Free-space optical (FSO) communication offers high capacity, license-free spectrum, and immunity to electromagnetic interference, making it a promising solution for future wireless links. However, practical deployment of FSO systems remains challenged by issues of link security and reliable transmission under atmospheric turbulence. In this work, we investigate the use of nonseparability encoding based on vectorial structured light as a turbulence-resilient and secure transmission approach. Through simulations and experiments, we compare multi-level nonseparability encoding with conventional amplitude modulation under various turbulence strengths and eavesdropping scenarios. The results demonstrate that vectorial structured light exhibits inherent security advantages, imposing higher optical signal-to-noise ratio penalties on eavesdroppers and making information recovery from partial interception significantly more difficult. These findings confirm the potential of nonseparability-based encoding for enhancing both security and robustness in FSO communication systems.

Structured light illumination is an active 3D scanning technique based on projecting and capturing a set of striped patterns and measuring the warping of the patterns as they reflect off a target object's surface. As designed, each pixel in the camera sees exactly one pixel from the projector; however, there are multi-path situations where a camera pixel sees light from multiple projector positions. In the case of bimodal multi-path, the camera pixel receives light from exactly two positions, which occurs along a step edge where the edge slices through a pixel which, therefore, sees both a foreground and background surface. In this paper, we present a general mathematical model to address this bimodal multi-path issue in a phase-shifting or so-called phase-measuring-profilometry scanner to measure the constructive and destructive interference between the two light paths, and by taking advantage of this interference, separate the paths and make two decoupled depth measurements. We validate our algorithm with both simulations and a number of challenging real-world scenarios, significantly outperforming the state-of-the-art methods.

Convenient and high-fidelity 3D model reconstruction is crucial for industries like manufacturing, medicine and archaeology. Current scanning approaches struggle with high manual costs and the accumulation of errors in large-scale modeling. This paper is dedicated to achieving industrial-grade seamless and high-fidelity 3D reconstruction with minimal manual intervention. The innovative method proposed transforms the multi-frame registration into a graph optimization problem, addressing the issue of error accumulation encountered in frame-by-frame registration. Initially, a global consistency cost is established based on point cloud cross-multipath registration, followed by using the geometric and color differences of corresponding points as dynamic nonlinear weights. Finally, the iteratively reweighted least squares (IRLS) method is adopted to perform the bundle adjustment (BA) optimization of all poses. Significantly enhances registration accuracy and robustness under the premise of maintaining near real-time efficiency. Additionally, for generating watertight, seamless surface models, a local-to-global transitioning strategy for multiframe fusion is introduced. This method facilitates efficient correction of normal vector consistency, addressing mesh discontinuities in surface reconstruction resulting from normal flips. To validate our algorithm, we designed a 3D reconstruction platform enabling spatial viewpoint transformations. We collected extensive real and simulated model data. These datasets were rigorously evaluated against advanced methods, roving the effectiveness of our approach. Our data and implementation is made available on GitHub for community development.

Ridge waveguides are key components in passive integrated circuits, silicon modulators, and hybrid lasers. However, TM polarization modes in shallow-etched ridge waveguides suffer from unexpected leakage loss due to the TM-TE polarization conversion. The leakage loss of TM modes can be suppressed to minimums only in specific ridge widths, which limits the diverse components design, as known as accidental bound states in the continuum. In this paper, we put forward and experimentally demonstrated an effective strategy for a universal high TM-mode transmission ridge waveguide. By introducing the hexagonal lattice photonic crystal into the slab region, we reduced the equivalent-medium index of the local slab and suppressed the TE-TM polarization conversion strength. For TM 0 mode, the maximum transmission improvement is over 16 dB in theory, and we also verified the feasibility of high-order TM mode. In the wavelength range of 1350-1450 nm, the experimental result shows the distinct advantages in low-loss TM 0 mode transmission and resonance suppression. The minimum loss is below 1 dB at the wavelength of 1397 nm, and the max transmission improvement above 20 dB is realized near the wavelength of 1427 nm. That method is meaningful for overcoming the limitation of bound states in the continuum and expanding diverse ridge waveguide devices for TM polarization modes.

