In this paper, we aim at addressing two critical issues in the 3D detection task, including the exploitation of multiple sensors (namely LiDAR point cloud and camera image), as well as the inconsistency between the localization and classification confidence. To this end, we propose a novel fusion module to enhance the point features with semantic image features in a point-wise manner without any image annotations. Besides, a consistency enforcing loss is employed to explicitly encourage the consistency of both the localization and classification confidence. We design an end-to-end learnable framework named EP-Net to integrate these two components. Extensive experiments on the KITTI and SUN-RGBD datasets demonstrate the superiority of EPNet over the state-of-the-art methods. Codes and models are available at: https://github.com/happinesslz/EPNet.

In this paper, we focus on exploring the robustness of the 3D object detection in point clouds, which has been rarely discussed in existing approaches. We observe two crucial phenomena: 1) the detection accuracy of the hard objects, e.g., Pedestrians, is unsatisfactory, 2) when adding additional noise points, the performance of existing approaches decreases rapidly. To alleviate these problems, a novel TANet is introduced in this paper, which mainly contains a Triple Attention (TA) module, and a Coarse-to-Fine Regression (CFR) module. By considering the channel-wise, point-wise and voxel-wise attention jointly, the TA module enhances the crucial information of the target while suppresses the unstable cloud points. Besides, the novel stacked TA further exploits the multi-level feature attention. In addition, the CFR module boosts the accuracy of localization without excessive computation cost. Experimental results on the validation set of KITTI dataset demonstrate that, in the challenging noisy cases, i.e., adding additional random noisy points around each object, the presented approach goes far beyond state-of-the-art approaches. Furthermore, for the 3D object detection task of the KITTI benchmark, our approach ranks the first place on Pedestrian class, by using the point clouds as the only input. The running speed is around 29 frames per second.

No abstract

No abstract

Finding reliable correspondences between sets of feature points in two images remains challenging in case of ambiguities or strong transformations. In this paper, we define a photometric descriptor for virtual lines that join neighbouring feature points. We show that it can be used in the second-order term of existing graph matchers to significantly improve their accuracy. We also define a semi-local matching method based on this descriptor. We show that it is robust to strong transformations and more accurate than existing graph matchers for scenes with significant occlusions, including for very low inlier rates. Used as a preprocessor to filter outliers from match candidates, it significantly improves the robustness of RANSAC and reduces camera calibration errors.

To select a better kind of feature for radar specific emitter identification, the unintentional frequency and phase modulation features are compared through theoretic analysis and experimental verification with 104 real radar instances. Results show that the unintentional phase modulation feature outperforms the unintentional frequency modulation feature.

Over the last years lattice-based cryptography has received much attention due to versatile average-case problems like Ring-LWE or Ring-SIS that appear to be intractable by quantum computers. In this work we evaluate and compare implementations of Ring-LWE encryption and the bimodal lattice signature scheme (BLISS) on an 8-bit Atmel ATxmega128 microcontroller. Our implementation of Ring-LWE encryption provides comprehensive protection against timing side-channels and takes 24.9 ms for encryption and 6.7 ms for decryption. To compute a BLISS signature, our software takes 317 ms and 86 ms for verification. These results underline the feasibility of lattice-based cryptography on constrained devices.

In this work, an ultra-wideband and high-efficiency reflective linear-to-circular polarization converter based on an anisotropic metasurface is proposed, which is an orthotropic structure with a pair of mutually perpendicular symmetric axes u and v along ±45 • directions with respect to the vertical y axis. The simulated and experimental results show that the polarization converter can realize ultrawideband linear-to-circular polarization conversion at both x-and y-polarized incidences, its 3dB-axialratio-band is between 5.8 and 20.4 GHz, which is corresponding to a relative bandwidth of 112%; moreover, the polarization conversion efficiency (PCE) can be kept larger than 99.6% in the frequency range of 6.1-19.8GHz. In addition, to get an insight into the root cause of the LTC polarization conversion, a detailed theoretical analysis is presented, in which the conclusion is reached that in the case of neglecting thelittle dielectric loss, the axial ratio (AR) of the reflected wave can be completely determined by the phase difference between the two reflection coefficients at u-and v-polarized incidences, and any anisotropic metasurface can be used as an effective LTC polarization converter when the phase difference is close to ±90 • .