Fast Sun-aligned Outdoor Scene Relighting based on TensoRF
This work addresses the problem of efficient outdoor scene relighting for computer graphics and vision applications, but it appears incremental as it builds on TensoRF with a sun-alignment strategy.
The paper tackles outdoor scene relighting for Neural Radiance Fields by introducing SR-TensoRF, a lightweight and rapid pipeline that eliminates the need for environment maps and achieves notable acceleration in training and rendering compared to existing methods.
In this work, we introduce our method of outdoor scene relighting for Neural Radiance Fields (NeRF) named Sun-aligned Relighting TensoRF (SR-TensoRF). SR-TensoRF offers a lightweight and rapid pipeline aligned with the sun, thereby achieving a simplified workflow that eliminates the need for environment maps. Our sun-alignment strategy is motivated by the insight that shadows, unlike viewpoint-dependent albedo, are determined by light direction. We directly use the sun direction as an input during shadow generation, simplifying the requirements of the inference process significantly. Moreover, SR-TensoRF leverages the training efficiency of TensoRF by incorporating our proposed cubemap concept, resulting in notable acceleration in both training and rendering processes compared to existing methods.