Multi-camera systems offer rich observation capabilities for visual navigation and 3D scene reconstruction; however, the resulting feature redundancy often compromises computational efficiency. This challenge is particularly pronounced during bundle adjustment, where the non-linear optimization of both system poses and scene points incurs substantial computational overhead. To address this challenge, this paper introduces a pose-only geometric constraint for multi-camera systems and proposes a corresponding pose adjustment algorithm. Specifically, we use generalized camera model to establish a unified representation of the multi-camera system. Building upon this model, we formulate the multi-camera pose-only constraint, which implicitly represents a 3D scene point using two base observations and their associated poses, thereby achieving a pose-only representation of the projection geometry. Subsequently, we introduce a multi-camera pose adjustment algorithm that eliminates 3D points from the parameter space, thereby achieving efficient and focused pose optimization. Experimental results on both synthetic and real-world datasets demonstrate that the proposed algorithm outperforms baseline bundle adjustment methods in computational efficiency, while maintaining or even improving pose estimation accuracy.

Accurate measurement of shock wave motion parameters with high spatiotemporal resolution is essential for applications such as power field testing and damage assessment. However, significant challenges are posed by the fast, uneven propagation of shock waves and unstable testing conditions. To address these challenges, a novel framework is proposed that utilizes multiple event cameras to estimate the asymmetry of shock waves, leveraging its high-speed and high-dynamic range capabilities. Initially, a polar coordinate system is established, which encodes events to reveal shock wave propagation patterns, with adaptive region-of-interest (ROI) extraction through event offset calculations. Subsequently, shock wave front events are extracted using iterative slope analysis, exploiting the continuity of velocity changes. Finally, the geometric model of events and shock wave motion parameters is derived according to event-based optical imaging model, along with the 3D reconstruction model. Through the above process, multi-angle shock wave measurement, motion field reconstruction, and explosive equivalence inversion are achieved. The results of the speed measurement are compared with those of the pressure sensors and the empirical formula, revealing a maximum error of 5.20% and a minimum error of 0.06%. The experimental results demonstrate that our method achieves high-precision measurement of the shock wave motion field with both high spatial and temporal resolution, representing significant progress.

Despite the rapid advancements in event-based motion estimation, current geometric methods primarily focus on velocity estimation. However, absolute pose estimation, which is equally crucial for key applications such as robotic navigation and augmented reality, remains relatively underexplored. Consequently, the simultaneous recovery of absolute pose and velocity from event streams remains an open and challenging problem. To address this gap, we propose a geometric framework for absolute pose and velocity estimation by leveraging 3D lines in the scene and the events they trigger. At the core of the framework lie two key geometric constraints: the orthogonality between a 3D line and the normal vector of its corresponding event plane, and the collinearity of an event with the 2D projection of its associated line. Based on these constraints, we present both linear and polynomial solvers for absolute pose estimation. The former enables efficient computation, while the latter provides a globally optimal solution for rotation. For velocity estimation, we develop an efficient linear solver and a more accurate optimization-based solver to recover both angular and linear velocities. Notably, our methods require a minimum of three event-line correspondences to determine the 6-DoF absolute pose or velocities independently. Extensive experiments in simulation and on real-world datasets demonstrate that our methods achieve state-of-the-art performance, with significant improvements in accuracy and computational efficiency compared to existing methods. The demo code is publicly available at https://github.com/Zibin6/EventPoseVelocity.

Affine correspondences have received significant attention due to their benefits in tasks like image matching and pose estimation. Existing methods for extracting affine correspondences still have many limitations in terms of performance; thus, exploring a new paradigm is crucial. In this paper, we present a new pipeline designed for extracting accurate affine correspondences by integrating dense matching and geometric constraints. Specifically, a novel extraction framework is introduced, with the aid of dense matching and a novel keypoint scale and orientation estimator. For this purpose, we propose loss functions based on geometric constraints, which can effectively improve accuracy by supervising neural networks to learn feature geometry. The experimental show that the accuracy and robustness of our method outperform the existing ones in image matching tasks. To further demonstrate the effectiveness of the proposed method, we applied it to relative pose estimation. Affine correspondences extracted by our method lead to more accurate poses than the baselines on a range of real-world datasets. The code is available at https://github.com/stilcrad/LearningACs.

