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Motion Blur as the Key to Motion Estimation: A New Approach in Computer Vision

In the world of robotics, Virtual Reality (VR), and Augmented Reality (AR), fast camera movements pose a challenge. The resulting motion blur in images impairs the accuracy of conventional camera estimation methods. A new research approach, however, considers this blur not as a disturbance but as a valuable source of information. Instead of eliminating it, this innovative method uses motion blur to precisely reconstruct camera movement.

The Principle: Motion Flow and Depth Map

The core of the new method lies in the prediction of a dense motion flow field and a monocular depth map directly from a single, motion-blurred image. The motion flow field describes the movement of each individual pixel in the image, while the depth map captures the distance of objects from the camera. These two pieces of information are combined to calculate the instantaneous velocity of the camera. This is done by solving a linear system of equations under the assumption of small movements.

IMU-like Measurements from Images

Essentially, the method generates an IMU-like measurement (Inertial Measurement Unit), which robustly captures fast and aggressive camera movements. An IMU is a sensor that measures acceleration and rotation rate and is frequently used in robotics and navigation. The new method makes it possible to obtain this information directly from images without requiring additional hardware.

Training Data and Validation

To train the model, a comprehensive dataset with realistic, synthetically generated motion blurs from ScanNet++v2 was created. Subsequently, the model was further refined through end-to-end training with real data. Extensive tests on real benchmarks show that the method achieves excellent results in estimating angular and translational velocity compared to existing methods such as MASt3R and COLMAP.

Applications and Potential

The ability to accurately reconstruct camera movements from blurred images opens up new possibilities in various fields. In robotics, this technology can improve the navigation and control of robots, especially in dynamic environments. In VR and AR, it can contribute to a more immersive and realistic experience by more accurately capturing the user's movements. This method could also find application in image stabilization and 3D reconstruction.

Future Research

Research in this area is not yet complete. Future work could focus on improving the robustness of the method against different types of motion blur and more complex scenarios. The integration of the method into existing camera estimation systems is also a promising research approach. The results of this research could fundamentally change the way we extract motion information from images.

Bibliography: - https://arxiv.org/abs/2503.17358 - https://arxiv.org/html/2503.17358v1 - https://www.themoonlight.io/review/image-as-an-imu-estimating-camera-motion-from-a-single-motion-blurred-image - https://www.themoonlight.io/fr/review/image-as-an-imu-estimating-camera-motion-from-a-single-motion-blurred-image - https://x.com/ducha_aiki/status/1904105448283701282 - https://synthical.com/article/Image-as-an-IMU%3A-Estimating-Camera-Motion-from-a-Single-Motion-Blurred-Image-de5f207b-52cc-4911-9172-ee8c1d9ea39f? - https://www.researchgate.net/publication/363401062_Optical_Flow_Estimation_from_a_Single_Motion-blurred_Image - https://cdn.aaai.org/ojs/16172/16172-13-19666-1-2-20210518.pdf - https://openaccess.thecvf.com/content_CVPR_2020/papers/Pan_Single_Image_Optical_Flow_Estimation_With_an_Event_Camera_CVPR_2020_paper.pdf - https://pmc.ncbi.nlm.nih.gov/articles/PMC11622971/ ```