In this study, a highly efficient 6.78-MHz power amplifier (PA) is proposed for power and data transfer in implantable medical systems. Based on a soft-switching push-pull topology, the amplifier maintains high power transfer efficiency (PTE) over a wide range of resistive load without requiring compensation networks. Moreover, the PTE degradation caused by increased distance has been significantly mitigated. A programmable bus voltage enables linear control of the PA’s output power which is preferable for closed-loop power control applications. The proposed power transfer system achieves a remarkable PTE of 60.9 % at 6.78 MHz, with an output power programmable between 20 mW and 130 mW. Additionally, 100-kbps on-off keying (OOK) modulation is demonstrated to establish the forward data link of the wireless system.

Given a single image of a 3D object, this paper proposes a novel method (named ConsistNet) that can generate multiple images of the same object, as if they are capturedfrom different viewpoints, while the 3D (multi-view) consistencies among those multiple generated images are effectively exploited. Central to our method is a lightweight multi-view consistency block that enables information exchange across multiple single-view diffusion processes based on the underlying multi-view geometry principles. ConsistNet is an extension to the standard latent diffusion model and it consists of two submodules: (a) a view aggregation module that unprojects multi-view features into global 3D volumes and infers consistency, and (b) a ray aggregation module that samples and aggregates 3D consistent features back to each view to enforce consistency. Our approach departs from previous methods in multi-view image generation, in that it can be easily dropped in pretrained LDMs without requiring explicit pixel correspondences or depth prediction. Experiments show that our method effectively learns 3D consistency over a frozen Zero123-XL backbone and can generate 16 surrounding views of the object within 11 seconds on a single A100 GPU. Our code will be made available on https://github.com/JiayuYANG/ConsistNet.

Non-rigid point cloud registration remains a significant challenge in 3D computer vision due to the complexity of structural deforms, lack of overlaps, and sensitivity to initialization. This paper introduces a framework inspired by the recent success in recurrent architecture, adapted to accommodate the unique characteristics of point clouds. More specifically, we design a recurrent update network block for progressively refining local registration results under a local rigidity assumption, starting from an initial global SE(3) alignment. Through comparison, our method consistently outperforms competing methods in standard metrics, achieving a 33% reduction in EPE on the 4DLoMatch benchmark compared to the second-best method. To the best of our knowledge, the proposed method is the first to successfully demonstrate that the recurrent update strategy can effectively address the non-rigid registration task with large displacement, significant deform, and low overlap. The source code and the model will be released at http://dummy.url/.

Real-time Stereo Matching is a cornerstone task for Extended Reality (XR) applications, such as 3D scene understanding, video pass-through, and mixed-reality games. Despite significant advancements, getting accurate depth information in real time on a low-power mobile device remains a challenge. One of the main difficulties is the lack of high-quality indoor video stereo data captured by head-mounted VR or AR glasses. To address this, we introduce a novel video stereo synthetic dataset that comprises photorealistic renderings of various indoor scenes and realistic camera motion captured by a moving VR/AR head-mounted display (HMD). Our newly proposed dataset enables one to develop a novel framework for continuous video-rate stereo matching.As another contribution, we also propose a new video-based stereo matching approach tailored for XR applications, which achieves real-time inference at an impressive 134fps on a standard desktop computer, or 30fps on a battery-powered HMD. Our key insight is that disparity and contextual information are highly correlated and redundant between consecutive stereo frames. By unrolling an iterative cost aggregation in time (i.e. in temporal dimension), we are able to distribute and reuse the aggregated features over time. This leads to a substantial reduction in computation without sacrificing accuracy. We conducted extensive evaluations and demonstrated that our method achieves superior performance compared to the current state-of-the-art, making it a strong contender for real-time stereo matching in VR/AR applications. Our dataset is released on https://github.com/za-cheng/XR-Stereo.

This paper proposes a practical photometric solution for the challenging problem of in-the-wild inverse rendering under unknown ambient lighting. Our system recovers scene geometry and reflectance using only multi-view images captured by a smartphone. The key idea is to exploit smartphone's built-in flashlight as a minimally controlled light source, and decompose image intensities into two photometric components – a static appearance corresponds to ambient flux, plus a dynamic reflection induced by the moving flashlight. Our method does not require flash/non-flash images to be captured in pairs. Building on the success of neural light fields, we use an off-the-shelf method to capture the ambient reflections, while the flashlight component enables physically accurate photometric constraints to decouple reflectance and illumination. Compared to existing inverse rendering methods, our setup is applicable to non-darkroom environments yet sidesteps the inherent difficulties of explicit solving ambient reflections. We demonstrate by extensive experiments that our method is easy to implement, casual to set up, and consistently outperforms existing in-the-wild inverse rendering techniques. Finally, our neural reconstruction can be easily exported to PBR textured triangle mesh ready for industrial renderers. Our source code and data are released to https://github.com/za-cheng/WildLight.

Point cloud registration (PCR) aims to recover the rigid transformation between two noisy, unordered point sets. This task is typically tackled by establishing point-wise correspondences, and solving the rigid transformation between the two sets. Since descriptor-based methods find correspondences by matching the feature space distance, a powerful and rotation-robust point feature extractor is critical to the success of this task. Existing methods assume soft rotation invariance/equivariance through the means of training augmentation, rotational discretization or pre-alignment of patches. In contrast, this paper proposes a new method which generates fully rotation invariant and equivariant descriptors by construction. For each keypoint patch, our network extracts not only a rotation invariant descriptor for establishing corre-spondences, but also a rotation equivariant one. The rotation equivariant descriptor allows relative transformation to be directly recovered from a single correspondence pair, unlike standard methods that require three correspondences. This design significantly reduces iteration number of RANSAC and guarantees high registration recall when the inlier ratio of estimated correspondences is low. Extensive experiments have demonstrated that the proposed method outperforms state-of-art methods in the same category even after much fewer RANSAC iterations.

Recovering the 3D geometry of a purely texture-less object with generally unknown surface reflectance (e.g. non-Lambertian) is regarded as a challenging task in multi-view reconstruction. The major obstacle revolves around establishing cross-view correspondences where photometric constancy is violated. This paper proposes a simple and practical solution to overcome this challenge based on a co-located camera-light scanner device. Unlike existing solutions, we do not explicitly solve for correspondence. Instead, we argue the problem is generally well-posed by multi-view geometrical and photometric constraints, and can be solved from a small number of input views. We formulate the reconstruction task as a joint energy minimization over the surface geometry and reflectance. Despite this energy is highly non-convex, we develop an optimization algorithm that robustly recovers globally optimal shape and reflectance even from a random initialization. Extensive experiments on both simulated and real data have validated our method, and possible future extensions are discussed.

Intrinsic image decomposition is a highly under-constrained problem that has been extensively studied by computer vision researchers. Previous methods impose additional constraints by exploiting either empirical or data-driven priors. In this paper, we revisit intrinsic image decomposition with the aid of near-infrared (NIR) imagery. We show that NIR band is considerably less sensitive to textures and can be exploited to reduce ambiguity caused by reflectance variation, promoting a simple yet powerful prior for shading smoothness. With this observation, we formulate intrinsic decomposition as an energy minimisation problem. Unlike existing methods, our energy formulation decouples reflectance and shading estimation, into a convex local shading component based on NIR-RGB image pair, and a reflectance component that encourages reflectance homogeneity both locally and globally. We further show the minimisation process can be approached by a series of multi-dimensional kernel convolutions, each within linear time complexity. To validate the proposed algorithm, a NIR-RGB dataset is captured over real-world objects, where our NIR-assisted approach demonstrates clear superiority over RGB methods.