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UrbanIR: Large-Scale Urban Scene Inverse Rendering from a Single Video
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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.
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Cited by 2 Pith papers
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InvRGB+L: Inverse Rendering of Complex Scenes with Unified Color and LiDAR Reflectance Modeling
InvRGB+L jointly estimates visible and LiDAR albedo with a physics-based specular LiDAR model and cross-modal consistency losses, improving inverse rendering and LiDAR intensity simulation for urban and indoor scenes.
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DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models
A single video diffusion system both estimates scene properties from video and renders photorealistic images from those properties, enabling relighting, material editing, and object insertion.
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