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Found 3 papers, 0 papers with code
Learning Photometric Feature Transform for Free-form Object Scan
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 fed to a multi-view stereo method to enhance 3D reconstruction.
DiFT: Differentiable Differential Feature Transform for Multi-View Stereo
We present a novel framework to automatically learn to transform the differential cues from a stack of images densely captured with a rotational motion into spatially discriminative and view-invariant per-pixel features at each view.
Learning Efficient Photometric Feature Transform for Multi-view Stereo
We present a novel framework to learn to convert the perpixel photometric information at each view into spatially distinctive and view-invariant low-level features, which can be plugged into existing multi-view stereo pipeline for enhanced 3D reconstruction.
