided discriminator. Experimental results on KITTI-360 and nuScenes datasets demonstrate both the robust expressiveness and fast speed of our LiDAR point cloud generation.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2403.10094")
get_code_for_paper("2403.10094")
have("2403.10094")
Connect an agent — have() is free.