홍제형 You Seong Jong ARSHAD MUHAMMAD ZEESHAN Young Min Kim Jinwook Kim 2024-01-12T03:44:43Z 2024-01-12T03:44:43Z 2021-12-14 2021-10 - https://pubs.kist.re.kr/handle/201004/77332 Re-assembling multiple pots accurately from numerous 3D scanned fragments remains a challenging task to this date. Previous methods extract all potential matching pairs of pot sherds and considers them simultaneously to search for an optimal global pot configuration. In this work, we empirically show such global approach greatly suffers from false positive matches between sherds inflicted by indistinctive sharp fracture surfaces in pot fragments. To mitigate this problem, we take inspirations from the field of structurefrom-motion (SfM), where many pipelines have matured in reconstructing a 3D scene from multiple images. Motivated by the success of the incremental approach in robust SfM, we present an efficient reassembly method for axially symmetric pots based on iterative registration of one sherd at a time. Our method goes beyond replicating incremental SfM and addresses indistinguishable false matches by embracing beam search to explore multitudes of registration possibilities. Additionally, we utilize multiple roots in each step to allow simultaneous reassembly of multiple pots. The proposed approach shows above 80% reassembly accuracy on a dataset of real 80 fragments mixed from 5 pots, pushing the state-of-the-art and paving the way towards the goal of large-scale pot reassembly. Our code and preprocessed data is available at https://github.com/SeongJong-Yoo/structure-from-sherds. English IEEE reassembly axially symmetric pots registration 3d reconsruction Structure-From-Sherds: Incremental 3D Reassembly of Axially Symmetric Pots From Unordered and Mixed Fragment Collections Conference 10.1109/ICCV48922.2021.00539 1 18th IEEE/CVF International Conference on Computer Vision (ICCV), pp.5423 - 5431 18th IEEE/CVF International Conference on Computer Vision (ICCV) 5423 5431 US Online 2021-10-11 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) 000797698905064