We introduce a data-driven approach to complete partial 3D shapes through a
combination of volumetric deep neural networks and 3D shape 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
fine-scale 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.

本篇论文提出一种数据驱动的方法，结合使用体积深度神经网络和 3D 形状合成来完成部分扫描的 3D 形状。该方法使用 3D 卷积层来对填充缺失数据进行预测，旨在在未知区域精确地预测全局结构，并使用来自形状数据库的 3D 几何图形进行中间结果的相关处理。最后，通过基于补丁的 3D 形状合成方法，根据 3D-EPN 获取的全局网格结构作为约束，生成高分辨率的输出，重建精细的细节。