Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images
This addresses the issue of reliable 3D shape recovery for computer vision applications, offering an incremental improvement over existing methods.
The paper tackles the problem of inconsistent and memory-loss-prone 3D object reconstruction from images by proposing Pix2Vox++, which uses a multi-scale context-aware fusion module and refiner to improve accuracy and efficiency, achieving favorable results against state-of-the-art methods on benchmarks like ShapeNet, Pix3D, and Things3D.
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.