IEEE Trans Vis Comput Graph
December 2024
We propose a novel framework to automatically learn to aggregate and transform photometric measurements from multiple unstructured views into spatially distinctive and view-invariant low-level features, which are subsequently fed to a multi-view stereo pipeline to enhance 3D reconstruction. The illumination conditions during acquisition and the feature transform are jointly trained on a large amount of synthetic data. We further build a system to reconstruct both the geometry and anisotropic reflectance of a variety of challenging objects from hand-held scans.
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February 2023
We propose a novel framework to efficiently capture the unknown reflectance on a non-planar 3D object, by learning to probe the 4D view-lighting domain with a high-performance illumination multiplexing setup. The core of our framework is a deep neural network, specifically tailored to exploit the multi-view coherence for efficiency. It takes as input the photometric measurements of a surface point under learned lighting patterns at different views, automatically aggregates the information and reconstructs the anisotropic reflectance.
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