Vitor Guizilini, Yue Wang, Haoxi Ran
We lifted 8 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| hancyran/lidar-diffusion | — | 5 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| AttnBlock | Ran | hancyran/lidar-diffusion/lidm/modules/diffusion/model_lidm.py code served (permissive licence) · get_code("d1c55266c7801e2b") |
| CircularConv2d | Ran | hancyran/lidar-diffusion/lidm/modules/diffusion/model_lidm.py code served (permissive licence) · get_code("99317db8781524bf") |
| Downsample | Ran | hancyran/lidar-diffusion/lidm/modules/diffusion/model_lidm.py code served (permissive licence) · get_code("ba09c4650052a805") |
| LinAttnBlock | Ran | hancyran/lidar-diffusion/lidm/modules/diffusion/model_lidm.py code served (permissive licence) · get_code("1be48aa5423551e2") |
| LinearAttention | Ran | hancyran/lidar-diffusion/lidm/modules/diffusion/model_lidm.py code served (permissive licence) · get_code("65eaa953ffaffaad") |
| Encoder | Not yet run | hancyran/lidar-diffusion/lidm/modules/diffusion/model_lidm.py code served (permissive licence) · get_code("6b0bf22688da1927") |
| ResnetBlock | Not yet run | hancyran/lidar-diffusion/lidm/modules/diffusion/model_lidm.py code served (permissive licence) · get_code("c54f70d846400a40") |
| make_attn | Not yet run | hancyran/lidar-diffusion/lidm/modules/diffusion/model_lidm.py code served (permissive licence) · get_code("321eebc8c4fd6b54") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Curve-based DMs (Ours) Throughput↑: 1.603 samples/sec (×107) Patch-based DMs (Latent Diffusion) Throughput↑: 2.171 samples/sec (×145) Point-based DMs (LiDARGen) Throughput↑: 0.015 samples/sec (×1) Figure 1. Our method (LiDM) steps towards LiDAR-realistic scene generation by preserving curve-like structures and objects with greater resemblance to real-world data (Reference), and marks a milestone for conditional LiDAR scene generation from different input modalities.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2404.00815")
get_code_for_paper("2404.00815")
have("2404.00815")
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