Integrated bionic LiDAR for adaptive 4D machine vision
Ruixuan Chen, Yichen Wu, Ke Zhang, Chuxin Liu, Wencan Li, Yikun Chen, Bitao Shen, Zhaoxi CHEN, Hanke Feng, Zhangfeng Ge, Yan Zhou, Zihan Tao, Xuguang Zhang, W N Xu, Yimeng Wang, Pengfei Cai, Dong Pan, Haowen Shu, Linjie Zhou, Cheng Wang, Xingjun Wang
Abstract
The human eye achieves high resolution and energy efficiency through adaptive focusing on pertinent details. This natural capability sets a benchmark that current machine vision systems struggle to emulate due to rigid parallel-sensing mechanisms. Efforts to achieve retinal-level resolution through brute-force scaling of channels can lead to prohibitive cost, complexity, and power consumption. Here, we address this bottleneck with a retina-inspired chip-scale LiDAR architecture featuring dynamic gazing capability. By synergizing an agile external cavity laser with reconfigurable electro-optic frequency combs, our integrated photonic platform enables coherent LiDAR with parallel, reconfigurable channels, allocating sensing resources to critical regions without global oversampling. This hardware-efficient design enables real-time comb-based 4D imaging at 0.012° beyond-retinal resolution and supports 4D-plus imaging through cooperative sensing with a camera. With its beyond-retinal resolution, dynamic gazing capability, and scalable chip-level manufacturability, this technology offers a path toward lightweight, high-performance perception across aerial, terrestrial, and marine autonomous systems.