DerainNeRF: 3D Scene Estimation with Adhesive Waterdrop Removal
Yunhao Li Jing Wu Lingzhe Zhao Peidong Liu
When capturing images through the glass during rainy or snowy weather
conditions, the resulting images often contain waterdrops adhered on the glass
surface, and these waterdrops significantly degrade the image quality and
performance of many computer vision algorithms. To tackle these limitations, we
propose a method to reconstruct the clear 3D scene implicitly from multi-view
images degraded by waterdrops. Our method exploits an attention network to
predict the location of waterdrops and then train a Neural Radiance Fields to
recover the 3D scene implicitly. By leveraging the strong scene representation
capabilities of NeRF, our method can render high-quality novel-view images with
waterdrops removed. Extensive experimental results on both synthetic and real
datasets show that our method is able to generate clear 3D scenes and
outperforms existing state-of-the-art (SOTA) image adhesive waterdrop removal
methods.