arXiv subjects

Mingyu Han

Publications and source records attributed to Mingyu Han.

3 recordsLinked to original sources

arXiv2026

AmbientEye: A Dataset for Pupil Segmentation under Natural Ambient Infrared Illumination

Eye tracking is essential for smart glasses, as it provides insight into user attention for ambient intelligence applications. However, most existing eye-tracking systems rely on active infrared (IR) illumination, creating practical barriers to all-day outdoor use due to power consumption. In this paper, we investigate whether passive IR cameras alone, without any active IR light source, can enable reliable pupil detection in unconstrained outdoor environments, where ambient sunlight serves as the sole illumination source. To support this investigation, we introduce AmbientEye, a large-scale dataset of 2,606,225 eye images collected from 35 participants from 19 countries. It is captured outdoors under natural sunlight with two off-axis camera configurations and two sun-orientation conditions. We provide high-quality pupil annotation through SAM2 automatic segmentation, followed by refinement by human annotators. We benchmark a state-of-the-art pupil segmentation algorithm on our dataset and compare its performance with that on existing datasets under controlled IR illumination. Results reveal a substantial drop in pupil segmentation performance from 0.928 on controlled IR datasets to 0.767 on AmbientEye. This performance gap highlights the challenge of the ambient-light setting. This positions AmbientEye as a first benchmark for an unexplored and highly practical eye-tracking scenario.

cs.CV↗

arXiv2026

Geometric Generalization of Neural Operators from a Kernel Integral Perspective

Neural operators are neural network-based surrogate models for approximating partial differential equation solution operators, enabling efficient many-query computations in science and engineering when low-to-moderate accuracy is sufficient. Many applications, including engineering design, involve variable and often nonparametric geometries, for which generalization to unseen shapes remains a central practical challenge. In this work, we adopt a kernel-integral perspective motivated by classical boundary integral formulations and study operator learning on variable geometries through the approximation of geometry-dependent kernel operators, including singular kernels. This perspective clarifies a mechanism for geometric generalization for fixed linear operators and reveals a direct connection between operator learning and fast kernel summation methods. Leveraging this connection, we propose a multiscale point cloud neural operator inspired by Ewald summation, combining Fourier long-range interactions with local geometry-aware corrections. We further establish approximation guarantees for the resulting multiscale representation of linear operators defined by singular kernels. Numerical experiments demonstrate robust generalization across diverse geometries for several commonly used kernels, Laplace-type boundary integral maps, and a large-scale three-dimensional nonlinear vehicle flow example.

math.NA↗

arXiv2025

BudsID: Mobile-Ready and Expressive Finger Identification Input for Earbuds

Wireless earbuds are an appealing platform for wearable computing on-the-go. However, their small size and out-of-view location mean they support limited different inputs. We propose finger identification input on earbuds as a novel technique to resolve these problems. This technique involves associating touches by different fingers with different responses. To enable it on earbuds, we adapted prior work on smartwatches to develop a wireless earbud featuring a magnetometer that detects fields from a magnetic ring. A first study reveals participants achieve rapid, precise earbud touches with different fingers, even while mobile (time: 0.98s, errors: 5.6%). Furthermore, touching fingers can be accurately classified (96.9%). A second study shows strong performance with a more expressive technique involving multi-finger double-taps (inter-touch time: 0.39s, errors: 2.8%) while maintaining high accuracy (94.7%). We close by exploring and evaluating the design of earbud finger identification applications and demonstrating the feasibility of our system on low-resource devices.

cs.HC↗