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Three-dimensional LiDAR systems that capture point cloud data enable the simultaneous acquisition of spatial geometry and multi-wavelength intensity information, thereby paving the way for three-dimensional point cloud recognition and processing. However, due to the irregular distribution, low resolution of point clouds, and limited spatial recognition accuracy in complex environments, inherent errors occur in classifying and segmenting the acquired target information. Conversely, two-dimensional visible light images provide real-color information, enabling the distinction of object contours and fine details, thus yielding clear, high-resolution images when desired. The integration of two-dimensional information with point clouds offers complementary advantages. In this paper, we present the incorporation of two-dimensional information to form a multi-modal representation. From this, we extract local features to establish three-dimensional geometric relationships and two-dimensional color relationships. We introduce a novel network model, termed MInet (Multi-Information net), which effectively captures features relating to both two-dimensional color and three-dimensional pose information. This enhanced network model improves feature saliency, thereby facilitating superior segmentation and recognition tasks. We evaluate our MInet architecture using the ShapeNet and ThreeDMatch datasets for point cloud segmentation, and the Stanford dataset for object recognition. The robust results, coupled with quantitative and qualitative experiments, demonstrate the superior performance of our proposed method in point cloud segmentation and object recognition tasks. [ABSTRACT FROM AUTHOR]

To overcome the inherent bandwidth limitation of non-line-of-sight (NLOS) ultraviolet communication (UVC) channel, a novel space division multiplexing (SDM) framework based on non-coplanar rotation angle is established. The influence of SDM structure parameters on inter-channel interference (ICI) is studied to obtain the optimal antenna configuration. And a Gaussian approximate distribution (GAD) signal detection model is presented, which simplifies the calculation of the Bit error rate (BER) of the Photon multiplier tube (PMT) reception and is suitable for complex ICI analysis. Furthermore, a low ICI scheme based on the relationship between BER and the interference factor is proposed to increase the data transmission rate or reduce the alignment configuration. As a result, the maximum data rate increases to 34 Mbps, or the alignment demand decreases by 33.33% at most. Moreover, these analysis results and techniques can be applied to other NLOS scattering optical wireless communication systems. [ABSTRACT FROM AUTHOR]

Point cloud registration based on local descriptors plays a crucial role in 3D computer vision applications. However, existing methods often suffer from limitations such as low accuracy, a large memory footprint, and slow speed, particularly when dealing with 3D point clouds from low-cost sensors. To overcome these challenges, we propose an efficient local descriptor called Binary Weighted Projection-point Height (BWPH) for point cloud registration. The core idea behind the BWPH descriptor is the integration of Gaussian kernel density estimation with weighted height characteristics and binarization components to encode distinctive information for the local surface. Through extensive experiments and rigorous comparisons with state-of-the-art methods, we demonstrate that the BWPH descriptor achieves high matching accuracy, strong compactness, and feasibility across contexts. Moreover, the proposed BWPH-based point cloud registration successfully registers real datasets acquired by low-cost sensors with small errors, enabling accurate initial alignment positions.

The Segmented Planar Imaging Detector for Electro-Optical Reconnaissance (SPIDER) is a small volume, lightweight, low energy consumption, and high-resolution system expected to replace traditional large aperture telescopes for long-distance detection. In this paper, an improved SPIDER system is proposed, which uses a multimode interference (MMI) coupler instead of an orthogonal detector, and successfully doubles the space spectrum coverage. We present a three-point configuring method to configure lenslets, calculate spatial spectrum values from the output currents obtained by MMI. By comparing the performance of the MMI-SPIDER and SPIDER systems through simulations, we demonstrate that the former has more complete spatial spectrum coverage, resulting in better image restoration quality.