UrbanIR: Large-Scale Urban Scene Inverse Rendering from a Single Video

Chih-Hao Lin, Bohan Liu, Yi-Ting Chen, David Forsyth, Jiabin Huang, Anand Bhattad, Shenlong Wang

UrbanIR: Large-Scale Urban Scene Inverse Rendering from a Single Video: 5 upvotes on Hugging Face Daily Papers, #20 of 26 papers on 2023-06-16. Day-by-day upvote history.

We show how to build a model that allows realistic, free-viewpoint renderings of a scene under novel lighting conditions from video. Our method -- UrbanIR: Urban Scene Inverse Rendering -- computes an inverse graphics representation from the video. UrbanIR jointly infers shape, albedo, visibility, and sun and sky illumination from a single video of unbounded outdoor scenes with unknown lighting. UrbanIR uses videos from cameras mounted on cars (in contrast to many views of the same points in typical NeRF-style estimation). As a result, standard methods produce poor geometry estimates (for example, roofs), and there are numerous ''floaters''. Errors in inverse graphics inference can result in strong rendering artifacts. UrbanIR uses novel losses to control these and other sources of error. UrbanIR uses a novel loss to make very good estimates of shadow volumes in the original scene. The resulting representations facilitate controllable editing, delivering photorealistic free-viewpoint renderings of relit scenes and inserted objects. Qualitative evaluation demonstrates strong improvements over the state-of-the-art.

Paper page on Hugging Face · arXiv

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