Learning to Reconstruct High-quality 3D Shapes with Cascaded Fully Convolutional Networks

We present a data-driven approach to reconstructing high-resolution and detailed volumetric representations of 3D shapes. Although well studied, algorithms for volumetric fusion from multi-view depth scans are still prone to scanning noise and occlusions, making it hard to obtain high-fidelity 3D reconstructions... (read more)

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