TY - GEN
T1 - Simultaneous Shape Registration and Active Stereo Shape Reconstruction using Modified Bundle Adjustment
AU - Furukawa, R.
AU - Nagamatsu, Genki
AU - Kawasaki, Hiroshi
PY - 2019/9
Y1 - 2019/9
N2 - Simultaneous registration and shape fusion using 3D scanners have been proposed for conducting wide-area and dense 3D shape reconstruction. However, because the 3D scanners for such a system must be robust and should provide feedback in real time, only a few devices are available, thereby limiting the application of the technique. In this study, we propose a new wide-area scanning algorithm that only requires an off-the-shelf projector and a camera. In our technique, the devices are not necessarily fixed to each other and the relative positions of the devices as well as the scene shapes can be precisely estimated by bundle adjustment (BA) in case of structured light. To efficiently perform shape registration, a robust and dense shape reconstruction is required, which is currently considered to be an open problem for structured light systems. In this study, we suggest a novel network-based feature detection algorithm as well as shape fusion algorithm for the solution.
AB - Simultaneous registration and shape fusion using 3D scanners have been proposed for conducting wide-area and dense 3D shape reconstruction. However, because the 3D scanners for such a system must be robust and should provide feedback in real time, only a few devices are available, thereby limiting the application of the technique. In this study, we propose a new wide-area scanning algorithm that only requires an off-the-shelf projector and a camera. In our technique, the devices are not necessarily fixed to each other and the relative positions of the devices as well as the scene shapes can be precisely estimated by bundle adjustment (BA) in case of structured light. To efficiently perform shape registration, a robust and dense shape reconstruction is required, which is currently considered to be an open problem for structured light systems. In this study, we suggest a novel network-based feature detection algorithm as well as shape fusion algorithm for the solution.
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U2 - 10.1109/3DV.2019.00057
DO - 10.1109/3DV.2019.00057
M3 - Conference contribution
T3 - Proceedings - 2019 International Conference on 3D Vision, 3DV 2019
SP - 453
EP - 462
BT - Proceedings - 2019 International Conference on 3D Vision, 3DV 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on 3D Vision, 3DV 2019
Y2 - 15 September 2019 through 18 September 2019
ER -