Temporal single‐photon (TSP‐) LiDAR presents a promising solution for imaging‐free target recognition over long distances with reduced size, cost, and power consumption. However, existing TSP‐LiDAR approaches are ineffective in handling open‐set scenarios where unknown targets emerge, and they suffer significant performance degradation under low signal‐to‐noise ratio (SNR) and short acquisition times (fewer photons). Here, inspired by semantic communication, we propose a semantic TSP‐LiDAR based on a self‐updating semantic knowledge base (SKB), in which the target recognition processing of TSP‐LiDAR is formulated as a semantic communication. The results, both simulation and experiment, demonstrate that our approach surpasses conventional methods, particularly under challenging conditions of low SNR and limited acquisition time. More importantly, our self‐updating SKB mechanism can dynamically update the semantic features of newly encountered targets in the SKB, enabling continuous adaptation without the need for extensive retraining of the neural network. In fact, a recognition accuracy of 89% is achieved on nine types of unknown targets in real‐world experiments, compared to 66% without the updating mechanism. These findings highlight the potential of our framework for adaptive and robust target recognition in complex and dynamic environments.
Li et al. (Sun,) studied this question.