  Recent advances in convolutional neural networks have shown promising results
in 3D shape completion. But due to GPU memory limitations, these methods can
only produce low-resolution outputs. To inpaint 3D models with semantic
plausibility and contextual details, we introduce a hybrid framework that
combines a 3D Encoder-Decoder Generative Adversarial Network (3D-ED-GAN) and a
Long-term Recurrent Convolutional Network (LRCN). The 3D-ED-GAN is a 3D
convolutional neural network trained with a generative adversarial paradigm to
fill missing 3D data in low-resolution. LRCN adopts a recurrent neural network
architecture to minimize GPU memory usage and incorporates an Encoder-Decoder
pair into a Long Short-term Memory Network. By handling the 3D model as a
sequence of 2D slices, LRCN transforms a coarse 3D shape into a more complete
and higher resolution volume. While 3D-ED-GAN captures global contextual
structure of the 3D shape, LRCN localizes the fine-grained details.
Experimental results on both real-world and synthetic data show reconstructions
from corrupted models result in complete and high-resolution 3D objects.
