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- W4322704459 abstract "Autonomous mobile robots rely on knowing the 3D structure of their environment in order to plan and operate safely. This paper addresses the problem of estimating depth maps from monocular RGB images and any potentially available sparse depth measurements—as would be the case of using inexpensive RGBD or LIDAR sensors. Our approach bridges depth estimation and depth completion by introducing a novel lightweight two-stream encoder-decoder neural network that exploits late fusion and additional output refinements. The novelty of our network is that it can benefit from any available depth data, while not making any assumptions about their actual availability, density or distribution. Our proposed architecture outperforms models with equivalent number of operations in terms of depth accuracy and performs on par with much larger models." @default.
- W4322704459 created "2023-03-02" @default.
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- W4322704459 date "2023-01-01" @default.
- W4322704459 modified "2023-10-16" @default.
- W4322704459 title "Bridging Depth Estimation and Completion for Mobile Robots Reliable 3D Perception" @default.
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- W4322704459 doi "https://doi.org/10.1007/978-3-031-26889-2_16" @default.
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