Mirror is the matchable image retrieval pipeline for 3D reconstruction and related applications. Different from typical object retrieval, matchable image retrieval aims to find similar images with large overlaps. Typical CNN-based methods do not generalize well to this problem because models are trained to find objects of the same category. This project proposes a new method to tackle this problem, by utilizing regional feature aggregation and the accurate auto-annotated 3D geometric data. For more details, you can refer to the paper:
Matchable Image Retrieval by Learning from Surface Reconstruction
Tianwei Shen*, Zixin Luo*, Lei Zhou, Runze Zhang, Siyu Zhu, Tian Fang, Long Quan (* denotes equal contributions)
In ACCV 2018.
Feel free to submit issues if you have any questions.
The code base has been tested under TensorFlow 1.5 (CUDA 8.0) to TensorFlow 1.7 (CUDA 9.0), using Python 2.7.12.
Mirror is extremely easy to use, first download the model files using the following command, suppose you are in the root directory of the project $MIRROR_ROOT
sh ./data/model/download_models.shSuppose you have a folder containing the images of the same scene, take fountain-P11 as an example (or any other image folder by your hand),
generate the image list by cd into that folder and output absolute image paths to a file:
cd /path/to/fountain-p11
ls -d $PWD/*.png > image_listThen generate the .npy features:
python retrieval/inference.py --img_list /home/tianwei/Data/fountain-p11/image_list --ckpt_step 720000 --img_size 896 --net resnet-50 --pool MAX --rmac_step 1,3,5 --ckpt_path ./data/model/model.ckptThen output all the absolute paths of feature files (*.npy) to a file and query:
ls -d $PWD/*.npy > feature_list
python retrieval/deep_query.py --feature_list /home/tianwei/Data/fountain-p11/feature_list --out_dir ./output --top 20 --out_dim 256You should now be able to see a match_pairs file in the output folder, recording the ranking and corresponding similarity scores (in range [0, 1] where 1 means identical)
between pairs of images (represented by index).
0 0 1.0
0 1 0.9598151070731027
0 2 0.9315839494977679
0 3 0.9064583369663783
0 4 0.8789794376918247
0 9 0.8777283804757254
0 8 0.868733160836356
0 5 0.8614536830357142
0 10 0.8602364676339286
0 7 0.8555836813790457
0 6 0.8524237496512277
1 1 1.0
1 2 0.9604712350027902
1 0 0.958723885672433
1 3 0.9399954659598214
1 4 0.9106133052280971
... ...This is the basic usage of this repo. If you cannot get it done, please submit a issue in the issue tracker and let us help you! If you find this work useful in your production environment or research paper, please cite
@inproceedings{shen2018mirror,
author={Shen, Tianwei and Luo, Zixin and Zhou, Lei and Zhang, Runze and Zhu, Siyu and Fang, Tian and Quan, Long},
title={Matchable Image Retrieval by Learning from Surface Reconstruction},
booktitle={The Asian Conference on Computer Vision (ACCV},
year={2018},
}The following describe our dataset and results presented in the paper.
The GL3D contains 90,590 high-resolution images in 378 different scenes. Each scene contains 50 to 1,000 images with large geometric overlaps, covering urban, rural area, or scenic spots captured by drones from multiple scales and perspectives. Several small objects are also included to enrich the data diversity. Each scene data is reconstructed to generate a triangular mesh model by the state-of-the-art 3D reconstruction pipeline.
Currently we release the 9368 test images for evaluation (see #Test). The full dataset can be found here.
See retrieval/train.py for an example. It also needs the GL3D dataset to be aligned with our internal file structure. The training script depends on an older version of GL3D, specifically L285-286 in retrieval/train.py.
image_list_path = os.path.join(FLAGS.gl3d, 'list', 'global_stats', 'global_img_list.txt')
train_list_path = os.path.join(FLAGS.gl3d, 'forward', 'train_list_with_mask.bin')Try clone the Gl3D dataset, and then download the necessary files gl3d_train.tar.gz compatible with this training script, and put it into the GL3D root folder.
Please refer to pipeline.sh for using the image retrieval pipeline. We release two trained models to demonstrate the use.
The googlenet model can be used to reproduce the results in the paper, which achieves 0.758 mAP@200 on GL3D, 0.768 mAP on Oxford5K
and 0.820 on Paris6K using the default settings in pipeline.sh.
