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A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps. (arXiv:2205.12918v1 [cs.CV])
May 26, 2022, 1:13 a.m. | Xiaowen Jiang, Valerio Cambareri, Gianluca Agresti, Cynthia Ifeyinwa Ugwu, Adriano Simonetto, Fabien Cardinaux, Pietro Zanuttigh
cs.CV updates on arXiv.org arxiv.org
Sparse active illumination enables precise time-of-flight depth sensing as it
maximizes signal-to-noise ratio for low power budgets. However, depth
completion is required to produce dense depth maps for 3D perception. We
address this task with realistic illumination and sensor resolution constraints
by simulating ToF datasets for indoor 3D perception with challenging sparsity
levels. We propose a quantized convolutional encoder-decoder network for this
task. Our model achieves optimal depth map quality by means of input
pre-processing and carefully tuned training with …
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