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Neural network learns subsurface scattering for realistic object relighting

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel method for acquiring and representing subsurface scattering properties of light transport in objects. This technique utilizes a U-Net Convolutional Neural Network (CNN) that learns pixel footprint responses from 3D scan data. By employing a stereo projector-camera setup with phase-shifted profilometry patterns, the system captures detailed scattering data, enabling realistic relighting of objects with arbitrary high-resolution patterns. AI

RANK_REASON The cluster contains a research paper published on arXiv detailing a new method for computer vision.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Neural network learns subsurface scattering for realistic object relighting

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The cluster contains a research paper published on arXiv detailing a new method for computer vision.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Arjun Majumdar, Raphael Braun, Hendrik Lensch ·

    Neural Acquisition & Representation of Subsurface Scattering

    arXiv:2606.02292v1 Announce Type: new Abstract: We present a method to acquire and estimate the sub-surface scattering properties of light transport at a highly detailed level by learning the pixel footprint response at each point on the object surface. The reconstruction leverag…

  2. arXiv cs.CV TIER_1 English(EN) · Hendrik Lensch ·

    Neural Acquisition & Representation of Subsurface Scattering

    We present a method to acquire and estimate the sub-surface scattering properties of light transport at a highly detailed level by learning the pixel footprint response at each point on the object surface. The reconstruction leverages 3D scanning techniques as input to a U-Net CN…

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