Approximating Signed Distance Field to a Mesh by Artificial Neural Networks

Previous research has resulted in many representations of surfaces for rendering. However, for some approaches, an accurate representation comes at the expense of large data storage. Considering that Artifcial Neural Networks (ANNs) have been shown to achieve good performance in approximating non-linear functions in recent years, the potential to apply them to the problem of surface representation needs to be investigated. The goal in this research is to exploring how ANNs can effciently learn the Signed Distance Field (SDF) representation of shapes. Specifcally, we investigate how well different architectures of ANNs can learn 2D SDFs, 3D SDFs, and SDFs approximating a complex triangle mesh. In this research, we performed three main experiments to determine which ANN architectures and confgurations are suitable for learning SDFs by analyzing the errors in training and testing as well as rendering results. Also, three different pipelines for rendering general SDFs, grid-based SDFs, and ANN based SDFs were implemented to show the resulting images on screen. The following data are measured in this research project: the errors in training different architectures of ANNs; the errors in rendering SDFs; comparison between grid-based SDFs and ANN based SDFs. This work demonstrates the use of using ANNs to approximate the SDF to a mesh by learning the dataset through training data sampled near the mesh surface, which could be a useful technique in 3D reconstruction and rendering. We have found that the size of trained neural network is also much smaller than either the triangle mesh or grid-based SDFs, which could be useful for compression applications, and in software or hardware that has a strict requirement of memory size

Characteristic length of a Holographic Superconductor with dd-wave gap

After the discovery of the $s$-wave and $p$-wave holographic superconductors, holographic models of $d$-wave superconductor have also been constructed recently. We study analytically the perturbation of the dual gravity theory to calculate the superconducting coherence length $\xi$ of the $d$-wave holographic superconductor near the superconducting phase transition point. The superconducting coherence length $\xi$ divergents as $(1-T/T_c)^{-1/2}$ near the critical temperature $T_c$. We also obtain the magnetic penetration depth $\lambda\propto(T_c-T)^{-1/2}$ by adding a small external homogeneous magnetic field. The results agree with the $s$-wave and $p$-wave models, which are also the same as the Ginzburg-Landau theory.Comment: last version, 10 pages, accepted by PR

Hybrid Video Stabilization for Mobile Vehicle Detection on SURF in Aerial Surveillance

Detection of moving vehicles in aerial video sequences is of great importance with many promising applications in surveillance, intelligence transportation, or public service applications such as emergency evacuation and policy security. However, vehicle detection is a challenging task due to global camera motion, low resolution of vehicles, and low contrast between vehicles and background. In this paper, we present a hybrid method to efficiently detect moving vehicle in aerial videos. Firstly, local feature extraction and matching were performed to estimate the global motion. It was demonstrated that the Speeded Up Robust Feature (SURF) key points were more suitable for the stabilization task. Then, a list of dynamic pixels was obtained and grouped for different moving vehicles by comparing the different optical flow normal. To enhance the precision of detection, some preprocessing methods were applied to the surveillance system, such as road extraction and other features. A quantitative evaluation on real video sequences indicated that the proposed method improved the detection performance significantly