Learning Universal Vector Representation for Objects of Different 3D Euclidean formats

We present a method for learning universal vector representations out of 3D objects represented in different data formats. A newly proposed switching mechanism is used in the design of neural network architecture. During the learning process, the encoder for one specific format also learns to perceive the object from the perspective of other formats, hence the learned universal representation contains richer information. With the learned universal representation, it would be possible to "translate" between different 3D shape formats of the input object since they share similar embedding of 3D information. Higher performance can also be achieved for the 3D data synthetic tasks with this method

LPMNet: Latent Part Modification and Generation for 3D Point Clouds

In this paper, we focus on latent modification and generation of 3D point cloud object models with respect to their semantic parts. Different to the existing methods which use separate networks for part generation and assembly, we propose a single end-to-end Autoencoder model that can handle generation and modification of both semantic parts, and global shapes. The proposed method supports part exchange between 3D point cloud models and composition by different parts to form new models by directly editing latent representations. This holistic approach does not need part-based training to learn part representations and does not introduce any extra loss besides the standard reconstruction loss. The experiments demonstrate the robustness of the proposed method with different object categories and varying number of points. The method can generate new models by integration of generative models such as GANs and VAEs and can work with unannotated point clouds by integration of a segmentation module