{CurveFusion}: reconstructing thin structures from {RGBD} sequences
| Autor: | Christian Theobalt, Lingjie Liu, Nenglun Chen, Duygu Ceylan, Niloy J. Mitra, Wenping Wang |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer science business.industry 020207 software engineering 02 engineering and technology Skeleton (category theory) Object (computer science) Sensor fusion Computer Graphics and Computer-Aided Design Graphics (cs.GR) Range (mathematics) Computer Science - Graphics 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Point (geometry) Computer vision Artificial intelligence Graphics business |
| Zdroj: | ACM Transactions on Graphics |
| ISSN: | 1557-7368 0730-0301 |
| Popis: | We introduce C urve F usion , the first approach for high quality scanning of thin structures at interactive rates using a handheld RGBD camera. Thin filament-like structures are mathematically just 1D curves embedded in R 3 , and integration-based reconstruction works best when depth sequences (from the thin structure parts) are fused using the object's (unknown) curve skeleton. Thus, using the complementary but noisy color and depth channels, C urve F usion first automatically identifies point samples on potential thin structures and groups them into bundles , each being a group of a fixed number of aligned consecutive frames. Then, the algorithm extracts per-bundle skeleton curves using L 1 axes, and aligns and iteratively merges the L 1 segments from all the bundles to form the final complete curve skeleton. Thus, unlike previous methods, reconstruction happens via integration along a data-dependent fusion primitive , i.e., the extracted curve skeleton. We extensively evaluate C urve F usion on a range of challenging examples, different scanner and calibration settings, and present high fidelity thin structure reconstructions previously just not possible from raw RGBD sequences. |
| Databáze: | OpenAIRE |
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