Rejoining fragmented ancient bamboo slips with physics-driven deep learning
- URL: http://arxiv.org/abs/2505.08601v2
- Date: Thu, 03 Jul 2025 02:44:45 GMT
- Title: Rejoining fragmented ancient bamboo slips with physics-driven deep learning
- Authors: Jinchi Zhu, Zhou Zhao, Hailong Lei, Xiaoguang Wang, Jialiang Lu, Jing Li, Qianqian Tang, Jiachen Shen, Gui-Song Xia, Bo Du, Yongchao Xu,
- Abstract summary: WisePanda is a physics-driven deep learning framework designed to rejoin fragmented bamboo slips.<n>Based on the physics of fracture and material deterioration, WisePanda automatically generates synthetic training data.<n>Archaeologists using WisePanda have experienced substantial efficiency improvements.
- Score: 77.2197174265539
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Advanced Deep Learning Approaches for Automated Recognition of Cuneiform Symbols [0.3749861135832073]
Five distinct deep-learning models were trained on a comprehensive dataset of cuneiform characters.<n>Two models demonstrated outstanding performance and were subsequently assessed using cuneiform symbols from the Hammurabi law acquisition.<n>Each model effectively recognized the relevant Akkadian meanings of the symbols and delivered precise English translations.
arXiv Detail & Related papers (2025-05-07T12:05:23Z) - Mambular: A Sequential Model for Tabular Deep Learning [0.7184556517162347]
This paper investigates the use of autoregressive state-space models for tabular data.<n>We compare their performance against established benchmark models.<n>Our findings indicate that interpreting features as a sequence and processing them can lead to significant performance improvement.
arXiv Detail & Related papers (2024-08-12T16:57:57Z) - Restoring Ancient Ideograph: A Multimodal Multitask Neural Network
Approach [11.263700269889654]
This paper proposes a novel Multimodal Multitask Restoring Model (MMRM) to restore ancient texts.
It combines context understanding with residual visual information from damaged ancient artefacts, enabling it to predict damaged characters and generate restored images simultaneously.
arXiv Detail & Related papers (2024-03-11T12:57:28Z) - Fantastic Breaks: A Dataset of Paired 3D Scans of Real-World Broken
Objects and Their Complete Counterparts [0.5572870549559665]
We present Fantastic Breaks, a dataset containing scanned, waterproofed, and cleaned 3D meshes for 150 broken objects.
Fantastic Breaks contains class and material labels, proxy repair parts that join to broken meshes, and manually annotated fracture boundaries.
We show experimental shape repair evaluation with Fantastic Breaks using multiple learning-based approaches.
arXiv Detail & Related papers (2023-03-24T17:03:40Z) - Batch-based Model Registration for Fast 3D Sherd Reconstruction [74.55975819488404]
3D reconstruction techniques have widely been used for digital documentation of archaeological fragments.
We aim to develop a portable, high- throughput, and accurate reconstruction system for efficient digitization of fragments excavated in archaeological sites.
We develop a new batch-based matching algorithm that pairs the front and back sides of the fragments, and a new Bilateral Boundary ICP algorithm that can register partial scans sharing very narrow overlapping regions.
arXiv Detail & Related papers (2022-11-13T13:08:59Z) - PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval
and Deformation [59.70430570779819]
We introduce a data-driven shape completion approach that focuses on completing geometric details of missing regions of 3D shapes.
Our key insight is to copy and deform patches from the partial input to complete missing regions.
We leverage repeating patterns by retrieving patches from the partial input, and learn global structural priors by using a neural network to guide the retrieval and deformation steps.
arXiv Detail & Related papers (2022-07-24T18:59:09Z) - Unsupervised Structure-Texture Separation Network for Oracle Character
Recognition [70.29024469395608]
Oracle bone script is the earliest-known Chinese writing system of the Shang dynasty and is precious to archeology and philology.
We propose a structure-texture separation network (STSN), which is an end-to-end learning framework for joint disentanglement, transformation, adaptation and recognition.
arXiv Detail & Related papers (2022-05-13T10:27:02Z) - Unsupervised Domain Adaptive Person Re-Identification via Human Learning
Imitation [67.52229938775294]
In past years, researchers propose to utilize the teacher-student framework in their methods to decrease the domain gap between different person re-identification datasets.
Inspired by recent teacher-student framework based methods, we propose to conduct further exploration to imitate the human learning process from different aspects.
arXiv Detail & Related papers (2021-11-28T01:14:29Z) - Is Deep Image Prior in Need of a Good Education? [57.3399060347311]
Deep image prior was introduced as an effective prior for image reconstruction.
Despite its impressive reconstructive properties, the approach is slow when compared to learned or traditional reconstruction techniques.
We develop a two-stage learning paradigm to address the computational challenge.
arXiv Detail & Related papers (2021-11-23T15:08:26Z) - Skeletal Feature Compensation for Imitation Learning with Embodiment
Mismatch [51.03498820458658]
SILEM is a proposed imitation learning technique that compensates for differences in skeletal features obtained from the learner and expert.
We create toy domains based on PyBullet's HalfCheetah and Ant to assess SILEM's benefits for this type of embodiment mismatch.
We also provide qualitative and quantitative results on more realistic problems -- teaching simulated humanoid agents to walk by observing human demonstrations.
arXiv Detail & Related papers (2021-04-15T22:50:48Z) - Learning compositional functions via multiplicative weight updates [97.9457834009578]
We show that multiplicative weight updates satisfy a descent lemma tailored to compositional functions.
We show that Madam can train state of the art neural network architectures without learning rate tuning.
arXiv Detail & Related papers (2020-06-25T17:05:19Z) - Restoration of Fragmentary Babylonian Texts Using Recurrent Neural
Networks [14.024892678242379]
The main source of information regarding ancient Mesopotamian history and culture are clay cuneiform tablets.
Despite being an invaluable resource, many tablets are fragmented leading to missing information.
In this work we investigate the possibility of assisting scholars and even automatically completing the breaks in ancient Akkadian texts from Achaemenid period Babylonia by modelling the language using recurrent neural networks.
arXiv Detail & Related papers (2020-03-04T06:36:50Z) - On Iterative Neural Network Pruning, Reinitialization, and the
Similarity of Masks [0.913755431537592]
We analyze differences in the connectivity structure and learning dynamics of pruned models found through a set of common iterative pruning techniques.
We show empirical evidence that weight stability can be automatically achieved through apposite pruning techniques.
arXiv Detail & Related papers (2020-01-14T21:11:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.