MS23D: A 3D object detection method using multi-scale semantic feature points to construct 3D feature layer.

LiDAR point clouds can effectively depict the motion and posture of objects in three-dimensional space. Many studies accomplish the 3D object detection by voxelizing point clouds. However, in autonomous driving scenarios, the sparsity and hollowness of point clouds create some difficulties for voxel-based methods. The sparsity of point clouds makes it challenging to describe the geometric features of objects. The hollowness of point clouds poses difficulties for the aggregation of 3D features. We propose a two-stage 3D object detection framework, called MS23D. (1) We propose a method using voxel feature points from multi-branch to construct the 3D feature layer. Using voxel feature points from different branches, we construct a relatively compact 3D feature layer with rich semantic features. Additionally, we propose a distance-weighted sampling method, reducing the loss of foreground points caused by downsampling and allowing the 3D feature layer to retain more foreground points. (2) In response to the hollowness of point clouds, we predict the offsets between deep-level feature points and the object's centroid, making them as close as possible to the object's centroid. This enables the aggregation of these feature points with abundant semantic features. For feature points from shallow-level, we retain them on the object's surface to describe the geometric features of the object. To validate our approach, we evaluated its effectiveness on both the KITTI and ONCE datasets.

Efficient three-dimensional point cloud object detection based on improved Complex-YOLO.

Lidar-based 3D object detection and classification is a critical task for autonomous driving. However, inferencing from exceedingly sparse 3D data in real-time is a formidable challenge. Complex-YOLO solves the problem of point cloud disorder and sparsity by projecting it onto the bird's-eye view and realizes real-time 3D object detection based on LiDAR. However, Complex-YOLO has no object height detection, a shallow network depth, and poor small-size object detection accuracy. To address these issues, this paper has made the following improvements: (1) adds a multi-scale feature fusion network to improve the algorithm's capability to detect small-size objects; (2) uses a more advanced RepVGG as the backbone network to improve network depth and overall detection performance; and (3) adds an effective height detector to the network to improve the height detection. Through experiments, we found that our algorithm's accuracy achieved good performance on the KITTI dataset, while the detection speed and memory usage were very superior, 48FPS on RTX3070Ti and 20FPS on GTX1060, with a memory usage of 841Mib.

Tailoring entanglement through domain engineering in a lithium niobate waveguide.

We propose to integrate the electro-optic (EO) tuning function into on-chip domain engineered lithium niobate (LN) waveguide. Due to the versatility of LN, both the spontaneously parametric down conversion (SPDC) and EO interaction could be realized simultaneously. Photon pairs are generated through SPDC, and the formation of entangled state is modulated by EO processes. An EO tunable polarization-entangled photon state is proposed. Orthogonally-polarized and parallel-polarized entanglements of photon pairs are instantly switchable by tuning the applied field. The characteristics of the source are theoretically investigated showing adjustable bandwidths and high entanglement degrees. Moreover, other kinds of reconfigurable entanglement are also achievable based on suitable domain-design. We believe tailoring entanglement based on domain engineering is a very promising solution for next generation function-integrated quantum circuits.

Experimental preparation of quadripartite cluster and Greenberger-Horne-Zeilinger entangled states for continuous variables.

The cluster states and Greenberger-Horne-Zeilinger (GHZ) states are two different types of multipartite quantum entangled states. We present the first experimental results generating continuous variable quadripartite cluster and GHZ entangled states of electromagnetic fields. Utilizing two amplitude-quadrature and two phase-quadrature squeezed states of light and linearly optical transformations, the two types of entangled states for amplitude and phase quadratures of light are experimentally produced. The combinations of the measured quadrature variances prove the full inseparability of the generated four subsystems. The presented experimental schemes show that the multipartite entanglement of continuous variables can be deterministically generated with the relatively simple implementation.

Experimental demonstration of quantum entanglement between frequency-nondegenerate optical twin beams.

The quantum entanglement of amplitude and phase quadratures between two intense optical beams with a total intensity of 22 mW and a frequency difference of 1 nm, which are produced from an optical parametric oscillator operating above threshold, is experimentally demonstrated with two sets of unbalanced Mach-Zehnder interferometers. The measured quantum correlations of intensity and phase are in reasonable agreement with the results calculated based on a semiclassical analysis of the noise characteristics given by Fabre et al. [J. Phys. (France) 50, 1209 (1989)].