NBField: Neural Burst Field for Full-Resolution Blind Deblurring in the Wild

Burst deblurring seeks to recover a single sharp photograph from multiple camera-shaken frames. Yet many conventional methods rely on translation-based, spatially invariant blur models, whereas real camera motion—from handheld photography to imaging on moving platforms—is often rotation-dominated and strongly spatially varying at high resolution. In this work, we reinterpret burst reconstruction in the domain of camera rotation, representing exposure motion in SO(3) and sharing a single 2D implicit neural representation across all frames. A differentiable forward model integrates the neural image along rotational homographies, while alternating optimization jointly refines the image and gyroscope-initialized motion without the need for supervised training. Across 447 in-the-wild GoPro bursts spanning severe shake blur and both natural- and low-light scenes, our method reconstructs native 12 megapixels (12MP) images and improves PSNR by an average of 5.75 dB over the best blurry input frame, and 3.87 dB over the best baseline. Each burst is reconstructed in 2.4 minutes on a single GPU, demonstrating practical full-resolution deblurring under everyday capture conditions.