Dynamic Neural Radiance Field From Defocused Monocular Video

TL;DR

This paper introduces D2RF, a novel dynamic NeRF approach that restores sharp, all-in-focus views from defocused monocular videos by modeling depth-of-field effects, improving dynamic scene reconstruction quality.

Contribution

D2RF is the first dynamic NeRF method specifically designed to handle defocus blur in monocular videos, enhancing sharpness and temporal consistency in reconstructed views.

Findings

Outperforms existing methods in synthesizing all-in-focus views from defocused videos.

Effectively models defocus blur using layered DoF volume rendering.

Maintains spatial-temporal consistency in dynamic scene reconstructions.

Abstract

Dynamic Neural Radiance Field (NeRF) from monocular videos has recently been explored for space-time novel view synthesis and achieved excellent results. However, defocus blur caused by depth variation often occurs in video capture, compromising the quality of dynamic reconstruction because the lack of sharp details interferes with modeling temporal consistency between input views. To tackle this issue, we propose D2RF, the first dynamic NeRF method designed to restore sharp novel views from defocused monocular videos. We introduce layered Depth-of-Field (DoF) volume rendering to model the defocus blur and reconstruct a sharp NeRF supervised by defocused views. The blur model is inspired by the connection between DoF rendering and volume rendering. The opacity in volume rendering aligns with the layer visibility in DoF rendering. To execute the blurring, we modify the layered blur kernel to the ray-based kernel and employ an optimized sparse kernel to gather the input rays efficiently and render the optimized rays with our layered DoF volume rendering. We synthesize a dataset with defocused dynamic scenes for our task, and extensive experiments on our dataset show that our method outperforms existing approaches in synthesizing all-in-focus novel views from defocus blur while maintaining spatial-temporal consistency in the scene.