Category: Publications

Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2025 (Highlight)

Abstract: Simultaneously acquisition of the surface normal and reflectance parameters is a crucial but challenging technique in the field of computer vision and graphics. It requires capturing multiple high dynamic range (HDR) images in existing methods using frame-based cameras. In this paper, we propose EventPSR, the first work to recover surface normal and reflectance parameters (e.g., metallic and roughness) simultaneously using an event camera. Compared with the existing methods based on photometric stereo or neural radiance fields, EventPSR is a robust and efficient approach that works consistently with different materials. Thanks to the extremely high temporal resolution and high dynamic range coverage of event cameras, EventPSR can recover accurate surface normal and reflectance of objects with various materials in 10 seconds. Extensive experiments on both synthetic data and real objects show that compared with existing methods using more than 100 HDR images, EventPSR recovers comparable surface normal and reflectance parameters with only about 30% of the data rate.

Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2025 (Highlight)

Abstract: Hyperspectral imaging plays a critical role in numerous scientific and industrial fields. Conventional hyperspectral imaging systems often struggle with the trade-off between capture speed, spectral resolution, and bandwidth, particularly in dynamic environments. In this work, we present a novel event-based active hyperspectral imaging system designed for real-time capture with low bandwidth in dynamic scenes. By combining an event camera with a dynamic illumination strategy, our system achieves unprecedented temporal resolution while maintaining high spectral fidelity, all at a fraction of the bandwidth requirements of traditional systems. Unlike basis-based methods that sacrifice spectral resolution for efficiency, our approach enables continuous spectral sampling through an innovative “sweeping rainbow” illumination pattern synchronized with a rotating mirror array. The key insight is leveraging the sparse, asynchronous nature of event cameras to encode spectral variations as temporal contrasts, effectively transforming the spectral reconstruction problem into a series of geometric constraints. Extensive evaluations of both synthetic and real data demonstrate that our system outperforms state-of-the-art methods in temporal resolution while maintaining competitive spectral reconstruction quality.

Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up)

Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume: 45, Issue: 8, August 2023)