Structured light (SL) systems acquire high-fidelity 3D geometry with active illumination projection. Conventional systems exhibit challenges when working in environments with strong ambient illumination. This paper studies a general-purposed solution to improve the robustness of SL by projecting a redundant number of patterns. Despite sacrificing the signal-noise-ratio at each frame, projected signals become more distinguishable from errors. Thus, the geometry can be recovered easily. We systematically analyze the redundant SL code design rules to achieve high accuracy with minimum redundancy. Based on the more reliable correspondence cost volume and the natural image prior, we integrate spatial context-aware disparity estimators into our system to further boost performance. We also demonstrate the application of such techniques in iterative error detection and refinement. We demonstrate significant performance improvements of efficient redundant code SL systems in both simulations and challenging real-world scenes.

The real-time and accurate three-dimensional object detection is one of the core tasks in the perception of autonomous driving environments. In recent years, the development of deep learning technology and lidar technology has led to significant advancements in the application of three-dimensional object detection algorithms in large-scale general scenarios. However, existing lidar-based three-dimensional object detection algorithms still face challenges in complex traffic scenarios, and the difficulty lies in balancing the accuracy and inference speed of the algorithms. In this regard, the voxel-based single-stage three-dimensional object detection algorithm SECOND is used as the baseline algorithm and an efficient single-stage vehicle detection algorithm framework tailored for complex autonomous driving scenarios is proposed. Firstly, a residual structure is introduced and the feature channel number is reconstructed in the three-dimensional feature extraction backbone, which effectively reduce the loss of spatial geometric features in the point cloud during the feature extraction process and make the model training more stable. Secondly, the multi-scale feature fusion technology and a spatial feature attention mechanism are introduced and a more efficient two-dimensional feature fusion backbone is designed, which facilitates the learning of the model for vehicle size and orientation. The proposed algorithm is trained and validated on the open-source dataset ONCE. Compared to the baseline algorithm, the average detection accuracy for vehicles is improved by 5.64%, while maintaining an inference speed of 20 frames per second (FPS). This significantly enhances the algorithm's perception performance for vehicles in complex traffic scenarios.

Structured light (SL) systems acquire high-fidelity 3D geometry with active illumination projection. Conventional systems exhibit challenges when working in environments with strong ambient illumination, global illumination and cross-device interference. This paper proposes a general-purposed technique to improve the robustness of SL by projecting redundant optical signals in addition to the native SL patterns. In this way, projected signals become more distinguishable from errors. Thus the geometry information can be more easily recovered using simple signal processing and the ``coding gain" in performance is obtained. We propose three applications using our redundancy codes: (1) Self error-correction for SL imaging under strong ambient light, (2) Error detection for adaptive reconstruction under global illumination, and (3) Interference filtering with device-specific projection sequence encoding, especially for event camera-based SL and light curtain devices. We systematically analyze the design rules and signal processing algorithms in these applications. Corresponding hardware prototypes are built for evaluations on real-world complex scenes. Experimental results on the synthetic and real data demonstrate the significant performance improvements in SL systems with our redundancy codes.

Three-dimensional scanning by means of structured light illumination is an active imaging technique involving projecting and capturing a series of striped patterns and then using the observed warping of stripes to reconstruct the target object’s surface through triangulating each pixel in the camera to a unique projector coordinate corresponding to a particular feature in the projected patterns. The undesirable phenomenon of multi-path occurs when a camera pixel simultaneously sees features from multiple projector coordinates. Bimodal multi-path is a particularly common situation found along step edges, where the camera pixel sees both a foreground and background surface. Generalized from bimodal multi-path, this paper examines the phenomenon of sparse or N-modal multi-path as a more general case, where the camera pixel sees no fewer than two reflective surfaces, resulting in decoding errors. Using fringe projection profilometry, our proposed solution is to treat each camera pixel as an underdetermined linear system of equations and to find the sparsest (least number of paths) solution by taking an application-specific Bayesian learning approach. We validate this algorithm with both simulations and a number of challenging real-world scenarios, demonstrating that it outperforms state-of-the-art techniques.

