Our method completes a partial 3D scan using a 3D Encoder-Predictor network that leverages semantic features from a 3D classification network. The predictions are correlated with a shape database, which we use in a multi-resolution 3D shape synthesis step. We obtain completed high-resolution meshes that are inferred from partial, low-resolution input scans. Abstract We introduce a data-driven approach to complete partial 3D shapes through a combination of volumetric deep neural networks and 3D

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... hape synthesis. From a partially-scanned input shape, our method first infers a low-resolution -but complete -output. To this end, we introduce a 3D-Encoder-Predictor Network (3D-EPN) which is composed of 3D convolutional layers. The network is trained to predict and fill in missing data, and operates on an implicit surface representation that encodes both known and unknown space. This allows us to predict global structure in unknown areas at high accuracy. We then correlate these intermediary results with 3D geometry from a shape database at test time. In a final pass, we propose a patch-based 3D shape synthesis method that imposes the 3D geometry from these retrieved shapes as constraints on the coarsely-completed mesh. This synthesis process enables us to reconstruct finescale detail and generate high-resolution output while respecting the global mesh structure obtained by the 3D-EPN. Although our 3D-EPN outperforms state-of-the-art completion method, the main contribution in our work lies in the combination of a data-driven shape predictor and analytic 3D shape synthesis. In our results, we show extensive evaluations on a newly-introduced shape completion benchmark for both real-world and synthetic data.