We propose a new approach for optical refocusing of three-dimensional (3-D) objects on their real depth without a pickup-range limitation based on subdivided-elemental image arrays (sub-EIAs) and local periodic δ-function arrays (L-PDFAs). The captured EIA from the 3-D objects locating out of the pickup-range, is divided into a number of sub-EIAs depending on the object distance from the lens array. Then, by convolving these sub-EIAs with each L-PDFA whose spatial period corresponds to the specific object’s depth, as well as whose size is matched to that of the sub-EIA, arrays of spatially-filtered sub-EIAs (SF-sub-EIAs) for each object depth can be uniquely extracted. From these arrays of SF-sub-EIAs, 3-D objects can be optically reconstructed to be refocused on their real depth. Operational principle of the proposed method is analyzed based on ray-optics. In addition, to confirm the feasibility of the proposed method in the practical application, experiments with test objects are carried out and the results are comparatively discussed with those of the conventional method.

Compared with traditional methods, the action recognition model based on 3D convolutional deep neural network captures spatio-temporal features more accurately, resulting in higher accuracy. However, the large number of parameters and computational requirements of 3D models make it difficult to deploy on mobile devices with limited computing power. In order to achieve an efficient video action recognition model, we have analyzed and compared classic lightweight network principles and proposed the 3D-ShuffleViT network. By deeply integrating the self-attention mechanism with convolution, we have introduced an efficient ACISA module that further enhances the performance of our proposed model. This has resulted in exceptional performance in both context-sensitive and context-independent action recognition, while reducing deployment costs. It is worth noting that our 3D-ShuffleViT network, with a computational cost of only 6% of that of SlowFast-ResNet101, achieved 98% of the latter’s Top1 accuracy on the EgoGesture dataset. Furthermore, on the same CPU (Intel i5-8300H), its speed was 2.5 times that of the latter. In addition, when we deployed our model on edge devices, our proposed network achieved the best balance between accuracy and speed among lightweight networks of the same order.

Rigid body localization (RBL) is to simultaneously estimate the position and attitude of a rigid target. In this paper, we focus on the RBL problem using a single base station (BS) and direction of arrival (DoA) measurements. Several wireless sensors are mounted on the rigid body of interest, and their topology information is known a priori. The single BS measures the DoAs of wireless sensor signals and fuses them with the sensor topology information to estimate the position and orientation of the rigid body and achieve RBL. We propose two RBL methods, namely, the observation matching (OM) algorithm and topology matching (TM) algorithm with refinement. The emerging participatory searching algorithm (PSA) is adopted in both methods to solve the nonlinear matching problems. Simulations show that, compared with the existing approach, the OM method can achieve better RBL accuracy under high DoA noise levels, while the performance of the TM algorithm with refinement is closer to the constrained Cramér–Rao bound (CCRB) under low DoA noise levels.

Due to limited depth-of-focus, classical 2D images inevitably lose details of targets out of depth-of-focus, while all-in-focus images break through the limit by fusing multi-focus images, thus being able to focus on targets in extended depth-of-view. However, conventional methods can hardly obtain dynamic all-in-focus imaging in both high spatial and temporal resolutions. To solve this problem, we design REPAID, meaning resolution-enhanced plenoptic all-in-focus imaging using deep neural networks. In REPAID, multi-focus images are first reconstructed from a single-shot plenoptic image, then upsampled using specially designed deep neural networks suitable for real scenes without ground truth to finally generate all-in-focus image in both high temporal and spatial resolutions. Experiments on both static and dynamic scenes have proved that REPAID can obtain high-quality all-in-focus imaging when using simple setups only; therefore, it is a promising tool in applications especially intended for imaging dynamic targets in large depth-of-view.

Due to the large measurement error in the practical non-cooperative scene, the passive localization algorithms based on traditional numerical calculation using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) often have no solution, i.e., the estimated result cannot meet the localization background knowledge. In this context, this paper intends to introduce interval analysis theory into joint FDOA/TDOA-based localization algorithm. The proposed algorithm uses the dichotomy algorithm to fuse the interval measurement of TDOA and FDOA for estimating the velocity and position of a moving target. The estimation results are given in the form of an interval. The estimated interval must contain the true values of the position and velocity of the radiation target, and the size of the interval reflects the confidence of the estimation. The point estimation of the position and the velocity of the target is given by the midpoint of the estimation interval. Simulation analysis shows the efficacy of the algorithm.

Optical metasurfaces, as a booming research field, have provided new methods for modulating the amplitude, phase, and polarization of light through artificial birefringent structures or structural resonances. It has been used to design planar optical components such as ultra‐thin lenses, ultra‐wideband achromatic lenses, and orbital angular momentum (OAM) generators. However, existing surveys typically examine either metasurface fundamentals or a single display modality, leaving no comprehensive roadmap that connects meta‐atom design to full‐device performance, hereafter, the term meta‐atom are denoted to be an individual sub‐wavelength building block of a metasurface. Here we present the first cross‐scale review that quantitatively bridgices phase‐dispersion engineering at the nanostructure level with system‐level figures‐of‐merit across three mainstream 3D display paradigms, computer‐generated holography, light‐field projection, and near‐eye/retinal displays. By critically benchmarking more than 150 demonstrations published between 2019 and 2025, we extract practical lookup charts that guide practitioners from material choice and meta‐atom geometry to field‐of‐view, depth acuity, efficiency, and form‐factor targets. Thanks to metasurfaces’ high integration density and functional diversity, its application in the light field display has attracted great interest. Metasurface can effectively improve the shortcomings of low spatial resolution, low diffraction efficiency, and narrow field of view common in traditional display components. In this paper, we first review the phase modulation method and structure resonance principle of metasurface. Then, we examine their application in the holographic display field and review the approaches for achieving structural‐color printing. We summarize the 3D display methods of holographic display, light field display, and near‐eye display and discuss how metasufaces enhance each modality. Finally, we distill emerging inflection points: AI assisted inverse design, dynamically tunable multifunctional platforms, and quantum or cascaded architectures into a looking forward commercialization roadmap that addresses the challenges still facing the 3D display industry.