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• W3154331193 abstract "We present a new generic method for shadow-aware multi-view satellite photogrammetry of Earth Observation scenes. Our proposed method, the Shadow Neural Radiance Field (S-NeRF) follows recent advances in implicit volumetric representation learning. For each scene, we train S-NeRF using very high spatial resolution optical images taken from known viewing angles. The learning requires no labels or shape priors: it is self-supervised by an image reconstruction loss. To accommodate for changing light source conditions both from a directional light source (the Sun) and a diffuse light source (the sky), we extend the NeRF approach in two ways. First, direct illumination from the Sun is modeled via a local light source visibility field. Second, indirect illumination from a diffuse light source is learned as a non-local color field as a function of the position of the Sun. Quantitatively, the combination of these factors reduces the altitude and color errors in shaded areas, compared to NeRF. The S-NeRF methodology not only performs novel view synthesis and full 3D shape estimation, it also enables shadow detection, albedo synthesis, and transient object filtering, without any explicit shape supervision." @default.
• W3154331193 created "2021-04-26" @default.
• W3154331193 creator A5020258120 @default.
• W3154331193 creator A5046537661 @default.
• W3154331193 date "2021-04-20" @default.
• W3154331193 modified "2023-09-27" @default.
• W3154331193 title "Shadow Neural Radiance Fields for Multi-view Satellite Photogrammetry" @default.
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• W3154331193 hasPublicationYear "2021" @default.
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