A joint multi-task network performs monocular depth estimation and sparse depth completion with a shared two-stage architecture, reporting competitive-to-superior numbers on KITTI, but the evaluation omits a stronger baseline and may have train/test overlap.
Deep Convolutional Compressed Sensing for LiDAR Depth Completion
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abstract
In this paper we consider the problem of estimating a dense depth map from a set of sparse LiDAR points. We use techniques from compressed sensing and the recently developed Alternating Direction Neural Networks (ADNNs) to create a deep recurrent auto-encoder for this task. Our architecture internally performs an algorithm for extracting multi-level convolutional sparse codes from the input which are then used to make a prediction. Our results demonstrate that with only two layers and 1800 parameters we are able to out perform all previously published results, including deep networks with orders of magnitude more parameters.
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2019 1verdicts
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To complete or to estimate, that is the question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation
A joint multi-task network performs monocular depth estimation and sparse depth completion with a shared two-stage architecture, reporting competitive-to-superior numbers on KITTI, but the evaluation omits a stronger baseline and may have train/test overlap.