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。

使用3D编码器-预测器CNN和形状合成进行形状补全