In-context learning (ICL), a type of universal model, demonstrates exceptional generalization across a wide range of tasks without retraining by leveraging task-specific guidance from context, making it particularly effective for the intricate demands of neuroimaging. However, current ICL models, limited to 2D inputs and thus exhibiting suboptimal performance, struggle to extend to 3D inputs due to the high memory demands of ICL. In this regard, we introduce Neuroverse3D, an ICL model capable of performing multiple neuroimaging tasks in 3D (e.g., segmentation, denoising, inpainting). Neuroverse3D overcomes the large memory consumption associated with 3D inputs through adaptive parallel-sequential context processing and a U-shaped fusion strategy, allowing it to handle an unlimited number of context images. Additionally, we propose an optimized loss function to balance multi-task training and enhance focus on anatomical boundaries. Our study incorporates 43,674 3D multi-modal scans from 19 neuroimaging datasets and evaluates Neuroverse3D on 14 diverse tasks using held-out test sets. The results demonstrate that Neuroverse3D significantly outperforms existing ICL models and closely matches task-specific models, enabling flexible adaptation to medical center variations without retraining. The code and model weights are publicly available at https://github.com/jiesihu/Neuroverse3D.

6D pose confidence region estimation has emerged as a critical direction, aiming to perform uncertainty quantification for assessing the reliability of estimated poses. However, current sampling-based approach suffers from critical limitations that severely impede their practical deployment: 1) the sampling speed significantly decreases as the number of samples increases. 2) the derived confidence regions are often excessively large. To address these challenges, we propose a deterministic and efficient method for estimating pose confidence regions. Our approach uses inductive conformal prediction to calibrate the deterministically regressed Gaussian keypoint distributions into 2D keypoint confidence regions. We then leverage the implicit function theorem to propagate these keypoint confidence regions directly into 6D pose confidence regions. This method avoids the inefficiency and inflated region sizes associated with sampling and ensembling. It provides compact confidence regions that cover the ground-truth poses with a user-defined confidence level. Experimental results on the LineMOD Occlusion and SPEED datasets show that our method achieves higher pose estimation accuracy with reduced computational time. For the same coverage rate, our method yields significantly smaller confidence region volumes, reducing them by up to 99.9\% for rotations and 99.8\% for translations. The code will be available soon.

Pose tracking of uncooperative spacecraft is an essential technology for space exploration and on-orbit servicing, which remains an open problem. Event cameras possess numerous advantages, such as high dynamic range, high temporal resolution, and low power consumption. These attributes hold the promise of overcoming challenges encountered by conventional cameras, including motion blur and extreme illumination, among others. To address the standard on-orbit observation missions, we propose a line-based pose tracking method for uncooperative spacecraft utilizing a stereo event camera. To begin with, we estimate the wireframe model of uncooperative spacecraft, leveraging the spatiotemporal consistency of stereo event streams for line-based reconstruction. Then, we develop an effective strategy to establish correspondences between events and projected lines of uncooperative spacecraft. Using these correspondences, we formulate the pose tracking as a continuous optimization process over six-degree-of-freedom (6-DOF) motion parameters, achieved by minimizing event-line distances. Moreover, we construct a stereo event-based uncooperative spacecraft motion dataset, encompassing both simulated and real events. The proposed method is quantitatively evaluated through experiments conducted on our self-collected dataset, demonstrating an improvement in terms of effectiveness and accuracy over competing methods. The code will be open-sourced athttps://github.com/Zibin6/SE6PT.

Object pose tracking is one of the pivotal technologies in multimedia, attracting ever-growing attention in recent years. Existing methods employing traditional cameras encounter numerous challenges such as motion blur, sensor noise, partial occlusion, and changing lighting conditions. The emerging bio-inspired sensors, particularly event cameras, possess advantages such as high dynamic range and low latency, which hold the potential to address the aforementioned challenges. In this work, we present an optical flow-guided 6DoF object pose tracking method with an event camera. A 2D-3D hybrid feature extraction strategy is firstly utilized to detect corners and edges from events and object models, which characterizes object motion precisely. Then, we search for the optical flow of corners by maximizing the event-associated probability within a spatio-temporal window, and establish the correlation between corners and edges guided by optical flow. Furthermore, by minimizing the distances between corners and edges, the 6DoF object pose is iteratively optimized to achieve continuous pose tracking. Experimental results of both simulated and real events demonstrate that our methods outperform event-based state-of-the-art methods in terms of both accuracy and robustness.

