TC-NeRF:Temporal Consistent Neural Radiance Fields with Cross-View Complementation for Occluded Object Removal

In this paper, a novel object removal method for Neural Radiance Fields (NeRF) is proposed to address inconsistencies encountered by current object removal methods. Existing methods typically inpaint masked images independently before integrating them into NeRF, often leading to cross-view inconsistencies. Our approach focuses on inpainting only regions that are entirely unseen or visible in very few views, thereby improving temporal consistency. We leverage the cross-view complementation property of NeRF to determine visible and fully occluded regions. By projecting masks across different views and counting pixel visibility, we identify regions that require inpainting. This selective inpainting strategy ensures coherent reconstructions and higher rendering quality.