In wireless sensor networks, sensor nodes are usually self-organized or self sorted, delivering data to a central sink in a multi-hop manner. Reconstructing the per-packet routing path enables fine-grained diagnostic analysis and performance optimizations of the network. The performances of existing path reconstruction approaches like MNT, however, goes down rapidly in large scale networks with loss links or failed links. We present Pathfinder, a vigorous path reconstruction method against packet losses as well as routing dynamics. At the node side, Pathfinder exploits temporal correlation between a set of packet paths and efficiently compresses the path information using path difference. At the sink side, Pathfinder infers packet paths from the compressed information and employs intelligent path speculation to reconstruct the packet paths with high reconstruction ratio. We propose a novel analytical model to analyze the performance of Pathfinder. We further calculate Pathfinder compared with two most related approaches using traces from a large scale deployment and extensive simulations by means of graph. Results show that Pathfinder outperforms existing approaches, achieving both high reconstruction ratio and low transmission cost compared to MNT.