This paper presents an automated calibration procedure for modeling the light field between a digital mirror device and a camera sensor for coded aperture based compressive spectral imagers and high dynamic range cameras.

Structured light illumination is an active 3-D scanning technique based on projecting/capturing a set of striped patterns and measuring the warping of the patterns as they reflect off a target object's surface. In the case of phase measuring profilometry (PMP), the projected patterns are composed of a rolling sinusoidal wave, but as a set of time-multiplexed patterns, PMP requires the target surface to remain motionless or for scanning to be performed at such high rates that any movement is small. But high speed scanning places a significant burden on the projector electronics to produce contone patterns inside of short exposure intervals. Binary patterns are, therefore, of great value, but converting contone patterns into binary comes with significant risk. As such, this paper introduces a contone-to-binary conversion algorithm for deriving binary patterns that best mimic their contone counterparts. Experimental results will show a greater than 3 times reduction in pattern noise over traditional halftoning procedures.

We present a practical and inexpensive method to reconstruct 3D scenes that include transparent and mirror objects. Our work is motivated by the need for automatically generating 3D models of interior scenes, which commonly include glass. These large structures are often invisible to cameras or even to our human visual system. Existing 3D reconstruction methods for transparent objects are usually not applicable in such a room-sized reconstruction setting. Our simple hardware setup augments a regular depth camera (e.g., the Microsoft Kinect camera) with a single ultrasonic sensor, which is able to measure the distance to any object, including transparent surfaces. The key technical challenge is the sparse sampling rate from the acoustic sensor, which only takes one point measurement per frame. To address this challenge, we take advantage of the fact that the large scale glass structures in indoor environments are usually either piece-wise planar or a simple parametric surface. Based on these assumptions, we have developed a novel sensor fusion algorithm that first segments the (hybrid) depth map into different categories such as opaque/transparent/infinity (e.g., too far to measure) and then updates the depth map based on the segmentation outcome. We validated our algorithms with a number of challenging cases, including multiple panes of glass, mirrors, and even a curved glass cabinet.

We present a practical and inexpensive method to reconstruct 3D scenes that include piece-wise planar transparent objects. Our work is motivated by the need for automatically generating 3D models of interior scenes, in which glass structures are common. These large structures are often invisible to cameras or even our human visual system. Existing 3D reconstruction methods for transparent objects are usually not applicable in such a room-size reconstruction setting. Our approach augments a regular depth camera (e.g., the Microsoft Kinect camera) with a single ultrasonic sensor, which is able to measure distance to any objects, including transparent surfaces. We present a novel sensor fusion algorithm that first segments the depth map into different categories such as opaque/transparent/infinity (e.g., too far to measure) and then updates the depth map based on the segmentation outcome. Our current hardware setup can generate only one additional point measurement per frame, yet our fusion algorithm is able to generate satisfactory reconstruction results based on our probabilistic model. We highlight the performance in many challenging indoor benchmarks.

Developing high efficient modulation technique has played an important role in the field of communication. In recent years, various modulation techniques have being brought out. The technique of ultra narrow bandwidth, with high efficiency in bandwidth and high suppression to sidebands as the main highlights, has developed particularly rapidly. This paper modifies the very-minimum waveform difference keying (VWDK), which is a key technique in ultra narrow bandwidth, proposes a new expression of waveform, which has higher efficiency, and applies it in traditional amplitude modulation systems. The new systems combing with VWDK and amplitude modulation can transmit the analog signals with high speed digital signals by the carriers.