Peize Li

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About

Peize Li is an emerging researcher working at the intersection of computer vision, medical imaging, and indoor positioning systems. His work spans two compelling domains: bronchoscopic navigation and multimodal localization, demonstrating a versatile technical foundation in visual perception and spatial computing. Li's most recognized contribution is his 2023 benchmark dataset for feature-based visual odometry in bronchoscopy, which has garnered 12 citations in a short time. This work addresses a genuinely difficult challenge — navigating the complex, visually repetitive branching structure of the bronchial tree — by establishing standardized evaluation protocols that can accelerate progress across the medical robotics community. By providing a dedicated dataset and benchmark, Li has laid critical groundwork for researchers developing more reliable bronchoscopic guidance systems, with direct implications for lung disease diagnosis and treatment. His 2024 work on multimodal indoor localization takes a practical approach to a persistent real-world problem, leveraging crowdsourced radio maps to reduce dependence on often-outdated floor plans. Though early in its citation trajectory, this research reflects Li's broader interest in robust, infrastructure-aware positioning. Together, these contributions mark Li as a promising young researcher tackling high-impact problems in both clinical and everyday technological contexts.