Best of Both Sides: Integration of Absolute and Relative Depth Sensing Modalities Based on iToF and RGB Cameras

LiDAR sensors have become one of the most popular active depth sensing devices nowadays with their wide applications in autonomous driving and robotics. Among various types of LiDARs, indirect time of flight (iToF) has been ubiquitously applied on smartphones and consumer-level imagining devices due to its affordable price. Based on the common camera configuration on nowadays smartphones of having an iToF sensor and multiple RGB cameras with different focal lengths (thus leading to different fields of view), in this work, we investigate the integration between two opposite but complementary sensing modalities to achieve better depth estimation: 1) The active sensing modality based on iToF provides absolute and metric depths but suffers from noises caused by environmental lighting and heat; 2) The passive sensing modality based on monocular RGB cameras produces high-resolution but relative depth estimation. Our proposed integration is built upon a weakly-supervised learning framework where the learning objective mainly stems from the inter-camera geometric consistency with the help of iToF depth estimates. Moreover, we adopt the structure distillation technique for preserving structure details from the passive sensing method. We conduct experiments on both synthetic and real-world datasets and demonstrate that the depth estimation produced by the proposed integration model has a comparable quantitative performance with respect to the supervised learning baselines. Besides, the qualitative evaluation of our model shows that it utilizes the advantages and further overcomes the limitations of both sensing modalities.