OutCast: Outdoor Single-image Relighting with Cast Shadows
David Griffiths, Tobias Ritschel, Julien Philip

TL;DR
OutCast introduces a novel outdoor image relighting technique that predicts cast shadows from a single image by converting approximate depth maps into deep 3D representations, enabling state-of-the-art results without multiple images.
Contribution
This work presents a new method that uses a learned image space ray-marching layer to improve shadow prediction from single-image depth estimates for outdoor relighting.
Findings
Achieves state-of-the-art outdoor relighting results from a single image.
Effectively predicts cast shadows and global lighting effects.
Uses a learned 3D representation to enhance depth-based shadow rendering.
Abstract
We propose a relighting method for outdoor images. Our method mainly focuses on predicting cast shadows in arbitrary novel lighting directions from a single image while also accounting for shading and global effects such the sun light color and clouds. Previous solutions for this problem rely on reconstructing occluder geometry, e.g. using multi-view stereo, which requires many images of the scene. Instead, in this work we make use of a noisy off-the-shelf single-image depth map estimation as a source of geometry. Whilst this can be a good guide for some lighting effects, the resulting depth map quality is insufficient for directly ray-tracing the shadows. Addressing this, we propose a learned image space ray-marching layer that converts the approximate depth map into a deep 3D representation that is fused into occlusion queries using a learned traversal. Our proposed method achieves,…
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Taxonomy
TopicsAdvanced Vision and Imaging · Image Enhancement Techniques · Computer Graphics and Visualization Techniques
