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

We present UrbanIR (Urban Scene Inverse Rendering), a new inverse graphics model that enables realistic, free-viewpoint renderings of scenes under various lighting conditions with a single video. It accurately infers shape, albedo, visibility, and sun and sky illumination from wide-baseline videos, such as those from car-mounted cameras, differing from NeRF's dense view settings. In this context, standard methods often yield subpar geometry and material estimates, such as inaccurate roof representations and numerous ‘floaters’. UrbanIR addresses these issues with novel losses that reduce errors in inverse graphics inference and rendering artifacts. Its techniques allow for precise shadow volume estimation in the original scene. The model's outputs support controllable editing, enabling photorealistic free-viewpoint renderings of night simulations, relit scenes, and inserted objects, marking a significant improvement over existing state-of-the-art methods. Our code and data will be made publicly available upon acceptance.

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Publication Details

Published
2025-03-25
DOI
https://doi.org/10.1109/3dv66043.2025.00052
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
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article

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

David Forsyth, Shenlong Wang, Anand Bhattad, Chih-Hao Lin et al.
Remote Sensing and LiDAR Applications
article

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

David Forsyth, Shenlong Wang, Anand Bhattad, Chih-Hao Lin, Kuan-Sheng Chen, Yi-Ting Chen, Bohan Liu, Jia‐Bin Huang
article en

Abstract

We present UrbanIR (Urban Scene Inverse Rendering), a new inverse graphics model that enables realistic, free-viewpoint renderings of scenes under various lighting conditions with a single video. It accurately infers shape, albedo, visibility, and sun and sky illumination from wide-baseline videos, such as those from car-mounted cameras, differing from NeRF's dense view settings. In this context, standard methods often yield subpar geometry and material estimates, such as inaccurate roof representations and numerous ‘floaters’. UrbanIR addresses these issues with novel losses that reduce errors in inverse graphics inference and rendering artifacts. Its techniques allow for precise shadow volume estimation in the original scene. The model's outputs support controllable editing, enabling photorealistic free-viewpoint renderings of night simulations, relit scenes, and inserted objects, marking a significant improvement over existing state-of-the-art methods. Our code and data will be made publicly available upon acceptance.

University of Illinois Urbana-Champaign (US), University of Maryland, College Park (US)
Sustainable cities and communities
Openalex Percentile: Top 80%
Remote Sensing and LiDAR Applications
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