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

TL;DR

This paper introduces an adaptive depth sampling method that, when combined with RGB data, improves dense depth map estimation, especially at very low sampling rates, and is trainable end-to-end.

Contribution

It proposes a novel adaptive sampling network that jointly trains with fusion algorithms, enabling effective depth estimation with minimal measurements.

Findings

Effective at sampling rates as low as 0.0625%

Generalizes well across various RGB and depth fusion methods

Fully differentiable and trainable end-to-end

Abstract

Dense depth map capture is challenging in existing active sparse illumination based depth acquisition techniques, such as LiDAR. Various techniques have been proposed to estimate a dense depth map based on fusion of the sparse depth map measurement with the RGB image. Recent advances in hardware enable adaptive depth measurements resulting in further improvement of the dense depth map estimation. In this paper, we study the topic of estimating dense depth from depth sampling. The adaptive sparse depth sampling network is jointly trained with a fusion network of an RGB image and sparse depth, to generate optimal adaptive sampling masks. We show that such adaptive sampling masks can generalize well to many RGB and sparse depth fusion algorithms under a variety of sampling rates (as low as $0.0625\%$). The proposed adaptive sampling method is fully differentiable and flexible to be trained end-to-end with upstream perception algorithms.