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A grating projection system consists of a projector and a camera, where the projector functions as the inverse imager of the camera. Achieving high-precision calibration remains a major challenge. This study proposes an improved high-precision projector calibration technique. First, the grating fringe features of the calibration plate are enhanced using the Gaussian bicubic interpolation algorithm, and the projector pixel coordinates are calculated using the phase mapping formula, forming point pairs with the camera pixel coordinates. Then, a linear system of equations is constructed using the orthogonal constrained homography transformation equation. The optimized homography matrix and precise projector pixel coordinates are iteratively obtained to complete the calibration. This method eliminates the need for bundle adjustment to find optimal parameters, improves calibration accuracy and robustness, and reduces time costs. Experiments show that the calibration accuracy is approximately 0.02 mm, an 89% improvement over the traditional direct mapping method. This technique is suitable for fast 3D reconstruction and significantly improves the calibration accuracy of projectors.

An acoustic matching layer can improve energy transmission between the projecting surface of an underwater acoustic projector and hence increase the frequency bandwidth. However, the conventional acoustic matching layer is mostly designed to match the fundamental resonance of the projector. This work investigates the effect of the acoustic matching layer on bandwidth improvement of dual-mode underwater acoustic projectors driven by Pb(Zn1/3Nb2/3)O3-5.5%PbTiO3 (PZN-5.5%PT) ferroelectric single crystals. The results show that when the melamine formaldehyde quarter-wavelength acoustic matching layer is designed to match the higher-frequency transverse resonance orthogonal beam (TROB) resonance of the dual-mode projector, an ultra-broad -3 dB frequency bandwidth of 76% (82 kHz-183 kHz), and a high transmission voltage response of 143.5 dB can be achieved. This work also systematically investigates and elaborates the influence of matching frequency on the performance of multi-mode projectors. In addition, due to the low sound velocities in the relaxor-PT ferroelectric single crystals, the lateral dimensions of the fabricated projector element can be kept at or less than the half-wavelength of sound of the design frequency in water, making possible fabrication of compact and ultra-broadband arrays.

Accurate rigid motion estimation plays a crucial role in numerous X-ray imaging tasks, including 2D/3D registration, motion-compensated reconstruction, and geometric calibration. Despite its importance, gradient-based optimization for these tasks has been constrained by the absence of efficient and generalizable projectors that are differentiable with respect to motion. In this work, we introduce a framework for differentiable forward- and back-projectors that enables scalable, accurate, and memory-efficient gradient computation. Unlike prior approaches that depend on auto-differentiation or are limited to specific projector algorithms, our method derives a general analytical gradient formulation for both forward and backprojection in the continuous domain. The key insight is that the motion gradients of these operations can be expressed directly in terms of the original projection operators themselves, yielding a unified gradient computation scheme applicable across diverse projector types. Building on this analytical foundation, we implement a discretized version equipped with an acceleration strategy that effectively balances computational efficiency and memory consumption. Experimental evaluations demonstrate the capability of the proposed approach: in 2D/3D registration, our method achieves approximately 8× speedup over an existing differentiable forward projector with comparable accuracy, and in motion-compensated analytical reconstruction, it enhances image sharpness and structural fidelity on physical phantom data while offering substantial efficiency gains over existing gradient-based method.

PurposeIterative model-based image reconstruction algorithms in cone beam computed tomography (CBCT) require repetitive forward and backward projection operations. We compare the quality of the branchless distance-driven (BDD) projector in iterative CBCT reconstruction with ray- and voxel-based methods in both regular and low-dose examinations, and introduce a hybrid approach that aims at faster computation by using the BDD as the backprojector only. We also demonstrate the potential of the BDD in FDK reconstructions.ApproachTwo measured and one simulated datasets are used. Contrast-to-noise ratio (CNR) and modulation transfer function values are computed for one measured dataset. The structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) are computed with the simulated data.ResultsBased on our results, BDD has reduced noise and better CNR compared to the other methods, with the quality dependent on the scanner geometry. The CNR is improved by 21% with the BDD and 4% with the hybrid method. BDD improves SSIM values by approximately 3.3% in the lowest dose case and 1% in the highest dose case, while for PSNR the values are 5% - 10% better. For the hybrid method, the SSIM improvements range from 0.8% - 2.2%, and the PSNR from 3.7% - 6.6 %. The hybrid method with the BDD as a backprojector can be computationally twice faster with similar image quality.ConclusionsThe hybrid projector is a good choice as a compromise between image quality and computation time. Furthermore, BDD and the hybrid projector are better choices in low-dose CBCT reconstructions.

