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In an Internet-of-Things system supported by Internet Protocol version 6 (IPv6), the Routing Protocol for Low-Power and Lossy Networks (RPL) presents extensive applications in various network scenarios. In these novel scenarios characterized by the access of massive devices, path recovery, which reconstructs the complete path of the packet transmission, plays a vital role in network measurement, topology inference, and information security. This paper proposes a Lightweight Path recovery algorithm (LiPa) for multi-hop point-to-point communication. The core idea of LiPa is to make full use of the spatial and temporal information of the network topology to recover the unknown paths iteratively. Specifically, spatial and temporal information refer to the potential correlations between different paths within a time slot and path status during different time slots, respectively. To verify the effect of our proposal, we separately analyze the performance of leveraging temporal information, spatial information, and their composition by extensive simulations. We also compare LiPa with two state-of-the-art methods in terms of the recovery accuracy and the gain–loss ratio. The experiment results show that LiPa significantly outperforms all its counterpart algorithms in different network settings. Thus, LiPa can be considered as a promising approach for packet-level path recovery with minor loss and great adaptability.