Mobile devices equipped with a multi-camera system and an inertial measurement unit (IMU) are widely used nowadays, such as self-driving cars. The task of relative pose estimation using visual and inertial information has important applications in various fields. To improve the accuracy of relative pose estimation of multi-camera systems, we propose a globally optimal solver using affine correspondences to estimate the generalized relative pose with a known vertical direction. First, a cost function about the relative rotation angle is established after decoupling the rotation matrix and translation vector, which minimizes the algebraic error of geometric constraints from affine correspondences. Then, the global optimization problem is converted into two polynomials with two unknowns based on the characteristic equation and its first derivative is zero. Finally, the relative rotation angle can be solved using the polynomial eigenvalue solver, and the translation vector can be obtained from the eigenvector. Besides, a new linear solution is proposed when the relative rotation is small. The proposed solver is evaluated on synthetic data and real-world datasets. The experiment results demonstrate that our method outperforms comparable state-of-the-art methods in accuracy.

Pose estimation and tracking of objects is a fundamental application in 3D vision. Event cameras possess remarkable attributes such as high dynamic range, low latency, and resilience against motion blur, which enables them to address challenging high dynamic range scenes or high-speed motion. These features make event cameras an ideal complement over standard cameras for object pose estimation. In this work, we propose a line-based robust pose estimation and tracking method for planar or non-planar objects using an event camera. Firstly, we extract object lines directly from events, then provide an initial pose using a globally-optimal Branch-and-Bound approach, where 2D-3D line correspondences are not known in advance. Subsequently, we utilize event-line matching to establish correspondences between 2D events and 3D models. Furthermore, object poses are refined and continuously tracked by minimizing event-line distances. Events are assigned different weights based on these distances, employing robust estimation algorithms. To evaluate the precision of the proposed methods in object pose estimation and tracking, we have devised and established an event-based moving object dataset. Compared against state-of-the-art methods, the robustness and accuracy of our methods have been validated both on synthetic experiments and the proposed dataset. The source code is available at https://github.com/Zibin6/LOPET.

Time-domain reflectometry (TDR) that employs electro-optical sampling affords excellent resolution at the femtosecond level and exhibits a comprehensible impulse waveform, thereby allowing quick defect identification in a single trace. However, it remains challenging to identify a defect in a trace of multiple branches; the TDR waveform is complex. Generally, the TDR waveform of a defective unit features defect-dependent reflection (DDR) and defect-independent reflection (DIR). DDR is contributed by a branch with the defect; DIR is contributed by the remaining good branches. The DDR (not the DIR) is required to analyze the defect; however, the DIR tends to overwhelm the waveform, rendering interpretation difficult. In this work, we use an open-short normalization (OSN) method to eliminate the DIR. The resulting DDR immediately identifies the defect location and type. The OSN method was verified using both simulation and measurements.

In this paper, we propose two perspective-n-line (PnL)-like methods with the presence of line detection process. Compared with the traditional methods, the proposed methods use the new error models derived from the edge points and their corresponding noisy observations rather than relying on the assumption that the noises for the two endpoints are statistically independent. Meanwhile, we improve the performance of the RAPiD-like method—another type of visual tracking approach without extracting image lines by fitting the interpolated location of the corresponding edge pixel in the local region. In addition, we compare the proposed PnL-like methods with the RAPiD-like methods and find that both the types of visual tracking methods for rigid objects are fundamentally equivalent and all of them are maximum-likelihood approaches to estimate the pose parameters, given the error model for the noisy edge points. Special consideration is put into deriving a unifying probabilistic framework to express these two types of methods. Moreover, comparisons under different performance criteria, including computational efficiency, accuracy, and robustness, are also conducted.

Precision medicine requires scalable bioinstrument for a personalized DNA sequencing, which can be label-free, cost-efficient, and high-throughput. This paper mainly presents three kinds of CMOS-based label-free sensors, including: i) a high-sensitivity ion-sensitive field-effect transistor (ISFET) sensor with pH-to-time-to-voltage conversion (pH-TVC); ii) a dual-mode sensor with image and chemical modes for high accuracy; and iii) a THz metamaterial sensor with electrical resonance detection. The developed CMOS multi-modal sensor platform can show a scaled solution for future personalized DNA sequencing.

In this study, the authors have proposed a new solution for the problem of pose estimation from a set of matched 3D model and 2D image lines. Traditional line‐based pose estimation methods utilising the finite information of the observations are based on the assumption that the noises for the two endpoints of the image line segment are statistically independent. However, in this study, the authors prove that these two noises are negatively correlative when the image line segment is fitted by the least‐squares technique from the noisy edge points. Moreover, the authors derive the noise model describing the probabilistic relationship between the 3D model line and their finite image observations. Based on the proposed noise model, the maximum‐likelihood approach is exploited to estimate the pose parameters. The authors have carried out synthetic experiments to compare the proposed method to other pose optimisation methods in the literature. The experimental results show that the proposed methods yield a clear higher precision than the traditional methods. The authors also use real image sequences to demonstrate the performance of the proposed method.

