Effective and Efficient Mask Image Generation for 3D Inpainting

Abstract

Recently, 3D Inpainting methods have been proposed that leverage NeRF and 3DGS radiance fields to inpaint some areas of image. 3D Inpainting incorporates spatial information, which offers the advantage of spatially consistent inpainting across multiple images. Inpainting images, mask images are required to indicate which areas of the images are to be complemented. However the mask generation methods used in existing 3D Inpainting methods cause some problems such as deviations in consistency due to unnecessarily large mask areas, and poor completion accuracy due to incompleteness in mask areas. In this paper, we propose an effective and efficient method for generating mask images for 3D Inpainting. Experiments in a real-world environment show that 3D Inpainting using our method can improve the above problems.