jerredchen00/image-as-an-imu-finetuning
Image as an IMU: Real-world Finetuning Dataset Official real-world finetuning dataset from Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred Image (ICCV 2025 Oral). [arXiv] [Webpage] [GitHub] PIXL, University of Oxford Jerred Chen, Ronald Clark Dataset Details This dataset consists of 32 sequences of real-world motion-blurred videos in various indoor scenes, captured using the iPhone 13 camera. dataset_train_real-world.csv and… See the full description on the dataset page: https://huggingface.co/datasets/jerredchen00/image-as-an-imu-finetuning.
Image as an IMU: Real-world Finetuning Dataset
Official real-world finetuning dataset from Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred Image (ICCV 2025 Oral).
[PIXL, University of Oxford](https://pixl.cs.ox.ac.uk/)
Dataset Details
This dataset consists of 32 sequences of real-world motion-blurred videos in various indoor scenes, captured using the iPhone 13 camera.
dataset_train_real-world.csv and dataset_val_real-world.csv are the CSV files used for training/validating the model in the paper. These can be directly plugged into the provided dataloader in the GitHub.
The CSVs provide the following:
- blurred: the relative path to the (resized 320x224) motion-blurred RGB image
- ts1,ts2: the frame timestamps between the previous RGB and next RGB image
- fx,fy,cx,cy: the scaled camera intrinsics, corresponding to the 320x224 image
- bRaqx,bRaqy,bRaqz,bRaqw: body-frame rotational velocity, parameterized as a quaternion
- btax,btay,bta_z: body-frame translational velocity
- exposure: exposure time at the given image
- sequence: the sequence name
Sequence Details
Each sequence consists of the following:
sequence1/
├─ blurry_frames_320x224
│ ├─ XXXXXX.jpg
│ └─ ...
├─ confidence
│ ├─ XXXXXX.png
│ └─ ...
├─ depth
│ ├─ XXXXXX.png
│ └─ ...
├─ rgb
│ ├─ XXXXXX.jpg
│ └─ ...
├─ rgb_320x224
│ ├─ XXXXXX.jpg
│ └─ ...
└─ blurred_frames_320x224.csv
└─ camera_matrix.csv
└─ camera_matrix_320x224.csv
└─ imu.csv
└─ odometry.csv
└─ velocities.csvSequences were recorded using the StrayScanner app, slightly modified to also obtain the exposure time from ARKit. confidence, depth, rgb, camera_matrix.csv, imu.csv, and odometry.csv are the original outputs from StrayScanner.
We provide the following data in addition to the StrayScanner outputs:
rgb_320x224are the resized recorded RGB imagesblurry_frames_320x224are the identified frames with more extensive blur using FFT as described in Liu et al.camera_matrix_320x224.csvare the corresponding scaled camera intrinsicsvelocities.csvconsist of the translational velocities computed from ARKit poses inodometry.csvand the rotational velocities directly from the gyroscope.
Of course, the RGB images/camera intrinsics can be resized/scaled online during training; we provide this to maintain consistency with our own training.
Since the ARKit computed poses can have very large errors, dataset_train_real-world.csv consists of manually filtered samples without large outlier pose estimates.
