torch-quickfps-cu130 2.1.0

PyTorch bucket-based farthest point sampling (CPU + CUDA).

PyTorch QuickFPS

Efficient farthest point sampling (FPS) for PyTorch, adapted from fpsample.

This project provides bucket-based FPS on both CPU and GPU. The GPU path is optimized for high-dimensional sampling (e.g., feature embeddings).

---

Installation

1) Install PyTorch (required)

Install PyTorch using the official instructions for your platform/CUDA:

• https://pytorch.org/get-started/locally/

2) Install torch_quickfps

Option A: prebuilt wheels from pip

# CPU-only
pip install torch-quickfps

# CUDA 12.8
pip install torch-quickfps-cu128

# CUDA 13.0
pip install torch-quickfps-cu130

Notes:

• The CUDA wheel you choose should match the CUDA-enabled PyTorch you installed (e.g., cu128 wheel with a cu128 PyTorch build).

Option B: install from source (GitHub)

pip install --no-build-isolation git+https://github.com/Astro-85/torch_quickfps

---

Usage

import torch
import torch_quickfps

x = torch.rand(64, 2048, 256)

# Random sample
sampled_points, indices = torch_quickfps.sample(x, 1024)

# Random sample with specific tree height
sampled_points, indices = torch_quickfps.sample(x, 1024, h=3)

# Random sample with start point index (int)
sampled_points, indices = torch_quickfps.sample(x, 1024, start_idx=0)

# For high-dimensional embeddings on CUDA, set low_d for faster bucketing
sampled_points, indices = torch_quickfps.sample(x, 1024, h=8, low_d=8)

# Indices-only
indices = torch_quickfps.sample(x, 1024, return_points=False)
# (equivalently)
indices = torch_quickfps.sample_idx(x, 1024)

# Masked sampling: only sample from valid points (mask shape [B, N])
mask = torch.ones(x.shape[:-1], dtype=torch.bool)
mask[:, 1000:] = False  # e.g. padding
sampled_points, indices = torch_quickfps.sample(x, 512, mask=mask)

print(sampled_points.size(), indices.size())
# torch.Size([64, 1024, 256]) torch.Size([64, 1024])

---

Performance comparison

Comparison includes CPU, a vanilla GPU FPS baseline, and our bucketed GPU implementation.

• N: number of input points
• D: point dimension
• K: number of sampled points
• CPU vs GPU (bucketed): CPU_ms / GPU_bucketed_ms
• GPU baseline vs bucketed: GPU_baseline_ms / GPU_bucketed_ms

N	D	K	CPU (ms)	GPU baseline (ms)	GPU bucketed (ms)	CPU vs GPU (bucketed)	GPU baseline vs bucketed
1000	8	250	0.271	0.404	2.671	0.10x	0.15x
1000	1024	250	69.697	94.144	4.867	14.32x	19.34x
1000	4096	250	248.521	378.458	10.614	23.41x	35.65x
2000	8	500	1.578	1.299	5.432	0.29x	0.24x
2000	1024	500	213.804	399.292	11.018	19.41x	36.24x
2000	4096	500	869.318	1585.913	33.974	25.59x	46.68x
5000	8	1250	6.151	7.156	16.970	0.36x	0.42x
5000	1024	1250	1075.742	2483.299	47.459	22.67x	52.33x
5000	4096	1250	4547.318	10027.665	154.874	29.36x	64.75x
10000	8	2500	22.135	26.152	43.379	0.51x	0.60x
10000	1024	2500	4503.257	9959.041	186.622	24.13x	53.36x
10000	4096	2500	21699.598	40439.047	645.883	33.60x	62.61x

---

Reference

Bucket-based FPS (QuickFPS) is proposed in the following paper:

@article{han2023quickfps,
  title={QuickFPS: Architecture and Algorithm Co-Design for Farthest Point Sampling in Large-Scale Point Clouds},
  author={Han, Meng and Wang, Liang and Xiao, Limin and Zhang, Hao and Zhang, Chenhao and Xu, Xiangrong and Zhu, Jianfeng},
  journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},
  year={2023},
  publisher={IEEE}
}

Thanks to the authors for their great work.