SSMB: Self-Supervised Local Feature Detection under Motion Blur
This repository hosts the pretrained checkpoint for SSMB, a deblur-free, self-supervised keypoint detector for motion-blurred images, accompanying our paper submitted to IEEE Transactions on Image Processing (under review).
Code: https://github.com//SSMB
Model Description
SSMB is trained in two self-supervised stages:
- Geometric pretraining on synthetic geometric shapes, bootstrapping spatially discriminative keypoint detection from rendered corner labels.
- Blur-aware training on real sharp-blur image pairs from the GoPro dataset, using a multi-component self-supervised objective (homographic adaptation, blur consistency, position consistency, and spatial diversity losses).
The architecture consists of an MLP-based encoder (adapted from MAXIM) with a Local Discriminability Enhancement (LDE) module inserted in each block, followed by a detector head that predicts a keypoint probability map and sub-pixel position offsets.
Files
| File | Description |
|---|---|
extraction.pth | Final SSMB checkpoint after Stage 2 (blur-aware) training, used to produce all main results reported in the paper |
Usage
import torch
from models.ssmb import get_ssmb
import yaml
with open('configs/ssmb.yaml') as f:
cfg = yaml.safe_load(f)
model = get_ssmb(model_cfg=cfg['MODEL'], image_shape=cfg['data']['IMAGE_SHAPE'])
ckpt = torch.load('extraction.pth', map_location='cpu')
model.load_state_dict(ckpt['model_state'], strict=False)
model.eval()
See the code repository for full training and evaluation instructions.
Training Data
Stage 1: synthetic geometric shapes (generated on-the-fly). Stage 2: GoPro dataset (Nah et al., CVPR 2017), 2,912 sharp-blur pairs from 30 sequences.
Evaluation Results
For complete quantitative results (keypoint detection repeatability, image matching, relative pose estimation, visual localization), please refer to the paper and its supplementary material.
License
Released under the MIT License. The encoder architecture is adapted from MAXIM (Apache License 2.0).
Citation
@article{ssmb2026,
title={SSMB: Self-Supervised Local Feature Detection under Motion Blur},
author={Zhao, Zhenjun and Bellavia, Fabio and Wang, Wenting and Zhu, Fan and Wu, Jiajun and Kumar, Suryansh and Wei, Mingqiang and Li, Haoang and Civera, Javier},
journal={IEEE Transactions on Image Processing},
note={Under review},
year={2026}
}