arXiv subjects

Yunzhou Zhu

Publications and source records attributed to Yunzhou Zhu.

3 recordsLinked to original sources

arXiv2026

Life 2.0: A Scalable Distributed Space-Telescope Array for Biosignature Spectroscopy

Answering the question "Are we alone?" requires atmospheric spectroscopy of nearby terrestrial planets. For an Earth--Sun analog, even the strongest transmission signals are expected to be of order 1 part per million (ppm). Unlike short-period planets, Earth 2.0 planets transit only about once per year, so single-transit sensitivity, rather than stacking repeated observations, is the fundamental design driver. Life 2.0 is a scalable space-mission concept linking Earth 2.0 candidates discovered by PLATO and the Earth 2.0 (ET) mission with atmospheric characterization and biosignature assessment. The baseline architecture comprises 900 one-meter space telescopes, each equipped with a high-throughput Waveguide Integrated Miniature Spectrograph and an ultra-low-read-noise CMOS detector. After independent calibration, spectra acquired simultaneously during a transit are combined, providing the photon-collecting capability of an approximately 30-m aperture at the selected spectral resolution while retaining a modular architecture. The baseline 0.2--1.05 $μ$m range covers O$_3$, O$_2$, H$_2$O, Rayleigh scattering, and other diagnostics, with extension into the infrared as detector technologies mature. Prototype Waveguide Spectral Lens devices have demonstrated 40--66\% throughput at resolving powers from $R \sim 200$ to $R \sim 20{,}000$. Lightweight silicon-carbide mirrors and sub-electron-noise CMOS detectors support replicated production. Life 2.0 must address detector systematics, instrument stability, and stellar variability; rather than assuming these limitations disappear, it builds on calibration, detector-characterization, and data-analysis techniques advanced during the JWST era. The concept offers a scalable alternative to a monolithic 30-m-class space telescope and a staged pathway toward biosignature spectroscopy of nearby Earth-like planets.

astro-ph.IM↗

arXiv2025

ThermoHands: A Benchmark for 3D Hand Pose Estimation from Egocentric Thermal Images

Designing egocentric 3D hand pose estimation systems that can perform reliably in complex, real-world scenarios is crucial for downstream applications. Previous approaches using RGB or NIR imagery struggle in challenging conditions: RGB methods are susceptible to lighting variations and obstructions like handwear, while NIR techniques can be disrupted by sunlight or interference from other NIR-equipped devices. To address these limitations, we present ThermoHands, the first benchmark focused on thermal image-based egocentric 3D hand pose estimation, demonstrating the potential of thermal imaging to achieve robust performance under these conditions. The benchmark includes a multi-view and multi-spectral dataset collected from 28 subjects performing hand-object and hand-virtual interactions under diverse scenarios, accurately annotated with 3D hand poses through an automated process. We introduce a new baseline method, TherFormer, utilizing dual transformer modules for effective egocentric 3D hand pose estimation in thermal imagery. Our experimental results highlight TherFormer's leading performance and affirm thermal imaging's effectiveness in enabling robust 3D hand pose estimation in adverse conditions.

cs.CV↗

arXiv2024

RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar

3D occupancy-based perception pipeline has significantly advanced autonomous driving by capturing detailed scene descriptions and demonstrating strong generalizability across various object categories and shapes. Current methods predominantly rely on LiDAR or camera inputs for 3D occupancy prediction. These methods are susceptible to adverse weather conditions, limiting the all-weather deployment of self-driving cars. To improve perception robustness, we leverage the recent advances in automotive radars and introduce a novel approach that utilizes 4D imaging radar sensors for 3D occupancy prediction. Our method, RadarOcc, circumvents the limitations of sparse radar point clouds by directly processing the 4D radar tensor, thus preserving essential scene details. RadarOcc innovatively addresses the challenges associated with the voluminous and noisy 4D radar data by employing Doppler bins descriptors, sidelobe-aware spatial sparsification, and range-wise self-attention mechanisms. To minimize the interpolation errors associated with direct coordinate transformations, we also devise a spherical-based feature encoding followed by spherical-to-Cartesian feature aggregation. We benchmark various baseline methods based on distinct modalities on the public K-Radar dataset. The results demonstrate RadarOcc's state-of-the-art performance in radar-based 3D occupancy prediction and promising results even when compared with LiDAR- or camera-based methods. Additionally, we present qualitative evidence of the superior performance of 4D radar in adverse weather conditions and explore the impact of key pipeline components through ablation studies.

cs.CV↗