In this paper, we propose a novel unsupervised method named GP-Aligner to address the problem of groupwise non-rigid point set registration. Compared to previous non-learning-based approaches, the proposed method gains competitive advantages by leveraging deep neural networks to effectively and efficiently align a large number of highly deformed 3D shapes with superior performance. Unlike most learning-based methods that use an explicit feature encoding network to extract per-shape features and their correlations, our model leverages a model-free learnable latent descriptor to characterize shape correlations among groups. More specifically, for a given group we first define an optimizable Group Latent Descriptor (GLD) to characterize the relationship among a group of point sets. Each GLD is randomly initialized from a Gaussian distribution and then concatenated with the coordinates of each point of the associated point sets in the group. A neural network-based decoder network is further constructed to predict the coherent flow fields to optimally deform the input groups of shapes to the aligned ones. During the optimization process, GP-Aligner jointly updates all GLDs and weight parameters of the decoder network towards the minimization of an unsupervised groupwise alignment loss. After optimization, for each group, our model coherently drives each point set towards a mean position (shape) without specifying one as the target. GP-Aligner does not require large-scale training data for network training, and it can directly align groups of point sets in a one-stage optimization process. GP-Aligner shows both accuracy and computational efficiency improvement in comparison with the state-of-the-art methods for groupwise point set registration. Moreover, GP-Aligner exhibits high efficiency in aligning a large number of groups of real-world 3D shapes.

Real-world networks, such as phone-call networks and social networks, are often not static but temporal. Mining cohesive subgraphs from static graphs is a fundamental task in network analysis and has been widely investigated in the past decades. However, the concepts of cohesive subgraphs shift from static to temporal graphs raise many important problems. For instance, how to detect stable cohesive subgraphs on temporal networks such that the nodes in the subgraph are densely and stably connected over time. To address this problem, we resort to the conventional quasi-clique and propose a new model, called maximal$\rho $-stable ($\delta, \gamma $)-quasi-clique, to capture both the cohesiveness and the stability of a subgraph. We show that the problem of enumerating all maximal$\rho $-stable ($\delta, \gamma $)-quasi-cliques is NP-hard. To efficiently tackle our problem, we first devise a novel temporal graph reduction algorithm to significantly reduce the temporal graph without losing any maximal$\rho $-stable ($\delta, \gamma $)-quasi-clique. Then, on the reduced temporal graph, we propose an effective branch and bound enumeration algorithm, namedBB&SCM, with four carefully designed pruning techniques to accomplish the enumeration process. Finally, we conduct extensive experiments on seven real-world temporal graphs, and the results demonstrate that the temporal graph reduction algorithm can safely reduce 98% nodes of the temporal graph (with millions of nodes and edges) andBB&SCMis at least two orders of magnitude faster than the baseline algorithms. Moreover, we also evaluate the effectiveness of our model against other baseline models.

3D objects modeling has gained considerable attention in the visual computing community. We propose a low-cost unsupervised learning model for 3D objects reconstruction from hand-drawn sketches. Recent advancements in deep learning opened new opportunities to learn high-quality 3D objects from 2D sketches via supervised networks. However, the limited availability of labeled 2D hand-drawn sketches data (i.e. sketches and its corresponding 3D ground truth models) hinders the training process of supervised methods. In this paper, driven by a novel design of combination of retrieval and reconstruction process, we developed a learning paradigm to reconstruct 3D objects from hand-drawn sketches, without the use of well-labeled hand-drawn sketch data during the entire training process. Specifically, the paradigm begins with the training of an adaption network via autoencoder with adversarial loss, embedding the unpaired 2D rendered image domain with the hand-drawn sketch domain to a shared latent vector space. Then from the embedding latent space, for each testing sketch image, we retrieve a few (e.g. five) nearest neighbors from the training 3D data set as prior knowledge for a 3D Generative Adversarial Network. Our experiments verify our network's robust and superior performance in handling 3D volumetric object generation from single hand-drawn sketch without requiring any 3D ground truth labels.