Collaborative Filtering (CF) algorithm is the common solution to Recommender System (RS). With the development of network and storage technology, the amount of users and items in RS system is exclusively growing. How to increase the scalability and recommendation accuracy of CF are the main concerns in the related research. In this paper, an efficient implementation for user-based CF algorithm on MapReduce is presented. We exploit Bag of Word (BoW) method and design a hierarchical inverted index to further increase the scalability of our method. Meanwhile, a soft-assignment mechanism for the hierarchical inverted index is proposed to make up the recommendation accuracy decrease caused by the index. The Mapreduce implementations of our methods are detailed discussed and analyzed on both simulated data and real data, demonstrating that our implementation has the ability to scale to huge numbers of users and items, eanwhile ensures recommendation accuracy.

3D physical design needs accurate device model of through-silicon vias (TSVs). In this paper, physics-based electrical-thermal model is introduced for both signal and dummy thermal TSVs with the consideration of nonlinear electrical-thermal dependence. Taking thermal-reliable 3D clock-tree synthesis as a case-study to verify the effectiveness of the proposed TSV model, one nonlinear programming-based clock-skew reduction problem is formulated to allocate thermal TSVs for clock-skew reduction under non-uniform temperature distribution. With a number of 3D clock-tree benchmarks, experiments show that under the nonlinear electrical-thermal TSV model, insertion of thermal TSVs can effectively reduce temperature-gradient introduced clock-skew by 58.4% on average, and has 11.6% higher clock-skew reduction than the result under linear electrical-thermal model.

This paper studies the illumination normalization of images which are captured under complex lighting condition and analyses the problems of halo and gradient reversal exist in traditional techniques theoretically. Based on the weighted least square filter (WLS), this paper proposes a new illumination normalization algorithm. The proposed algorithm gets the accurate estimation of the illumination of the image by using the weighted least square filter and then eliminates the effects brought by light changing, it also enhances the contrast adaptively by using the modified S function. Experiment results show that the proposed algorithm can eliminates the degradation caused by light changing drastically and improves the post processing algorithms' performances effectively.

A robust physical design of 3-D IC requires investigation on through-silicon via (TSV). The large temperatures and stress gradients can severely affect TSV delay with large variation. The traditional physical model treats TSV as a resistor with linear electrical-thermal dependence, which ignores the fundamental device physics. In this paper, a physics-based electrical-thermal–mechanical delay model is developed for signal TSVs in 3-D IC. With consideration of liner material and also stress, a nonlinear model is established between electrical delay with temperature and stress. Moreover, sensitivity analysis is performed to relate the reduction of temperature and stress gradients with respect to dummy TSVs insertion. Taking the design of 3-D clock tree as a case study, we have formulated a nonlinear optimization problem for clock-skew reduction. By allocating dummy TSVs to reduce the temperature and stress gradients, the clock skew introduced by signal TSVs and drivers can be minimized. A number of 3-D clock-tree benchmarks are utilized in experiments. We have observed that with the use of dummy TSV insertion, clock skew can be reduced by 61.3% on average when the accurate nonlinear electrical-thermal–mechanical delay model is applied.

Hybrid integration of CMOS and non-volatile memory (NVM) devices has become the technology foundation for emerging non-volatile memory based computing. The primary challenge to validate a hybrid system with both CMOS and non-volatile devices is to develop a SPICE-like simulator that can simulate the dynamic behavior of hybrid system accurately and efficiently. Since spin-transfer-toque magnetic-tunneling-junction (STT-MTJ) device is one of the most promising candidates of next generation NVM devices, it is under great interest in including this new device in the standard CMOS design flow. The previous approaches require complex equivalent circuits to represent the STT-MTJ device, and ignore dynamic effect without consideration of internal states. This paper proposes a new modified nodal analysis for STT-MTJ device with identified internal state variables. As demonstrated by a number of experiment examples on hybrid systems with both CMOS and STT-MTJ devices, our newly developed SPICE-like simulator can deal with the dynamic behavior of STT-MTJ device under arbitrary driving condition and reduce the CPU time by more than 20 times for memory circuits when compared to the previous equivalent circuit approaches.