Rejoining fragmented ancient bamboo slips with physics-driven deep learning
This work addresses the challenging and time-consuming task of reconstructing fragmented bamboo slips for archaeologists, offering a substantial efficiency improvement in artifact restoration.
The researchers tackled the problem of rejoining fragmented ancient bamboo slips by developing WisePanda, a physics-driven deep learning framework that automatically generates synthetic training data based on fracture physics, resulting in an increase in Top-50 matching accuracy from 36% to 52% and making the rejoining process approximately 20 times faster for archaeologists.
Bamboo slips are a crucial medium for recording ancient civilizations in East Asia, and offers invaluable archaeological insights for reconstructing the Silk Road, studying material culture exchanges, and global history. However, many excavated bamboo slips have been fragmented into thousands of irregular pieces, making their rejoining a vital yet challenging step for understanding their content. Here we introduce WisePanda, a physics-driven deep learning framework designed to rejoin fragmented bamboo slips. Based on the physics of fracture and material deterioration, WisePanda automatically generates synthetic training data that captures the physical properties of bamboo fragmentations. This approach enables the training of a matching network without requiring manually paired samples, providing ranked suggestions to facilitate the rejoining process. Compared to the leading curve matching method, WisePanda increases Top-50 matching accuracy from 36% to 52% among more than one thousand candidate fragments. Archaeologists using WisePanda have experienced substantial efficiency improvements (approximately 20 times faster) when rejoining fragmented bamboo slips. This research demonstrates that incorporating physical principles into deep learning models can significantly enhance their performance, transforming how archaeologists restore and study fragmented artifacts. WisePanda provides a new paradigm for addressing data scarcity in ancient artifact restoration through physics-driven machine learning.