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- W4313532585 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." @default.
- W4313532585 created "2023-01-06" @default.
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- W4313532585 date "2023-01-03" @default.
- W4313532585 modified "2023-09-26" @default.
- W4313532585 title "BS3D: Building-scale 3D Reconstruction from RGB-D Images" @default.
- W4313532585 doi "https://doi.org/10.48550/arxiv.2301.01057" @default.
- W4313532585 hasPublicationYear "2023" @default.
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