Yiran Chen, Cong Guo, Qinsi Wang, Yueqian Lin, Hancheng Ye, Helen Li, Yuzhe Fu, Junyao Zhang, Changchun Zhou
We lifted 11 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 |
|---|---|---|
| Yuzhe-Fu/FlashFPS | canonical | 10 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| concat_collate_fn | Ran | Yuzhe-Fu/FlashFPS/FlashFPS-Openpoints/openpoints/dataset/build.py pointer only (licence: NOASSERTION) · get_code("65c970f35976128a") |
| download_and_extract_archive | Ran | Yuzhe-Fu/FlashFPS/FlashFPS-Openpoints/openpoints/dataset/modelnet/modelnet40_ply_2048_loader.py pointer only (licence: NOASSERTION) · get_code("b9389712035a81db") |
| farthest_point_sample | Ran | Yuzhe-Fu/FlashFPS/FlashFPS-Openpoints/openpoints/dataset/modelnet/modelnet40_normal_resampled_loader.py pointer only (licence: NOASSERTION) · get_code("f80066a00e7156a2") |
| fnv_hash_vec | Ran | Yuzhe-Fu/FlashFPS/FlashFPS-Openpoints/openpoints/dataset/data_util.py pointer only (licence: NOASSERTION) · get_code("0086c892bd1df796") |
| is_model | Ran | Yuzhe-Fu/FlashFPS/FlashFPS-Openpoints/openpoints/models/registry.py pointer only (licence: NOASSERTION) · get_code("cd2a47ded9dd84cf") |
| list_models | Ran | Yuzhe-Fu/FlashFPS/FlashFPS-Openpoints/openpoints/models/registry.py pointer only (licence: NOASSERTION) · get_code("d14a6ca5ff9d530e") |
| load_data | Ran | Yuzhe-Fu/FlashFPS/FlashFPS-Openpoints/openpoints/dataset/modelnet/modelnet40_ply_2048_loader.py pointer only (licence: NOASSERTION) · get_code("9ed5c671b662922a") |
| pc_normalize | Ran | Yuzhe-Fu/FlashFPS/FlashFPS-Openpoints/openpoints/dataset/modelnet/modelnet40_normal_resampled_loader.py pointer only (licence: NONE) · get_code("4783fbece52f500e") |
| ravel_hash_vec | Ran | Yuzhe-Fu/FlashFPS/FlashFPS-Openpoints/openpoints/dataset/data_util.py pointer only (licence: NOASSERTION) · get_code("afdc8ad15d9098b8") |
| register_model | Ran | Yuzhe-Fu/FlashFPS/FlashFPS-Openpoints/openpoints/models/registry.py pointer only (licence: NOASSERTION) · get_code("e13039fb1c347452") |
| download_url | Not yet run | Yuzhe-Fu/FlashFPS/FlashFPS-Openpoints/openpoints/dataset/data_util.py pointer only (licence: NOASSERTION) · get_code("556e417290a6e1fd") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Point-based Neural Networks (PNNs) have become a key approach for point cloud processing. However, a core operation in these models, Farthest Point Sampling (FPS), often introduces significant inference latency, especially for large-scale processing. Despite existing CUDA-and hardware-level optimizations, FPS remains a major bottleneck due to exhaustive computations across multiple network layers in PNNs, which hinders scalability. Through systematic analysis, we identify three substantial redundancies in FPS, including unnecessary full-cloud computations, redundant late-stage iterations, and predictable inter-layer outputs that make later FPS computations avoidable. To address these, we propose FlashFPS, a hardware-agnostic, plug-and-play framework for FPS acceleration, composed of FPS-Prune and FPS-Cache. FPS-Prune introduces candidate pruning and iteration pruning to reduce redundant computations in FPS while preserving sampling quality, and FPS-Cache eliminates layer-wise redundancy via cache-and-reuse. Integrated into existing CUDA libraries and state-of-the-art PNN accelerators, FlashFPS achieves 5.16× speedup over the standard CUDA baseline on GPU and 2.69× on PNN accelerators, with negligible accuracy loss, enabling efficient and scalable PNN inference. Codes are released at https://github.com/Yuzhe-Fu/FlashFPS.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2604.17720")
get_code_for_paper("2604.17720")
have("2604.17720")
Connect an agent — have() is free.