Diabetic foot neuropathy (DFN) is a critical factor leading to diabetic foot ulcers, which is one of the most common and severe complications of diabetes mellitus (DM) and is associated with high risks of amputation and mortality. Despite its significance, existing datasets do not directly derive from plantar data and lack continuous, long-term foot-specific information. To advance DFN research, we have collected a novel dataset comprising continuous plantar pressure data to recognize diabetic foot neuropathy. This dataset includes data from 94 DM patients with DFN and 41 DM patients without DFN. Moreover, traditional methods divide datasets by individuals, potentially leading to significant domain discrepancies in some feature spaces due to the absence of mid-domain data. In this paper, we propose an effective domain adaptation method to address this proplem. We split the dataset based on convolutional feature statistics and select appropriate sub-source domains to enhance efficiency and avoid negative transfer. We then align the distributions of each source and target domain pair in specific feature spaces to minimize the domain gap. Comprehensive results validate the effectiveness of our method on both the newly proposed dataset for DFN recognition and an existing dataset.

Recent advancements in Generalizable Gaussian Splatting have enabled robust 3D reconstruction from sparse input views by utilizing feed-forward Gaussian Splatting models, achieving superior cross-scene generalization. However, while many methods focus on geometric consistency, they often neglect the potential of text-driven guidance to enhance semantic understanding, which is crucial for accurately reconstructing fine-grained details in complex scenes. To address this limitation, we propose TextSplat-the first text-driven Generalizable Gaussian Splatting framework. Specifically, our framework employs three parallel modules to obtain complementary representations: the Diffusion Prior Depth Estimator for accurate depth information, the Semantic Aware Segmentation Network for detailed semantic information, and the Multi-View Interaction Network for refined cross-view features. Then, in the Text-Guided Semantic Fusion Module, these representations are integrated via the text-guided and attention-based feature aggregation mechanism, resulting in enhanced 3D Gaussian parameters enriched with detailed semantic cues. Experimental results on various benchmark datasets demonstrate improved performance compared to existing methods across multiple evaluation metrics, validating the effectiveness of our framework. The code will be publicly available.

A well-known challenge in applying deep-learning methods to omnidirectional images is spherical distortion. In dense regression tasks such as depth estimation, where structural details are required, using a vanilla CNN layer on the distorted 360 image results in undesired information loss. In this paper, we propose a 360 monocular depth estimation pipeline, OmniFusion, to tackle the spherical distortion issue. Our pipeline transforms a 360 image into less-distorted perspective patches (i.e. tangent images) to obtain patch-wise predictions via CNN, and then merge the patch-wise results for final output. To handle the discrepancy between patch-wise predictions which is a major issue affecting the merging quality, we propose a new framework with the following key components. First, we propose a geometry-aware feature fusion mechanism that combines 3D geometric features with 2D image features to compensate for the patch-wise discrepancy. Second, we employ the self-attention-based transformer architecture to conduct a global aggregation of patch-wise information, which further improves the consistency. Last, we introduce an iterative depth refinement mechanism, to further refine the estimated depth based on the more accurate geometric features. Experiments show that our method greatly mitigates the distortion issue, and achieves state-of-the-art performances on several 360 monocular depth estimation benchmark datasets. Our code is available at https://github.com/yuyanli0831/OmniFusion.

Omnidirectional 3D information is essential for a wide range of applications such as Virtual Reality, Autonomous Driving, Robotics, etc. In this paper, we propose a novel, model-agnostic, two-stage pipeline for omnidirectional monocular depth estimation. Our proposed framework PanoDepth takes one 360 image as input, produces one or more synthesized views in the first stage, and feeds the original image and the synthesized images into the subsequent stereo matching stage. In the second stage, we propose a differentiable Spherical Warping Layer to handle omnidirectional stereo geometry efficiently and effectively. By utilizing the explicit stereo-based geometric constraints in the stereo matching stage, PanoDepth can generate dense high-quality depth. We conducted extensive experiments and ablation studies to evaluate PanoDepth with both the full pipeline as well as the individual modules in each stage. Our results show that PanoDepth outperforms the state-of-the-art approaches by a large margin for 360 monocular depth estimation. Our code is available at https:// github.com/yuyanli0831/PanoDepth_3dv.