Projection mapping (PM) optically overlays computer-generated imagery onto real-world objects, enabling users to experience augmented reality without wearing any display devices. However, surface textures often cause color distortion, leading the displayed colors to deviate from the desired colors. To address this issue, we propose a projector radiometric compensation method that minimizes the color difference between a target image and the projected result using a 2D spectroradiometer (2DSR). In the proposed method, we model the color transformation between the projector and the 2DSR in a differentiable manner. Based on this formulation, we propose two optimization strategies for projector radiometric compensation: (i) minimizing the spectral error between the target appearance and the projected result, and (ii) minimizing the color difference measured in a differentiable color space designed to reflect human visual perception. Experiments with a physical prototype demonstrate that our method achieves more accurate projector radiometric compensation and better alignment with human color perception than conventional methods using an RGB camera.

Projection mapping (PM) enables augmented reality (AR) experiences without requiring users to wear head-mounted displays and supports multi-user interaction. It is regarded as a promising technology for a variety of applications in which users interact with content superimposed onto augmented objects in tabletop workspaces, including remote collaboration, healthcare, industrial design, urban planning, artwork creation, and office work. However, conventional PM systems often suffer from projection shadows when users occlude the light path. Prior approaches employing multiple distributed projectors can compensate for occlusion, but suffer from latency due to computational processing, degrading the user experience. In this research, we introduce a synthetic-aperture PM system that uses a significantly larger number of projectors, arranged densely in the environment, to achieve delay-free, shadowless projection for tabletop workspaces without requiring computational compensation. To address spatial resolution degradation caused by subpixel misalignment among overlaid projections, we develop and validate an offline blur compensation method whose computation time remains independent of the number of projectors. Furthermore, we demonstrate that our shadowless PM plays a critical role in achieving a fundamental goal of PM: altering material properties without evoking projection-like impression. Specifically, we define this perceptual impression as "sense of projection (SoP)" and establish a PM design framework to minimize the SoP based on user studies.

Augmented reality (AR) assembly guidance systems can help users quickly master the assembly process of unfamiliar objects. However, it is very difficult for practical use without a thorough understanding of users' intentions. Furthermore, existing approaches still struggle to handle complex hand-part interactions and nonlinear assembly steps, and achieving intuitive and real-time guidance remains challenging. To address these issues, we propose an intention-driven camera-projector AR assembly guidance system (IntentionAR) that integrates online user intention inference with a finite-state assembly machine. The intention module recognizes interaction actions and infers the target object, enabling feedback before assembly begins, while the finite state machine (FSM) manages step progression. Using a camera-projector device, the system highlights candidate parts and, based on inferred intention, flags correct/incorrect selections to provide real-time guidance. As the assembly progresses, the models used for spatial registration and visualization switch dynamically to accommodate the nonlinear workflow. Experiments and user studies show that the system delivers a more robust AR interaction experience and improves assembly efficiency. A free copy of this paper and all supplemental materials, as well as project assets and source code, will be available at the project website.

