  We propose a non-learning depth completion method for a sparse depth map
captured using a light detection and ranging (LiDAR) sensor guided by a pair of
stereo images. Generally, conventional stereo-aided depth completion methods
have two limiations. (i) They assume the given sparse depth map is accurately
aligned to the input image, whereas the alignment is difficult to achieve in
practice. (ii) They have limited accuracy in the long range because the depth
is estimated by pixel disparity. To solve the abovementioned limitations, we
propose selective stereo matching (SSM) that searches the most appropriate
depth value for each image pixel from its neighborly projected LiDAR points
based on an energy minimization framework. This depth selection approach can
handle any type of mis-projection. Moreover, SSM has an advantage in terms of
long-range depth accuracy because it directly uses the LiDAR measurement rather
than the depth acquired from the stereo. SSM is a discrete process; thus, we
apply variational smoothing with binary anisotropic diffusion tensor (B-ADT) to
generate a continuous depth map while preserving depth discontinuity across
object boundaries. Experimentally, compared with the previous state-of-the-art
stereo-aided depth completion, the proposed method reduced the mean absolute
error (MAE) of the depth estimation to 0.65 times and demonstrated
approximately twice more accurate estimation in the long range. Moreover, under
various LiDAR-camera calibration errors, the proposed method reduced the depth
estimation MAE to 0.34-0.93 times from previous depth completion methods.
