mlsanityData to 13 Oct 2026

No Shadow Left Behind: Removing Objects and their Shadows using Approximate Lighting and Geometry

arXiv 2012.10565 ↗First posted 19 Dec 2020cs.CV cs.GRcited by 9

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Abstract (arXiv)

Removing objects from images is a challenging problem that is important for many applications, including mixed reality. For believable results, the shadows that the object casts should also be removed. Current inpainting-based methods only remove the object itself, leaving shadows behind, or at best require specifying shadow regions to inpaint. We introduce a deep learning pipeline for removing a shadow along with its caster. We leverage rough scene models in order to remove a wide variety of shadows (hard or soft, dark or subtle, large or thin) from surfaces with a wide variety of textures. We train our pipeline on synthetically rendered data, and show qualitative and quantitative results on both synthetic and real scenes.

Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). First posted on arXiv 19 Dec 2020.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 4; 2025: 0; 2026: 2.

9 papers.