Sketch-based 3D Shape Retrieval using Convolutional Neural Networks

Retrieving 3D models from 2D human sketches has received considerable attention in the areas of graphics, image retrieval, and computer vision. Almost always in state of the art approaches a large amount of "best views" are computed for 3D models, with the hope that the query sketch matches one of these 2D projections of 3D models using predefined features. We argue that this two stage approach (view selection -- matching) is pragmatic but also problematic because the "best views" are subjective and ambiguous, which makes the matching inputs obscure. This imprecise nature of matching further makes it challenging to choose features manually. Instead of relying on the elusive concept of "best views" and the hand-crafted features, we propose to define our views using a minimalism approach and learn features for both sketches and views. Specifically, we drastically reduce the number of views to only two predefined directions for the whole dataset. Then, we learn two Siamese Convolutional Neural Networks (CNNs), one for the views and one for the sketches. The loss function is defined on the within-domain as well as the cross-domain similarities. Our experiments on three benchmark datasets demonstrate that our method is significantly better than state of the art approaches, and outperforms them in all conventional metrics.Comment: CVPR 201

Exile, Deception and Magic Revelation: A Thematic Exploration of Shakespeare’s Pastorals of Love

Though the theme of love by no means gains the very popularity among Shakespeare’s works, he does not simply relate a story of romance, the affection between male or female, but entirely every aspect of human love (the parental love, the brotherly love, the sisterly love as well as the friendly love). That is, love does exactly have been dissected into a multilayered pyramid in his hands. Exemplified in As You Like It, The Tempest, The Winter’s Tale, Shakespeare takes exile as a kind of precondition, getting the heroes and heroines out of the court into the forest, island and countryside with a disguised appearance and identity which performs as some means of deception, more or less casting magic as the promotive power in order to explore the extensive knowledge of love. In doing so, love then is endowed not only a purified but also a unified function and finally becomes the symbol of harmony

Design of Block Transceivers with Decision Feedback Detection

This paper presents a method for jointly designing the transmitter-receiver pair in a block-by-block communication system that employs (intra-block) decision feedback detection. We provide closed-form expressions for transmitter-receiver pairs that simultaneously minimize the arithmetic mean squared error (MSE) at the decision point (assuming perfect feedback), the geometric MSE, and the bit error rate of a uniformly bit-loaded system at moderate-to-high signal-to-noise ratios. Separate expressions apply for the ``zero-forcing'' and ``minimum MSE'' (MMSE) decision feedback structures. In the MMSE case, the proposed design also maximizes the Gaussian mutual information and suggests that one can approach the capacity of the block transmission system using (independent instances of) the same (Gaussian) code for each element of the block. Our simulation studies indicate that the proposed transceivers perform significantly better than standard transceivers, and that they retain their performance advantages in the presence of error propagation.Comment: 14 pages, 8 figures, to appear in the IEEE Transactions on Signal Processin

Cross-Scale Cost Aggregation for Stereo Matching

Human beings process stereoscopic correspondence across multiple scales. However, this bio-inspiration is ignored by state-of-the-art cost aggregation methods for dense stereo correspondence. In this paper, a generic cross-scale cost aggregation framework is proposed to allow multi-scale interaction in cost aggregation. We firstly reformulate cost aggregation from a unified optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Then, an inter-scale regularizer is introduced into optimization and solving this new optimization problem leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation methods can be integrated into the proposed general framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets.Comment: To Appear in 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2014 (poster, 29.88%