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Dense depth map capture is challenging in existing active sparse illuminationbased depth acquisition techniques, such as LiDAR. Various techniques have beenproposed to estimate a dense depth map based on fusion of the sparse depth mapmeasurement with the RGB image. Recent advances in hardware enable adaptivedepth measurements resulting in further improvement of the dense depth mapestimation. In this paper, we study the topic of estimating dense depth fromdepth sampling. The adaptive sparse depth sampling network is jointly trainedwith a fusion network of an RGB image and sparse depth, to generate optimaladaptive sampling masks. We show that such adaptive sampling masks cangeneralize well to many RGB and sparse depth fusion algorithms under a varietyof sampling rates (as low as $0.0625\%$). The proposed adaptive sampling methodis fully differentiable and flexible to be trained end-to-end with upstreamperception algorithms.

Adaptive Illumination based Depth Sensing using Deep Superpixel and Soft Sampling Approximation