Ubiquitous Augmented Reality requires robust localization in complex daily environments. The combination of camera and Inertial Mersurement Unit (IMU) has shown promising results for robust localization due to the complementary characteristics of the visual and inertial modalities. However, there exists many cases where the measurements from visual and inertial modalities do not provide a single consistent motion estimate thus causing disagreement on the estimated motion. Limited literature has addressed this problem associated with sensor fusion for localization. Since the disagreement is not a result of measurement noises, existing outlier rejection techniques are not suitable to address this problem. In this paper, we propose a novel approach to handle the disagreement as motion conflict with two key components. The first one is a generalized Hidden Markov Model (HMM) that formulates the tracking and management of the primary motion and the secondary motion as a single estimation problem. The second component is an epipolar constrained Deep Neural Network that generates a per-pixel motion conflict probability map. Experimental evaluations demonstrate significant improvement to the tracking accuracy in cases of strong motion conflict compared to previous state-of-the-art algorithms for localization. Moreover, as a consequence of motion tracking on the secondary maps, our solution enables augmentation of virtual content attached to secondary motions, which brings us one step closer to Ubiquitous Augmented Reality.

Visual SLAM is one of the key technologies to align the virtual and real world together in Augmented Reality applications. RGBD dense Visual SLAM approaches have shown their advantages in robustness and accuracy in recent years. However, there are still several challenges such as the inconsistencies in RGBD measurements across multiple frames that could jeopardize the accuracy of both camera trajectory and scene reconstruction. In this paper, we propose a novel map representation called Probabilistic Surfel Map (PSM) for dense visual SLAM. The main idea is to maintain a globally consistent map with both photometric and geometric uncertainties encoded in order to address the inconsistency issue. The key of our PSM is proper modeling and updating of sensor measurement uncertainties, as well as the strategies to apply them for improving both the front-end pose estimation and the back-end optimization. Experimental results on publicly available datasets demonstrate major improvements with our approach over the state-of-the-art methods. Specifically, comparing with σ-DVO, we achieve a 40% reduction in absolute trajectory error and an 18% reduction in relative pose error in visual odometry, as well as an 8.5% reduction in absolute trajectory error in complete SLAM. Moreover, our PSM enables generation of a high quality dense point cloud with comparable accuracy as the state-of-the-art approach.

In this paper we propose a novel method called s-DVO for dense visual odometry using a probabilistic sensor noise model. In contrast to sparse visual odometry, where camera poses are estimated based on matched visual features, we apply dense visual odometry which makes full use of all pixel information from an RGB-D camera. Previously, t-distribution was used to model photometric and geometric errors in order to reduce the impacts of outliers in the optimization. However, this approach has the limitation that it only uses the error value to determine outliers without considering the physical process. Therefore, we propose to apply a probabilistic sensor noise model to weigh each pixel by propagating linearized uncertainty. Furthermore, we find that the geometric errors are well represented with the sensor noise model, while the photometric errors are not. Finally we propose a hybrid approach which combines t-distribution for photometric errors and a probabilistic sensor noise model for geometric errors. We extend the dense visual odometry and develop a visual SLAM system that incorporates keyframe generation, loop constraint detection and graph optimization. Experimental results with standard benchmark datasets show that our algorithm outperforms previous methods by about a 25% reduction in the absolute trajectory error.

A new mixer circuit is proposed by using the novel gate-controlled lateral PNP (LPNP) device. A major advantage of this four terminal device is that its collector current I/sub c/ can also be controlled by applying a voltage V/sub G/ to the additional input gate terminal. The associated large-signal and small-signal characteristics, such as current gain h/sub FE/ and transconductance g/sub M/, show very strong non-linear features as a function of the gate voltage. Therefore, this new device is particularly suitable for a mixer circuit for possible applications to portable wireless communications. The primary experimental results show that the mixing outputs for an intermediate frequency (IF) of 0.5 MHz using this new mixer circuit have conversion gain of 5 dB to 12 dB for the input radio frequency (RF) signal ranges of up to 400 MHz. The signal-to-noise ratio of its mixing IF output is about 50 dB. Another potential feature of this mixer circuit is that it can be easily integrated with other required signal processing circuits using a BiCMOS technology, and with no additional processing steps.

A new resonant-tunnel diode (RTD) large-signal DC model suitable for PSPICE simulation is presented in this paper. For better accuracy, the model equations are deliberately chosen through the combination of Gaussian and/or exponential functions, and it can be easily implemented in PSPICE using the FUNCTION statement. Most of the associated parameters required in this new model have explicit relations to the measured I-V curves, and can be easily extracted. This new DC model has been successfully applied to simulating single RTD devices, and a RTD-based three-state memory circuit. Compared to other RTD DC models, the presented model gives better accuracy and has less convergence problems. In addition, the new model can be used to simulate hysteresis effect, and can easily incorporate AC effects.>