Chao Ping Chen

We present an ultrawide waveguide near-eye display as a unified architecture compatible with both augmented reality and virtual reality. The core idea is to segment the field of view into 2×2 channels, with each corresponding to one quarter of the field of view. Compared with the traditional field-of-view expansion methods, i.e., dividing the field of view along a single dimension, two-dimensional field-of-view division can push the envelope further. The upper limit of the field of view is illustrated and deduced. The design rules of the waveguide, gradient-linewidth grating as the in-coupler, and two-dimensional binary grating as the out-coupler are expounded. Through the Fourier modal method, the efficiencies of gratings can be calculated and optimized. As an overall evaluation, its key performance indicators are summarized as follows: field of view is 120 deg (diagonal), eye relief is 10 mm, exit pupil is 5.3 mm×5.3 mm, and pupil uniformity is 81%.

For simulating the actual diffraction characteristics of the volume holographic grating (VHG), this paper incorporates multiple periodic gratings into the finite element method (FEM) analysis. The grating periods and refractive index modulation are modeled according to statistical distribution. Thus, the refractive index distribution within the gratings is reconstructed through the Fourier expansion. Utilizing the enhanced model, a quantitative analysis was performed to examine the relationships among model parameters, in relation to the diffraction characteristics. The results demonstrated that the optimized model achieves a correlation degree R ² of 98% with experimental data. This model also provided a reliable theoretical basis for the precise design of optical systems based on VHGs.

Computer‐generated holography (CGH) has advanced the development of human‐centric holographic near‐eye displays. Recent work has proposed an end‐to‐end convolutional neural network that converts 2D images into 3D holograms, enabling real‐time 3D holographic displays from widespread available images. However, the high computational cost of deep learning‐based methods limits their practical application. Deploying such algorithms on resource‐constrained mobile platforms requires more efficient models with reduced computational memory and power demands, which plays a crucial role in promoting human‐centric virtual reality/augmented reality displays. In this article, a lightweight 3D hologram generation model is proposed using neural network quantization from the input of single 2D image. Specifically, a 2D‐to‐3D CGH model is quantized from 32‐bit floating‐point to 8‐bit integer precision. The results show that the INT8 model reduces size by 60%, improves processing speed by a factor of three, and achieves comparable hologram quality to the FP32 model. This work enables the practical deployment of 2D‐to‐3D CGH model on low‐power platforms, bridging the gap between high‐performance holographic computation and real‐world wearable display systems.

This study proposes an image processing framework based on bionic principles to optimize 3D visual perception in virtual reality (VR) systems. By simulating the physiological mechanisms of the human visual system, the framework significantly enhances depth perception and visual fidelity in VR content. The research focuses on three core algorithms: Gabor texture feature extraction algorithm based on directional selectivity of neurons in the V1 region of the visual cortex, which enhances edge detection capability through fourth-order Gaussian kernel; improved Retinex model based on adaptive mechanism of retinal illumination, achieving brightness balance under complex illumination through horizontal–vertical dual-channel decomposition; the RGB adaptive adjustment algorithm, based on the three color response characteristics of cone cells, integrates color temperature compensation with depth cue optimization, enhances color naturalness and stereoscopic depth. Build a modular processing system on the Unity platform, integrate the above algorithms to form a collaborative optimization process, and ensure per-frame processing time meets VR real-time constraints. The experiment uses RMSE, AbsRel, and SSIM metrics, combined with subjective evaluation to verify the effectiveness of the algorithm. The results show that compared with traditional methods (SSAO, SSR, SH), our algorithm demonstrates significant advantages in simple scenes and marginal superiority in composite metrics for complex scenes. Collaborative processing of three algorithms can significantly improve depth map noise and enhance the user’s subjective experience. The research results provide a solution that combines biological rationality and engineering practicality for visual optimization in fields such as implantable metaverse, VR healthcare, and education.

The pancake optical system, which reduces optical thickness by folding the optical path through polarization elements, emerges as the leading solution for virtual reality displays. As manufacturing technologies evolve and consumer demand for higher image quality grows, pancake systems transition from flat to curved‐laminate configurations. This article analyzes the advantages of pancake systems incorporating curved‐laminated lenses over those employing conventional flat‐laminated lenses, with respect to form factor, imaging quality, and other aspects of optical performance. Both designs deliver an 8 mm exit pupil diameter, 15 mm eye relief, and a 105° field of view (FOV). The curved‐laminated system demonstrates higher image uniformity than conventional designs, with RMS spot diameters below two‐pixel dimensions across the field. Subsequent optimization expanded its FOV to 110° while RMS spot diameters consistently below 14.5 . Finally, a binocular prototype based on the design is fabricated, demonstrating exceptional visual clarity and accurate color reproduction.

Traditional offline high-definition maps have significant limitations owing to their high cost and difficulty in updating. Online vector maps represent a new trend in the development of autonomous driving. In this paper, we propose an online lane mapping system, which leverages both multi-sensor simultaneous localization and mapping (SLAM) and Catmull–Rom splines. The map is constructed in real-time through the localization provided by SLAM and the fitting of splines. Existing SLAM algorithms perform poorly in complex urban environments, a deep learning network is incorporated into the visual front-end of our system to improve stability. Deep learning can extract and study deeper and less observable features, so that the system enhances the accuracy of visual tracking in scenarios characterized by low texture, lighting variations, and rapid motion. The system state is optimized by a factor graph framework to achieve precise localization. To construct continuous and consistent online lane maps, our system combines SLAM localization results with spline curves to achieve lane connection. Additionally, we develop a convenient method for initializing, extending, and optimizing the control points of splines to obtain more precise results. Our method combines the geometric characteristics with historical observations of lanes, enhancing the association and consistency of maps. Experiments conducted on the mainstream datasets demonstrate that our system exhibits greater robustness and improves the accuracy of localization and mapping across diverse environments.

We present an ultra-wide near-eye display featuring penta-channel waveguide. The core idea is to divide its field of view into five by placing in-couplers within regions, which are merely exclusive to subsets of field of view.