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- W4300794540 abstract "We propose an active 3D mapping method for depth sensors, which allow individual control of depth-measuring rays, such as the newly emerging solid-state lidars. The method simultaneously (i) learns to reconstruct a dense 3D occupancy map from sparse depth measurements, and (ii) optimizes the reactive control of depth-measuring rays. To make the first step towards the online control optimization, we propose a fast prioritized greedy algorithm, which needs to update its cost function in only a small fraction of pos- sible rays. The approximation ratio of the greedy algorithm is derived. An experimental evaluation on the subset of the KITTI dataset demonstrates significant improve- ment in the 3D map accuracy when learning-to-reconstruct from sparse measurements is coupled with the optimization of depth-measuring rays." @default.
- W4300794540 created "2022-10-04" @default.
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- W4300794540 date "2017-08-07" @default.
- W4300794540 modified "2023-10-18" @default.
- W4300794540 title "Learning for Active 3D Mapping" @default.
- W4300794540 doi "https://doi.org/10.48550/arxiv.1708.02074" @default.
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