Deep End-to-End Alignment and Refinement for Time-of-Flight RGB-D Module
This work addresses the need for high-quality depth sensing in mobile devices equipped with ToF sensors, representing an incremental improvement over existing methods.
The paper tackles the problems of online calibration and error correction for Time-of-Flight (ToF) depth sensors paired with RGB cameras, proposing a deep learning framework that achieves state-of-the-art results for ToF refinement.
Recently, it is increasingly popular to equip mobile RGB cameras with Time-of-Flight (ToF) sensors for active depth sensing. However, for off-the-shelf ToF sensors, one must tackle two problems in order to obtain high-quality depth with respect to the RGB camera, namely 1) online calibration and alignment; and 2) complicated error correction for ToF depth sensing. In this work, we propose a framework for jointly alignment and refinement via deep learning. First, a cross-modal optical flow between the RGB image and the ToF amplitude image is estimated for alignment. The aligned depth is then refined via an improved kernel predicting network that performs kernel normalization and applies the bias prior to the dynamic convolution. To enrich our data for end-to-end training, we have also synthesized a dataset using tools from computer graphics. Experimental results demonstrate the effectiveness of our approach, achieving state-of-the-art for ToF refinement.