Depth Estimation fusing Image and Radar Measurements with Uncertain
  Directions
Masaya Kotani Takeru Oba Norimichi Ukita
This paper proposes a depth estimation method using radar-image fusion by
addressing the uncertain vertical directions of sparse radar measurements. In
prior radar-image fusion work, image features are merged with the uncertain
sparse depths measured by radar through convolutional layers. This approach is
disturbed by the features computed with the uncertain radar depths.
Furthermore, since the features are computed with a fully convolutional
network, the uncertainty of each depth corresponding to a pixel is spread out
over its surrounding pixels. Our method avoids this problem by computing
features only with an image and conditioning the features pixelwise with the
radar depth. Furthermore, the set of possibly correct radar directions is
identified with reliable LiDAR measurements, which are available only in the
training stage. Our method improves training data by learning only these
possibly correct radar directions, while the previous method trains raw radar
measurements, including erroneous measurements. Experimental results
demonstrate that our method can improve the quantitative and qualitative
results compared with its base method using radar-image fusion.