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egeerdem/
sf-flow

Name: sf-flow
Availability: InStock

mit

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SF-Flow — pretrained checkpoints

Pretrained models for SF-Flow: Sound field magnitude estimation via flow matching guided by sparse measurements (IWAENC 2026).

SF-Flow reconstructs a dense 3D acoustic transfer function (ATF) magnitude field on an 11×11×11 grid from a sparse, variable-size set of microphone observations, using conditional Flow Matching with a permutation-invariant set encoder and a 3D U-Net.

Project page: https://egerdem.github.io/sf-flow/
Code: https://github.com/egerdem/SF-Flow

Checkpoints

Four models trained on dataset R1, one per frequency range. Optimizer state has been stripped, so each file is ~460 MB and is intended for evaluation or fine-tuning from the weights rather than exact resumption of the original training run.

file	freq bins (max freq)	val LSD (dB)	paper test LSD (dB, M=5)
`sfflow_r1_freq20.pt`	0–20 (312 Hz)	1.7555	1.75 ± 0.58
`sfflow_r1_freq30.pt`	0–30 (468 Hz)	3.1703	3.17 ± 0.67
`sfflow_r1_freq40.pt`	0–40 (625 Hz)	4.1857	4.16 ± 0.63
`sfflow_r1_freq64.pt`	0–64 (1000 Hz)	5.5800	5.56 ± 0.52

Test LSD is averaged over the 102 test sources with M=5 observations, using the fixed microphone-selection protocol described in the code repository.

Usage

bash

hf download egeerdem/sf-flow sfflow_r1_freq30.pt --local-dir .
python evaluate.py --model_path sfflow_r1_freq30.pt \
    --data_dir data/ir_fs2000_s1024_m1331_room4.0x6.0x3.0_rt200/

evaluate.py, and the script that regenerates the dataset the checkpoints expect, are in the code repository.