#South Korea #Seoul #Chung-Ang University #MoBluRF #Neural Radiance Fields

A Technological Leap: MoBluRF Framework

In an exciting advancement in visual technology, researchers from Chung-Ang University, led by Assistant Professor Jihyong Oh, have introduced a new framework called MoBluRF (Motion Deblurring Neural Radiance Fields). This innovative framework paves the way for producing sharp 3D scene reconstructions from blurry videos captured by casual, handheld devices, such as smartphones and drones. This groundbreaking development marks a significant turning point in the field of novel view synthesis (NVS).

Understanding Neural Radiance Fields (NeRF)

Neural Radiance Fields (NeRF) is a disciplined technique that enables three-dimensional representations of scenes from two-dimensional images taken from various angles. By employing a deep neural network, it can predict both the color and density of any point in 3D space. This prediction involves casting imaginary light rays from the camera through pixels in all the input images, sampling each ray with its corresponding 3D coordinates and viewing direction. The final outcome is a reconstructed scene in 3D, which can be viewed from entirely fresh perspectives through the innovative process known as novel view synthesis (NVS).

The Challenge of Blurry Videos

While NeRF can effectively work with videos by treating each frame like a static image, standard methods usually falter with poor-quality video inputs. Monocular videos captured from devices often exhibit motion blur from rapid object movement or unsteady camera handling. Hence, attempting to create sharp, dynamic NVS under these conditions proves to be quite challenging. Most existing deblurring techniques are tailored for static multi-view images and fail to account for both global camera shifts and local object movements, leading to distortion in camera pose estimations and a lack of geometric accuracy.

Introducing MoBluRF: A Two-Stage Solution

To tackle these hurdles, the research team has developed MoBluRF, which operates through a two-stage motion deblurring method specifically designed for NeRFs. The two main stages are Base Ray Initialization (BRI) and Motion Decomposition-based Deblurring (MDD). Unlike typical deblurring methods that misinterpret blurry images as base rays, BRI initially reconstructs dynamic 3D scenes from the blurry video and efficiently refines the base ray initialization from less accurate camera rays.

Following this initialization, the MDD phase utilizes these refined base rays to accurately predict what is termed latent sharp rays via an Incremental Latent Sharp-rays Prediction (ILSP) technique. This method incrementally breaks down motion blur into components of global camera motion and local object movement, dramatically enhancing deblurring precision.

MoBluRF also proposes two novel loss functions, one that differentiates static and dynamic areas without relying on motion masks and another that improves the geometric accuracy of dynamic objects. Thanks to these innovations, MoBluRF has shown a noteworthy improvement over existing methods, performing both quantitatively and qualitatively with substantial advantages across various datasets, remaining resilient against varying levels of blur.

Practical Implications and Future Outcomes

Dr. Oh highlights the real-world impacts of MoBluRF, stating, "By enabling deblurring and 3D reconstruction from everyday handheld captures, our framework empowers smartphones and other consumer devices to deliver sharper and more immersive content." This technology could revolutionize applications such as producing crisp 3D models from shaky footage in museums, enhancing the understanding of scenes and ensuring safety for robots and drones, and minimizing the need for specialized setup in virtual and augmented reality environments.

MoBluRF symbolizes a promising future for developing high-quality 3D reconstructions from standard blurry videos, setting a fresh benchmark for Neural Radiance Fields.

References

• - Title of original paper: MoBluRF Motion Deblurring Neural Radiance Fields for Blurry Monocular Video
• - Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
• - DOI: 10.1109/TPAMI.2025.3574644

For more information on this topic and to stay updated with future advancements, please visit Chung-Ang University's official website.