To develop and assess the feasibility of integrating model-based CBCT reconstruction with rigid motion estimation, using only a projection-domain data fidelity objective coupled with a differentiable forward projector. We propose a joint image reconstruction and motion estimation framework that integrates motion compensation directly into a model-based iterative reconstruction (MBIR) scheme. Rigid motion with six degrees of freedom (DoF) is modeled as geometric perturbations that alter the projection geometry and thereby affect the estimated projections. To enable gradient-based optimization, a differentiable forward projector with an analytical gradient formulation is employed, allowing efficient backpropagation of the loss between the estimated and measured projections. Within this differentiable framework, the motion parameters and attenuation volume are optimized simultaneously. To evaluate the proposed method, we conducted both simulation and physical phantom studies. In the simulation study, we assessed performance under jerk motion with progressively increasing motion amplitudes, ranging from 1 mm to 10 mm in translation and from 0.02 rad to 0.2 rad in rotation. Reconstruction accuracy was quantified using structural similarity index (SSIM) relative to motion-free ground truth. In the physical phantom study, a jerk motion of 5 mm translation and 0.1 rad rotation was applied during scanning. Motion-free, motion-corrupted, and motion-compensated reconstructions were displayed side-by-side for qualitative comparison, enabling visual assessment of motion artifact reduction. The experimental results demonstrate that the proposed motion compensation method substantially enhances image quality across a range of motion amplitudes. Qualitative evaluation reveal that even under significant translational and rotational motion, the method effectively recovers anatomical structures with minimal residual errors. In simulation studies, SSIM values improved by 18%, 37%, and 48% for motion amplitudes (3 mm, 0.06 rad), (6 mm, 0.12 rad), and (9 mm, 0.18 rad), respectively. Reconstructions from physical bench data further confirm efficacy, showing restored sharpness and anatomical continuity across coronal and sagittal planes. The discrepancy between the estimated and acquired projections serves as a sufficient objective for jointly estimating both the image volume and motion parameters, potentially avoiding the need for image-based sharpness criteria.

Projector photometric compensation corrects color distortions introduced by surface texture, reflection, and ambient lighting. Existing deep learning-based methods usually require professional scene-specific data collection and lack consideration for perceptual quality. To address this limitation, we present a diffusion-based photometric compensation method that reconstructs compensation images under photometric and content-aware guidance. Specifically, we fi rst mo del th e ph otometric distortions introduced during projection as environment-dependent additive noise, thereby reformulating the photometric compensation problem as a denoising task with physical constraints. Next, we introduce a diffusion model, which generates compensation images by following an additive trajectory to iteratively remove the noise. Finally, to accurately estimate the noise at each timestep, by analyzing the factors that contribute to distortions in the physical process of projection and capturing, we design a noise estimation network that incorporates features of both photometry-aware and content conditions. Experiments show that our method achieves superior visual performance in unknown scenarios, thereby exhibiting significant practical advantages over prior art. Our source code is available at https://github.com/cyxwang/DiffPC.

Structured light systems based on a camera-projector pair (CPP) are widely used for indoor 3D reconstruction, particularly in textureless environments where passive methods often fail. However, existing approaches typically rely on pre-calibrated intrinsics or multi-view self-calibration with known reference objects, which limits their applicability in practical indoor scenarios. Recovering CPP intrinsics from only two views without any known objects remains a challenging problem. In this paper, we present a simple yet reliable calibration framework that enables direct 3D reconstruction with an unknown CPP. By exploiting a commonly available indoor structure-an unknown cuboid corner (C2) (e.g., a room corner), we show that sufficient geometric constraints can be obtained from only two views. With only the camera principal point known, the initially coupled multi-parameter estimation is reduced to a univariable optimization problem, resulting in stable and accurate intrinsic recovery. Extensive experiments demonstrate that our method consistently outperforms both traditional and learning-based alternatives in terms of robustness and reconstruction accuracy. Moreover, the framework can be naturally extended to passive settings without active illumination, showing promising potential for sparse-view structure-from-motion applications.

Pattern recognition and image classification are essential tasks in machine vision. Autonomous vehicles, for example, require being able to collect the complex information contained in a changing environment and classify it in real time. Here, we experimentally demonstrate image classification at multi-kHz frame rates, combining the technique of single pixel imaging (SPI) with a low complexity machine learning model. The use of a microLED-on-CMOS digital light projector for SPI enables ultrafast pattern generation for sub-ms image encoding. We investigate the classification accuracy of our experimental system against the benchmarking MNIST dataset. We compare the classification performance of two low complexity machine learning models, an extreme learning machine (ELM) and a backpropagation-trained deep neural network, ensuring their inference-time overhead remains comparable to image-generation time. By exploring the performance of our SPI-based ELM as a binary classifier, we demonstrate its potential for efficient anomaly detection in ultrafast imaging scenarios. Crucially, our single pixel image classification approach is based on a spatiotemporal transformation of the information, entirely bypassing the need for image reconstruction.