  Recovering the 3D shape of an object from single or multiple images with deep
neural networks has been attracting increasing attention in the past few years.
Mainstream works (e.g. 3D-R2N2) use recurrent neural networks (RNNs) to
sequentially fuse feature maps of input images. However, RNN-based approaches
are unable to produce consistent reconstruction results when given the same
input images with different orders. Moreover, RNNs may forget important
features from early input images due to long-term memory loss. To address these
issues, we propose a novel framework for single-view and multi-view 3D object
reconstruction, named Pix2Vox++. By using a well-designed encoder-decoder, it
generates a coarse 3D volume from each input image. A multi-scale context-aware
fusion module is then introduced to adaptively select high-quality
reconstructions for different parts from all coarse 3D volumes to obtain a
fused 3D volume. To further correct the wrongly recovered parts in the fused 3D
volume, a refiner is adopted to generate the final output. Experimental results
on the ShapeNet, Pix3D, and Things3D benchmarks show that Pix2Vox++ performs
favorably against state-of-the-art methods in terms of both accuracy and
efficiency.