We have additionally trained a ResNet-50 model not documented in the original paper. By refined from a base model trained on Google-Landmarks-Dataset, it achieves better performance than GoogleNet on object retrieval datasets (Oxford5K and Paris6K).
| Model | GL3D (mAP@200) | Oxford5K | Paris6K | Holidays |
|---|---|---|---|---|
| GoogleNet | 0.758 | 0.768 | 0.820 | 0.861 |
| ResNet-50 | 0.745 | 0.833 | 0.805 | 0.892 |
| ResNet-50 + QE | - | 0.894 | 0.858 | 0.881 |
Download the GL3D test images of size 896 x 896 (around 3.7GB) and unzip them to your GL3D image folder (suppose it is $MIRROR_ROOT/data/gl3d/), and then run the commands for testing. The detailed instruction is given below.
# Download evaluation dataset and unzip
wget https://s3-ap-southeast-1.amazonaws.com/awsiostest-deployments-mobilehub-806196172/GL3D/eval_data.zip
mv eval_data.zip ./data/gl3d/
cd data/gl3d/
unzip eval_data.zip
cd -
# Set up testing configs
net_type=resnet-50 #googlenet
pooling=MAX
step=720000 #400000
ckpt_save_dir=./data/model/model.ckpt
img_size=896
feature_dim=256
gl3d_root_dir=$MIRROR_ROOT/data/gl3d/
test_image_list=./data/gl3d/eval_img_list.txt
test_feature_list=./data/gl3d/eval_feature_list.txt
rmac_step=1,3,5
output_dir=./output
# Extract features
python retrieval/inference.py --img_list $test_image_list --ckpt_step $step --img_size $img_size \
--net $net_type --pool $pooling --rmac_step $rmac_step --ckpt_path $ckpt_save_dir --gl3d_root $gl3d_root_dir
# PR-MAC + Top-k
python retrieval/deep_query.py --feature_list $test_feature_list --out_dir $output_dir --top 200 --out_dim $feature_dim --dataset_root $gl3d_root_dir
# Compute statistics
python retrieval/compute_overlap_benchmark.py $test_image_list $output_dir/match_pairs data/gl3d/gl3d_gt_pairsTo run the model on Oxford5K or Paris6K,
there is one additional step. Suppose you are in the root directory of this repo, you need to first
compile the C++ program compute_ap for computing the average precision (AP).
cd cpp
g++ -o compute_ap compute_ap.cppThe commands are similar to GL3D:
dataset=oxford #paris
oxford_image_list=/path/to/your/oxford/image/list
ground_truth_folder=./data/$oxford/groundtruth
query_output_path=$dataset'_queries'
db_output_path=$dataset'_output'
oxford_feature_list=$db_output_path/feature_list
oxford_query_feat_list=$query_output_path/query_list
img_size=896
rmac_step=1,2
output_dir=./output
# extract query features
rm -rf $query_output_path
python retrieval/inference.py --img_list $oxford_image_list --oxford_gt $ground_truth_folder --net $net_type --pool $pooling \
--ckpt_step $step --ckpt_path $ckpt_save_dir --img_size $img_size --rmac_step $rmac_step --multi_scale
ls -d $PWD/$query_output_path/*.npy > $query_output_path/query_list
# extract db features
rm -rf $db_output_path
python retrieval/inference.py --output_folder $db_output_path --img_list $oxford_image_list --net $net_type --pool $pooling \
--ckpt_step $step --ckpt_path $ckpt_save_dir --img_size $img_size --rmac_step $rmac_step --multi_scale
ls -d $PWD/$db_output_path/*.npy > $db_output_path/feature_list
# query
python retrieval/deep_query.py --out_dir $output_dir --feature_list $oxford_feature_list \
--query_list oxford_queries/query_list --top 6500 --out_dim $feature_dim --qe 10 --et 2 #--pca_thresh 0.90 #--rmac --pca_file $pca_file
# evaluate
python retrieval/oxford_bench.py data/$dataset/query_list.txt data/$dataset/image_list.txt $output_dir/match_pairs $ground_truth_folder \
--output_dir $output_dir
# remove temporary feature files
rm -rf $query_output_path
rm -rf $db_output_pathDownloads INRIA Holidays dataset at official website, prepare the required input lists and use the commands in pipeline.sh to reproduce the results.
Also checkout the following related geometric learning repositories:
GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints
MVSNet: Depth Inference for Unstructured Multi-view Stereo
Add training script and training data compatible with the python script.
We've made the benchmark of Oxford (or Paris) simply. You only need to prepare the oxford dataset images (download from the official website) and run `pipeline.sh', see "Note on Oxford5K or Paris6K" for details. Using the default settings in the script, you can easily achieve 0.915 mAP on Oxford5K.
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