CVPR 2023 - NeRFLiX: High-Quality Neural View Synthesis

In this episode we discuss NeRFLiX: High-Quality Neural View Synthesis by Authors: - Kun Zhou - Wenbo Li - Yi Wang - Tao Hu - Nianjuan Jiang - Xiaoguang Han - Jiangbo Lu. The paper proposes NeRFLiX, a degradation-driven inter-viewpoint mixer which is a general NeRF-agnostic restorer paradigm for improving the synthesis quality of NeRF-based approaches. NeRFs are successful in novel view synthesis but suffer from rendering artifacts such as noise and blur, and imperfect calibration information. NeRFLiX removes these artifacts and improves performance by fusing highly related, high-quality training images using an inter-viewpoint aggregation framework. Large-scale training data and a degradation modeling approach are utilized to achieve these improvements.