torch-fps 0.5

Native PyTorch farthest point sampling for point cloud workloads

torch-fps

Optimized standard farthest point sampling (FPS) for PyTorch written in C++.

Install

torch-fps is currently published as a source distribution, so pip builds the extension locally during install.

# First install a PyTorch build that matches your platform and CUDA version.
# Example for CUDA 12.8:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128

# Then build torch-fps against that exact Torch install.
pip install --no-build-isolation torch-fps

Note: Ensure gcc > 9 and < 14. Install can take a while because it is built from source. --no-build-isolation is recommended so pip uses your existing PyTorch install instead of creating a temporary build environment with a different Torch/CUDA combination.

Usage

import torch
from torch_fps import farthest_point_sampling, farthest_point_sampling_with_knn

# Create example inputs
points = torch.randn(4, 1000, 3)     # [B, N, D] - batch of point clouds
mask = torch.ones(4, 1000, dtype=torch.bool)  # [B, N] - valid point mask
K = 512  # Number of samples per batch (must be <= number of valid points)

# Perform farthest point sampling
idx = farthest_point_sampling(points, mask, K)  # [B, K] - selected point indices

# Use indices to gather sampled points
sampled_points = points.gather(1, idx.unsqueeze(-1).expand(-1, -1, 3))  # [B, K, D]

# Fused FPS + kNN: get centroids and their k nearest neighbors in one pass
centroid_idx, neighbor_idx = farthest_point_sampling_with_knn(
    points, mask, K=512, k_neighbors=32
)  # centroid_idx: [B, K], neighbor_idx: [B, K, k_neighbors]

Performance

Benchmarked on AMD Threadripper 7970X and NVIDIA RTX 5090. Values show CPU / CUDA measurements. By default uses float32; override with precision= parameter. Numbers below come from the in-repo benchmark script (python tests/profile.py) against the local extension build.

FPS:

B	N	K	Baseline (ms)	Optimized (ms)	Speedup
4	100	20	0.45 / 1.48	0.05 / 0.10	9.00x / 14.80x
8	512	64	2.98 / 4.31	0.12 / 0.16	24.83x / 26.94x
16	1024	128	30.00 / 8.49	0.37 / 0.27	81.08x / 31.44x
32	2048	256	151.86 / 16.77	1.43 / 1.01	106.20x / 16.60x

FPS+kNN:

B	N	K	k	Baseline (ms)	Optimized (ms)	Speedup
4	100	16	8	0.50 / 1.28	0.05 / 0.21	10.00x / 6.10x
8	512	64	16	4.97 / 4.40	0.17 / 1.09	29.24x / 4.04x
16	1024	128	16	37.27 / 8.57	0.79 / 2.28	47.18x / 3.76x
32	2048	256	16	172.63 / 18.11	2.64 / 5.15	65.39x / 3.52x