BS3D : building-scale 3D reconstruction from RGB-D images
Mustaniemi, Janne; Kannala, Juho; Rahtu, Esa; Liu, Li; Heikkilä, Janne (2023-04-27)
Mustaniemi, J., Kannala, J., Rahtu, E., Liu, L., Heikkilä, J. (2023). BS3D: Building-Scale 3D Reconstruction from RGB-D Images. In: Gade, R., Felsberg, M., Kämäräinen, JK. (eds) Image Analysis. SCIA 2023. Lecture Notes in Computer Science, vol 13886. Springer, Cham. https://doi.org/10.1007/978-3-031-31438-4_36
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2023062057123
Tiivistelmä
Abstract
Various datasets have been proposed for simultaneous localization and mapping (SLAM) and related problems. Existing datasets often include small environments, have incomplete ground truth, or lack important sensor data, such as depth and infrared images. We propose an easy-to-use framework for acquiring building-scale 3D reconstruction using a consumer depth camera. Unlike complex and expensive acquisition setups, our system enables crowd-sourcing, which can greatly benefit data-hungry algorithms. Compared to similar systems, we utilize raw depth maps for odometry computation and loop closure refinement which results in better reconstructions. We acquire a building-scale 3D dataset (BS3D) and demonstrate its value by training an improved monocular depth estimation model. As a unique experiment, we benchmark visual-inertial odometry methods using both color and active infrared images.
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