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CURVEFUSION: Reconstructing Thin Structures from RGBD Sequences

Liu, L; Chen, N; Ceylan, D; Theobalt, C; Wang, W; Mitra, NJ; (2018) CURVEFUSION: Reconstructing Thin Structures from RGBD Sequences. ACM Transactions on Graphics (TOG) , 37 (6) , Article 218. 10.1145/3272127.3275097. Green open access

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Abstract

We introduce CurveFusion, 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 R3, 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, CurveFusion 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 L1 axes, and aligns and iteratively merges the L1 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 CurveFusion 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.

Type: Article
Title: CURVEFUSION: Reconstructing Thin Structures from RGBD Sequences
Location: Tokyo, JAPAN
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3272127.3275097
Publisher version: http://doi.org/10.1145/3272127.3275097
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Technology, Computer Science, Software Engineering, Computer Science, curve reconstruction, L-1 axis, data fusion, RGBD scans
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10073125
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