KFNet: Learning Temporal Camera Relocalization using Kalman Filtering (CVPR 2020 Oral)
This is a Tensorflow implementation of our CVPR 2020 Oral paper - "KFNet: Learning Temporal Camera Relocalization using Kalman Filtering" by Lei Zhou, Zixin Luo, Tianwei Shen, Jiahui Zhang, Mingmin Zhen, Yao Yao, Tian Fang, Long Quan.
This paper addresses the temporal camera relocalization of time-series image data by folding the scene coordinate regression problem into the principled Kalman filter framework.
If you find this project useful, please cite:
@inproceedings{zhou2020kfnet, title={KFNet: Learning Temporal Camera Relocalization using Kalman Filtering}, author={Zhou, Lei and Luo, Zixin and Shen, Tianwei and Zhang, Jiahui and Zhen, Mingmin and Yao, Yao and Fang, Tian and Quan, Long}, booktitle={Computer Vision and Pattern Recognition (CVPR)}, year={2020} }
KFNet simultaneously predicts the mapping points and camera poses in a temporal fashion within the coordinate system defined by a known scene.
|| DSAC++ | KFNet |
|:--:|:--:|:--:|
|7scenes-fire | |
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|12scenes-office2-5a|
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|Description | Blue - ground truth poses | Red - estimated poses |
Below we visualize the measurement and process noise.
|Data | Measurement noise | Process noise |
|:--:|:--:|:--:|
|7scenes-fire | |
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|12scenes-office2-5a|
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|Description | The brighter color means smaller noise. | The figure bar measures the inverse of the covariances (in centimeters) |
As an essential component of KFNet, the process system of KFNet (i.e., OFlowNet) delineates pixel transitions across frames through optical flow reasoning yet without recourse to grourd truth optical flow labelling. We visualize the predicted optical flow fields below while suppressing the predictions with too large uncertainties.
|Data | Description | Optical flow |
|:--:|:--:|:--:|
|7scenes-fire | Indoor; hand-held; small shaky motions | |
|12scenes-office2-5a | Indoor; hand-held; larger movements |
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|Cambridge-KingsCollege | Outdoor; hand-held; large random motions |
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|DeepLoc | Outdoor; vehicle-mounted; forward motions |
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Remark For DeepLoc, since OFlowNet is trained only on one scene included in DeepLoc, the flow predictions appear somewhat messy due to the lack of training data. Training with a larger amount and variety of data would improve the results.
Input: The input folder of a project should contain the files below.
image_list.txtcomprising the sequential full image paths in lines. Please go to the 7scenes dataset to download the source images.
label_list.txtcomprising the full label paths in lines corresponding to the images. The label files are generated by the
tofile()function of numpy matrices. They have a channel number of 4, with 3 for scene coordinates and 1 for binary masks of pixels. The mask for one pixel is 1 if its label scene coordinates are valid and 0 otherwise. Their resolutions are 8 times lower than the images. For example, for the 7scenes dataset, the images have a resolution of 480x640, while the label maps have a resolution of 60x80.
transform.txtrecording the 4x4 Euclidean transformation matrix which decorrelates the scene point cloud to give zero mean and correlations.
|chess(13G) |fire(9G) |heads(4G) |office(22G) |pumpkin(13G) |redkitchen(27G) |stairs(7G) | |:-:|:-:|:-:|:-:|:-:|:-:|:-:|
Output: The testing program (to be introduced below) outputs a 3-d scene coordinate map (in meters) and a 1-d confidence map into a 4-channel numpy matrix for each input image. And then you can run the provided PnP program (in
PnP.zip) or your own algorithms to compute the camera poses from them.
python vis/vis_scene_coordinate_map.py.
python vis/vis_scene_coordinate_map_list.py.
The codes are tested along with
To directly install tensorflow and other python packages, run
sudo pip install -r requirements.txt
If you are familiar with Conda, you can create the environment for KFNet by running
conda create -f environment.yml conda activate KFNet
You can download the trained models of 7scenes from the Google drive link (3G).
Test SCoordNet ``` git checkout SCoordNet python SCoordnet/eval.py --inputfolder <inputfolder> --outputfolder <outputfolder> --modelfolder <modelfolder> --scene
Test OFlowNet
git checkout OFlowNet python OFlowNet/eval --input_folder --output_folder --model_folderThe testing program of OFlowNet will save the 2-d optical flows and 1-d uncertainties of consecutive image pairs as npy files of the dimension 60x80x3. You can visualize the flow results by running scripts
vis/vis_optical_flow.pyand
vis/vis_optical_flow_list.py.
Test KFNet
git checkout master python KFNet/eval.py --input_folder --output_folder --model_folder --scene
Run PnP to compute camera poses
unzip PnP.zip && cd PnP python main.py --gt --thread_num <32> // Please note that you need to install git-lfs before cloning to get PnP.zip, since the zip file is stored via LFS.
The training procedure has 3 stages.
Train SCoordNet for each scene independently.
git checkout SCoordnet python SCoordNet/train.py --input_folder --model_folder --scene
Train OFlowNet using all the image sequences that are not limited to any specific scenes, for example, concatenating all the
image_list.txtand
label_list.txtof 7scenes for training.
git checkout OFlowNet python OFlowNet/train.py --input_folder --model_folder
Train KFNet for each scene from the pre-trained SCoordNet and OFlowNet models to jointly finetune their parameters.
git checkout master python KFNet/train.py --input_folder --model_folder --scoordnet --oflownet --scene
This implementation was developed by Lei Zhou. Feel free to contact Lei for any enquiry.