Meet me at CVPR'18: Tuesday, June 19th, I will be presenting our work on weakly-supervised 3D shape completion.


Abhishek Sharma, Oliver Grau, Mario Fritz. VConv-DAE: Deep Volumetric Shape Learning Without Object Labels. ECCV Workshops, 2016.

Sharma et al. use a volumetric convolutional denoising auto-encoder for shape completion and classification on the ModelNet [] Dataset. Their approach is comparably simple — the extension of the regular denoising auto-encoder to volumetric data is straight forwards on the used resolution of $30^3$. The architecture is quite simple and illustrated in Figure 1. A dropout layer after the input simulates noise and a bottleneck layer of dimensionality $6912$ is used.


Figure 1 (click to enlarge): The volumetric, convolutional denoising auto-encoder architecture used by Sharma et al.


In experiments on the ModelNet [] Dataset, they demonstrate the applicability of their model for classification and shape completion. For classification they outperform the ShapeNet model [] both when training an SVM on the $6912$ dimensional representation and when fine-tuning by adding two additional fully connected layers. However, VoxNet [] still outperforms their approach. Qualitative results for shape completion on random noise and slicing noise are shown in Figure 2 and 3, respectively. It seems as if the model struggles most with the low resolution. Especially for slicing noise, the model performs poorly because of the low resolution.


Figure 2 (click to enlarge): Qualitative results for shape completion from random noise. Comparison to ShapeNet [].

Figure 3 (click to enlarge): Qualitative results of shape completion on slicing noise and comparison to ShapeNet [].

  • [] Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: A deep representation for volumetric shapes. In: CVPR. (2015).
  • [] Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.G.: Multi-view convolutional neural networks for 3d shape recognition. In: Proc. ICCV. (2015).

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below or get in touch with me: