Learning Modal and Spatial Features With Lightweight 3D Convolution for RGB Guided Depth Completion

RGB guided depth completion aims to recover a complete depth map from a sparse set of depth measurements and one corresponding RGB image, which is efficient for 3D applications to generate high-quality depth maps. Most prevailing approaches feed the sparse depth data and RGB image collected by consumer devices into a 2D convolutional network performed only at the spatial level. We argue that there...