IAM

DAVIDSTUTZ

02ndMAY2017

READING

Angela Dai, Charles Ruizhongtai Qi, Matthias Nießner. Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis. CoRR, 2016.

Dai et al. use a 3D convolutional neural network architecture called 3D-Encoder-Predictor Network for shape completion. Figure 1 illustrates the high-level pipeline. The first step consists of two networks which are combined in the framework of their 3D Encoder-Preodictor Network as illustrated in Figure 2. The input is a two channel volume encoding the signed truncated distance function (STDF) and the output is only a distance function (DF). Nearest neighbors of the output shape (in resolution $32^3$) are searched utilizing features taken from a 3D classification network following []. Finally, the output volume and the nearest neighbor shapes are used to produce a higher-resolution mesh ($128^3$), see the paper for details.

Figure 1 (click to enlarge): High-level illustration of the proposed approach as described in the text.

Figure 2 (click to enlarge): The network architecture used for shape completion.

  • [] C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. Guibas. Volumetric and multi-view cnns for object classification on 3D data. In Proc. Computer Vision and Pattern Recognition (CVPR), 2016.
What is your opinion on this article? Let me know your thoughts on Twitter @davidstutz92 or LinkedIn in/davidstutz92.